The study examines different graph-based methods of detecting anomalous activities on digital markets, proposing the most efficient way to increase market actors’ protection and reduce information asymmetry. Anomalies are defined below as both bots and fraudulent users (who can be both bots and real people). Methods are compared against each other, and state-of-the-art results from the literature and a new algorithm is proposed. The goal is to find an efficient method suitable for threat detection, both in terms of predictive performance and computational efficiency. It should scale well and remain robust on the advancements of the newest technologies. The article utilized three publicly accessible graph-based datasets: one describing the Twitter social network (TwiBot-20) and two describing Bitcoin cryptocurrency markets (Bitcoin OTC and Bitcoin Alpha). In the former, an anomaly is defined as a bot, as opposed to a human user, whereas in the latter, an anomaly is a user who conducted a fraudulent transaction, which may (but does not have to) imply being a bot. The study proves that graph-based data is a better-performing predictor than text data. It compares different graph algorithms to extract feature sets for anomaly detection models. It states that methods based on nodes’ statistics result in better model performance than state-of-the-art graph embeddings. They also yield a significant improvement in computational efficiency. This often means reducing the time by hours or enabling modeling on significantly larger graphs (usually not feasible in the case of embeddings). On that basis, the article proposes its own graph-based statistics algorithm. Furthermore, using embeddings requires two engineering choices: the type of embedding and its dimension. The research examines whether there are types of graph embeddings and dimensions that perform significantly better than others. The solution turned out to be dataset-specific and needed to be tailored on a case-by-case basis, adding even more engineering overhead to using embeddings (building a leaderboard of grid of embedding instances, where each of them takes hours to be generated). This, again, speaks in favor of the proposed algorithm based on nodes’ statistics. The research proposes its own efficient algorithm, which makes this engineering overhead redundant.

This dataset contains complementary data to the paper "A Row Generation Algorithm for Finding Optimal Burning Sequences of Large Graphs" [1], which proposes an exact algorithm for the Graph Burning Problem, an NP-hard optimization problem that models a form of contagion diffusion on social networks. Concerning the computational experiments discussed in that paper, we make available: - Four sets of instances; - The optimal (or best known) solutions obtained; - The source code; - An Appendix with additional details about the results. The "delta" input sets include graphs that are real-world networks [1,2], while the "grid" input set contains graphs that are square grids. The directories "delta_10K_instances", "delta_100K_instances", "delta_4M_instances" and "grid_instances" contain files that describe the sets of instances. The first two lines of each file contain: {n} {m} where {n} and {m} are the number of vertices and edges in the graph. Each of the next {m} lines contains: {u} {v} where {u} and {v} identify a pair of vertices that determines an undirected edge. The directories "delta_10K_solutions", "delta_100K_solutions", "delta_4M_solutions" and "grid_solutions" contain files that describe the optimal (or best known) solutions for the corresponding sets of instances. The first line of each file contains: {s} where {s} is the number of vertices in the burning sequence. Each of the next {s} lines contains: {v} where {v} identifies a fire source. The fire sources are listed in the same order that they appear in a burning sequence of length {s}. The directory "source_code" contains the implementations of the exact algorithm proposed in the paper [1], namely, PRYM. Lastly, the file "appendix.pdf" presents additional details on the results reported in the paper. This work was supported by grants from Santander Bank, Brazil, Brazilian National Council for Scientific and Technological Development (CNPq), Brazil, São Paulo Research Foundation (FAPESP), Brazil and Fund for Support to Teaching, Research and Outreach Activities (FAEPEX). Caveat: the opinions, hypotheses and conclusions or recommendations expressed in this material are the sole responsibility of the authors and do not necessarily reflect the views of Santander, CNPq, FAPESP or FAEPEX. References [1] F. C. Pereira, P. J. de Rezende, T. Yunes and L. F. B. Morato. A Row Generation Algorithm for Finding Optimal Burning Sequences of Large Graphs. Submitted. 2024. [2] Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. 2024. https://snap.stanford.edu/data [3] Ryan A. Rossi and Nesreen K. Ahmed. The Network Data Repository with Interactive Graph Analytics and Visualization. In: AAAI, 2022. https://networkrepository.com

Node statistics for TwiBot-20, Bitcoin OTC and Bitcoin Alpha datasets.

Full results are available in S1 Appendix as Table 2d.

Dataset includes:

1. Tcl scripts to compute H-bonds between lipids, H-bonds between lipid and water molecules (1-water bridges), interactions between lipids and ions from simulations.
2. Matlab scripts to compute lipid H-bond clusters and detect types of cluster topologies using depth-first search (DFS) algorithm.
3. Tcl script to visualize in VMD lipid H-bonds or ion interactions. Lipids are color-coded based on the topology type.

Workflow is generated and tested in MATLAB R2017b and VMD 1.9.3. Guidelines for running the scripts are in README text file in the topology_analysis folder.

"When using these scripts, please cite: Karathanou, K. and Bondar, A.N., 2022. Algorithm to catalogue topologies of dynamic lipid hydrogen-bond networks. Biochimica et Biophysica Acta (BBA)-Biomembranes, p.183859."

DFS algorithm:

To cluster lipid H-bond clusters and detect types of topologies, we perform Connected Component searches based on the Depth-First Search (DFS) algorithm. The DFS algorithm starts from an initial (source) node and performs exhaustive searches of all the nodes along the current path. When all nodes are visited, it moves backwards on the same path to find unvisited nodes. When all nodes of the current path are visited, the algorithm selects the next unexplored path. The computation is completed when the entire graph is explored.

The Degree Centrality (DC) of a node ni gives the number of edges of the node.

Algorithm computes three main types of topologies linear, star and circular and combinations thereof. All paths are catalogued according to their path length. For each lipid cluster found in the membrane and for each simulation time, the length of each path is defined as the longest number of edges between a start and an end node excluding short branches from star paths, and keep for the circular paths only the edge that connects the longest path to the end node.

References:

Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C., 2009. Introduction to Algorithms (3-rd edition). MIT Press and McGraw-Hill. Freeman LC: Centrality in social networks. Conceptual clarification. Social Networks 1979, 1:215-239. V.K. Balakrishnan, Schaum's outline of theory and problems of graph theory, McGraw-Hill, 1997. J.L. Gross, J. Yellen, Graph theory and its applications, CRC Press, 1998.

The project analysed the performance of community detection algorithms on the Twitter social network operating on a graph compressed using minhash signatures. The data supplied gives minhash signatures of roughly 16,000 Twitter users who have been classified into 16 categories. It is described in https://arxiv.org/abs/1601.03958 and together with code at https://github.com/melifluos/LSH-community-detection allows the results within to be replicated. Date Accepted: 2017-11-14

Bluesky Social Dataset Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. To address this pressing issue, we present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social.

The dataset contains the complete post history of over 4M users (81% of all registered accounts), totaling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions.

Since Bluesky allows users to create and bookmark feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions and time of bookmarking.

This dataset allows unprecedented analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection, and performing content virality and diffusion analysis.

Dataset Here is a description of the dataset files.

followers.csv.gz. This compressed file contains the anonymized follower edge list. Once decompressed, each row consists of two comma-separated integers u, v, representing a directed following relation (i.e., user u follows user v). posts.tar.gz. This compressed folder contains data on the individual posts collected. Decompressing this file results in 100 files, each containing the full posts of up to 50,000 users. Each post is stored as a JSON-formatted line. interactions.csv.gz. This compressed file contains the anonymized interactions edge list. Once decompressed, each row consists of six comma-separated integers, and represents a comment, repost, or quote interaction. These integers correspond to the following fields, in this order: user_id, replied_author, thread_root_author, reposted_author ,quoted_author, and date. graphs.tar.gz. This compressed folder contains edge list files for the graphs emerging from reposts, quotes, and replies. Each interaction is timestamped. The folder also contains timestamped higher-order interactions emerging from discussion threads, each containing all users participating in a thread. feed_posts.tar.gz. This compressed folder contains posts that appear in 11 thematic feeds. Decompressing this folder results in 11 files containing posts from one feed each. Posts are stored as a JSON-formatted line. Fields are correspond to those in posts.tar.gz, except for those related to sentiment analysis (sent_label, sent_score), and reposts (repost_from, reposted_author); feed_bookmarks.csv. This file contains users who bookmarked any of the collected feeds. Each record contains three comma-separated values, namely the feed name, the user id, and the timestamp. feed_post_likes.tar.gz. This compressed folder contains data on likes to posts appearing in the feeds, one file per feed. Each record in the files contains the following information, in this order: the id of the ``liker'', the id of the post's author, the id of the liked post, and the like timestamp; scripts.tar.gz. A collection of Python scripts, including the ones originally used to crawl the data, and to perform experiments. These scripts are detailed in a document released within the folder.

Citation If used for research purposes, please cite the following paper describing the dataset details:

Andrea Failla and Giulio Rossetti. "I'm in the Bluesky Tonight": Insights from a Year Worth of Social Data. (2024) arXiv:2404.18984

Acknowledgments: This work is supported by :

the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021; EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).

This dataset contains complementary data to the paper "Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches" [1], which studies integer/constraint programming formulations for the Least Cost Directed Perfect Awareness Problem, an NP-hard optimization problem that arises in the context of influence marketing. Regarding the computational experiments conducted in the paper, we make available:

• Two sets of instances;
• The best known attained solutions;
• The source code;
• An appendix with additional details about the results.

The first input set includes 300 synthetic instances composed of graphs that resemble real-world social networks [2]. The second set consists of 14 instances built from online interactions crawled from X (formerly known as Twitter) [3].

The directories "synthetic_instances" and "x_instances" contain files that describe both sets of instances. The first two lines of each file contain:

where

where

The network visualization software market is experiencing robust growth, driven by the increasing need to analyze complex data relationships across diverse industries. The market's expansion is fueled by the rising adoption of big data analytics, the proliferation of interconnected systems, and the demand for intuitive tools to understand intricate network structures. Businesses across sectors, including finance, telecommunications, healthcare, and social sciences, are leveraging network visualization to identify patterns, predict outcomes, and optimize operations. The market's growth trajectory is further enhanced by advancements in software capabilities, such as improved algorithms for large-scale data processing and the integration of artificial intelligence for automated insights. While challenges like data security and the complexity of implementing these solutions exist, the overall market outlook remains positive, with a projected sustained Compound Annual Growth Rate (CAGR) reflecting consistent expansion in the coming years. The competition is dynamic, with established players like SolarWinds and emerging companies like TigerGraph vying for market share. The market segmentation is likely driven by software functionalities (e.g., open-source vs. proprietary), deployment models (cloud-based vs. on-premise), and specific industry applications. The forecast period of 2025-2033 suggests a significant expansion in the network visualization software market. Assuming a conservative CAGR of 15% (a reasonable estimate considering the growth drivers), and a 2025 market size of $500 million (an educated guess based on similar software markets), the market is projected to reach approximately $1.8 billion by 2033. This substantial increase underscores the growing importance of effective network visualization in making sense of ever-increasing datasets. The regional distribution will likely be skewed towards developed economies initially, with North America and Europe holding a significant market share, though emerging economies in Asia-Pacific are expected to witness accelerated growth in the latter half of the forecast period. Open-source solutions are expected to maintain a significant presence due to their cost-effectiveness, while proprietary solutions will continue to offer advanced features and robust support, catering to enterprise-level requirements.

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

Identity Resolution Market Outlook

The global identity resolution market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach a valuation of around USD 5.8 billion by 2032, growing at a CAGR of 16.2% during the forecast period from 2024 to 2032. This remarkable growth is primarily driven by the increasing need for organizations to accurately identify and understand their customers, thereby enhancing marketing efficiency and reducing fraud.

One of the significant growth factors for the identity resolution market is the exponential increase in digital interactions. With the proliferation of digital channels such as social media, e-commerce platforms, and mobile applications, organizations face the challenge of integrating diverse data points to create a unified customer profile. Identity resolution technology enables businesses to overcome this challenge by linking disparate data sources, thereby providing a holistic view of the customer. This capability is particularly crucial in enhancing targeted marketing campaigns, improving customer engagement, and boosting overall business performance.

Another critical driver is the rising incidences of fraud and cyber threats. As digital transactions surge, the risk of identity theft and fraud also escalates. Businesses are increasingly adopting identity resolution solutions to detect and prevent fraudulent activities in real-time. These solutions employ advanced algorithms and machine learning techniques to analyze data patterns and identify anomalies. By doing so, businesses can protect themselves and their customers from potential fraud, thereby safeguarding their reputation and financial stability.

The push for regulatory compliance also fuels the demand for identity resolution solutions. Various regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), mandate organizations to manage and protect personal data effectively. Identity resolution tools help organizations comply with these regulations by ensuring data accuracy and consistency across different systems and applications. This not only mitigates the risk of non-compliance penalties but also enhances customer trust and loyalty.

Regionally, North America is expected to hold the largest market share in the identity resolution market during the forecast period. This dominance is attributed to the early adoption of advanced technologies, a high concentration of market players, and stringent data privacy regulations. Moreover, the growing focus on customer experience management and security concerns are driving the adoption of identity resolution solutions in the region. The Asia Pacific region is also anticipated to witness significant growth, driven by the rapid digital transformation, increasing internet penetration, and rising awareness about data privacy and security.

Component Analysis

The identity resolution market is segmented by component into software and services. The software segment encompasses various tools and platforms designed to integrate and reconcile different data points to form a unified customer identity. This segment is expected to hold a significant share of the market due to the increasing reliance on advanced analytics and machine learning algorithms to process large volumes of data. These software solutions are pivotal in ensuring data accuracy and consistency, thereby enabling businesses to derive actionable insights from their data.

Within the software segment, various types of solutions are available, including customer data platforms (CDPs), data management platforms (DMPs), and identity graph technologies. CDPs and DMPs are particularly popular due to their ability to aggregate data from multiple sources, allowing for real-time customer identity resolution. Identity graph technologies, on the other hand, focus on mapping relationships between different data points, thereby enhancing the accuracy of customer profiles. The continuous innovation in these software solutions is expected to drive the growth of the software segment.

The services segment includes consulting, implementation, and support services offered by various vendors to help organizations deploy and maintain identity resolution solutions. Consulting services are vital in assessing an organization's current data landscape and identifying the best strategies for implementing identity resolution technologies. Implementation services ensure the seamless integration of these solutions into existing systems, while support services provide ongoing m

Research in digital fabrication, specifically in 3D concrete printing (3DCP), has seen a substantial increase in publication output in the past five years, making it hard to keep up with the latest developments. The 3dcp.fyi database aims to provide the research community with a comprehensive, up-to-date, and manually curated literature dataset documenting the development of the field from its early beginnings in the late 1990s to its resurgence in the 2010s until today. The data set is compiled using a systematic approach. A thorough literature search was conducted in scientific databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scheme. This was then enhanced iteratively with non-indexed literature through a snowball citation search. The authors of the articles were assigned unique and persistent identifiers (ORCID® IDs) through a systematic process that combined querying APIs systematically and manually curating data. The works in the data set also include references to other works, as long as those referenced works are also included within the same data set. A citation network graph is created where scientific articles are represented as vertices, and their citations to other scientific articles are the edges. The constructed network graph is subjected to detailed analysis using specific graph-theoretic algorithms, like PageRank. These algorithms evaluate the structure and connections within the graph, yielding quantitative metrics. Currently, the high-quality dataset contains more than 2600 manually curated scientific works, including journal articles, conference articles, books, and theses, with more than 40000 cross-references and 2000 authors, opening up the possibility for more detailed analysis. The data is published on https://3dcp.fyi, ready for import into several reference managers, and is continuously updated. We encourage researchers to enrich the database by submitting their publications, adding missing works, or suggesting new features.

Full results are available in the S1 Appendix as Table 2e.

This dataset contains complementary data to the paper "The Least Cost Directed Perfect Awareness Problem: Complexity, Algorithms and Computations" [1]. Here, we make available two sets of instances of the combinatorial optimization problem studied in that paper, which deals with the spread of information on social networks. We also provide the best known solutions and bounds obtained through computational experiments for each instance.

The first input set includes 300 synthetic instances composed of graphs that resemble real-world social networks. These graphs were produced with a generator proposed in [2]. The second set consists of 14 instances built from graphs obtained by crawling Twitter [3].

The directories "synthetic_instances" and "twitter_instances" contain files that describe both sets of instances, all of which follow the format: the first two lines correspond to:

where

where

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

International Journal of Engineering and Advanced Technology FAQ - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level agreements (drafting,

International Journal of Engineering and Advanced Technology Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

Dataset about stage length and water level estimation experiments, as analyzed in the manuscript "Assessing the optimal setup for continuous water level monitoring in ephemeral streams: experimental evidence"

Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer application to the humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors' status, were determining factors.