This dataset contains a range of directed signed networks (signed digraphs) from social domain. The data come from 9 different sources and in total there are 29 network files. There are two temporal networks and one multilayer network in this dataset. Each network is provided in two formats: edgelist (.csv) and .gml format.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (https://doi.org/10.6084/m9.figshare.12152628) and the reference article listed below (https://doi.org/10.1038/s41598-020-71838-6), and license the new creations under the identical terms.For more information about the data, one may refer to the article below:Samin Aref, Ly Dinh, Rezvaneh Rezapour, and Jana Diesner. "Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory" Scientific Reports (2020) https://doi.org/10.1038/s41598-020-71838-6

Graph Analytics Market size was valued at USD 77.1 Million in 2023 and is projected to reach USD 637.1 Million by 2030, growing at a CAGR of 35.1% during the forecast period 2024-2030.

Global Graph Analytics Market Drivers
The market drivers for the Graph Analytics Market can be influenced by various factors. These may include:

Growing Need for Data Analysis: In order to extract insightful information from the massive amounts of data generated by social media, IoT devices, and corporate transactions, there is a growing need for sophisticated analytics tools like graph analytics.

Growing Uptake of Big Data Tools: Graph analytics solutions are becoming more and more popular due to the spread of big data platforms and technology. Businesses are using these technologies to improve the efficiency of their analysis of intricately linked datasets.

Developments in AI and ML: The capabilities of graph analytics solutions are being improved by advances in machine learning and artificial intelligence. These technologies make it possible for recommendation systems, anomaly detection, and forecasts based on graph data to be more accurate.

Increasing Recognition of the Advantages of Graph Databases: Businesses are realizing the advantages of graph databases for handling and evaluating highly related data. Consequently, there’s been a sharp increase in the use of graph analytics tools to leverage the potential of graph databases for diverse applications.

The use of advanced analytics solutions, such as graph analytics, for fraud detection, cybersecurity, and risk management is becoming more and more important as a result of the increase in cyberthreats and fraudulent activity.

Demand for Personalized suggestions: Companies in a variety of sectors are using graph analytics to provide their clients with suggestions that are tailored specifically to them. Personalized recommendations increase consumer engagement and loyalty on social networking, e-commerce, and entertainment platforms.

Analysis of Networks and Social Media is Necessary: In order to comprehend relationships, influence patterns, and community structures, networks and social media data must be analyzed using graph analytics. The capacity to do this is very helpful for security agencies, sociologists, and marketers.

Government programs and Regulations: The need for graph analytics solutions is being driven by regulations pertaining to data security and privacy as well as government programs aimed at encouraging the adoption of data analytics. These tools are being purchased by organizations in order to guarantee compliance and reduce risks.

Emergence of Industry-specific Use Cases: Graph analytics is finding applications in a number of areas, such as healthcare, finance, retail, and transportation. These use cases include supply chain management, customer attrition prediction, and financial fraud detection in addition to patient care optimization.

Technological Developments in Graph Analytics Tools: As graph analytics tools, algorithms, and platforms continue to evolve, their capabilities and performance are being enhanced. Adoption is being fueled by this technological advancement across a variety of industries and use cases.

The global graph database market is poised for substantial growth, with a market size of USD XX million in 2025 and projected to reach USD XXX million by 2033, exhibiting a CAGR of XXX% during the forecast period. This expansion is primarily driven by the increasing adoption of graph databases in various end-use industries including BFSI, retail & e-commerce, healthcare, and social media. The need for efficient data management and analysis of complex interconnections is driving the demand for graph databases, as they offer superior capabilities in handling highly connected data compared to traditional relational databases. Key trends influencing the market include the increasing adoption of artificial intelligence (AI) and machine learning (ML) techniques, which require advanced data management solutions to handle large and complex datasets. Additionally, the growing popularity of cloud computing and the availability of graph database solutions as cloud-based services is also contributing to the market's growth. Moreover, increasing investments in research and development by key market players are leading to advancements in graph database technologies, further driving the market expansion.

This repository contains network graphs and network metadata from Moviegalaxies, a website providing network graph data from about 773 films (1915–2012). The data includes individual network graph data in Graph Exchange XML Format and descriptive statistics on measures such as clustering coefficient, degree, density, diameter, modularity, average path length, the total number of edges, and the total number of nodes.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

A shift in scientific publishing from paper-based to knowledge-based practices promotes reproducibility, machine actionability and knowledge discovery. This is important for disciplines like social science, as study indicators are often social constructs such as race or education; hypothesis tests are challenging to compare in demographic research due to their limited temporal and spatial coverage; and natural language in research papers is often imprecise and ambiguous. Therefore, we present the MIRA-KG, consisting of: (1) an ontology for capturing social demography research, which links hypotheses and findings to evidence, (2) annotations of papers on health inequality in terms of the ontology, gathered by (i) prompting a Large Language Model to annotate paper abstracts using the ontology, (ii) mapping concepts to terms from NCBO BioPortal ontologies and GeoNames, and (iii) refining the final graph by a set of SHACL constraints, developed according to data quality criteria. The utility of the resource lies in its use for formally representing social demography research hypotheses, discovering research biases, discovery of knowledge, and the derivation of novel questions.This dataset was generated using the code available on Github at https://w3id.org/mira/ at version v1.0. It uses the following ontology: https://w3id.org/mira/ontology/.

The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.

We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.

In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization.

Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information:

1. A description of the use case
2. Affiliation of the researchers involved
3. How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

The Enhanced Microsoft Academic Knowledge Graph (EMAKG) is a large dataset of scientific publications and related entities, including authors, institutions, journals, conferences, and fields of study. The proposed dataset originates from the Microsoft Academic Knowledge Graph (MAKG), one of the most extensive freely available knowledge graphs of scholarly data. To build the dataset, we first assessed the limitations of the current MAKG. Then, based on these, several methods were designed to enhance data and facilitate the number of use case scenarios, particularly in mobility and network analysis. EMAKG provides two main advantages: It has improved usability, facilitating access to non-expert users It includes an increased number of types of information obtained by integrating various datasets and sources, which help expand the application domains. For instance, geographical information could help mobility and migration research. The knowledge graph completeness is improved by retrieving and merging information on publications and other entities no longer available in the latest version of MAKG. Furthermore, geographical and collaboration networks details are employed to provide data on authors as well as their annual locations and career nationalities, together with worldwide yearly stocks and flows. Among others, the dataset also includes: fields of study (and publications) labelled by their discipline(s); abstracts and linguistic features, i.e., standard language codes, tokens , and types entities’ general information, e.g., date of foundation and type of institutions; and academia related metrics, i.e., h-index. The resulting dataset maintains all the characteristics of the parent datasets and includes a set of additional subsets and data that can be used for new case studies relating to network analysis, knowledge exchange, linguistics, computational linguistics, and mobility and human migration, among others.

Austria - Individuals using the internet for participating in social networks was 64.74% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Austria - Individuals using the internet for participating in social networks - last updated from the EUROSTAT on March of 2025. Historically, Austria - Individuals using the internet for participating in social networks reached a record high of 64.74% in December of 2024 and a record low of 35.28% in December of 2011.

A study conducted in the United States in 2022 found that 31 percent of social media users thought LinkedIn protected their privacy and data. Although LinkedIn gained the highest level of trust out of the selected online networks, trust around privacy and data given to the site decreased from 50 percent in 2020. In general, only 18 percent of social media users trusted Facebook with their data, making the social media giant the least trusted platform. Overall, levels in trust around data and privacy protection have decreased over the past few years across all major social networks.

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

Excel spreadsheet containing all the data files necessary for the analysis

Croatia - Individuals using the internet for participating in social networks was 61.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Croatia - Individuals using the internet for participating in social networks - last updated from the EUROSTAT on March of 2025. Historically, Croatia - Individuals using the internet for participating in social networks reached a record high of 62.76% in December of 2023 and a record low of 32.02% in December of 2011.

How many people use social media? Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

What are the most popular social media platforms? Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Despite a tumultuous 2018, Facebook is still the top social network in the United States, with 169.76 million mobile users accessing the Facebook app in September 2019. The company’s other properties Instagram and Facebook Messenger ranked second and third with 121 and 106 million users respectively. Cambridge Analytica scandal 2018 The biggest social media company in the world had a difficult 2018. The platform has long been accused of enabling the spread of fake news during the 2016 election and beyond. One of the first and biggest negative Facebook stories of 2018 was the Cambridge Analytica scandal, which unfolded from March 2018. The Guardian, The Observer, and The New York Times simultaneously reported on political data firm Cambridge Analytica harvesting data of millions of Facebook users worldwide without their knowledge before the 2016 election, which led to Facebook CEO Mark Zuckerberg having to testify before Congress in April 2018. Facebook usage behavior changes Due to the revelations about Facebook and the company’s treatment of private user data, many U.S. users are planning to use the social network much less and to be much more careful about what they post . During an April 2018 survey, only 20 percent of respondents stated that their Facebook usage has not changed, and they were planning on continuing to use the social platform as much as they always had in the past.

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 data set has been downloaded via the OAI-PMH endpoint of the Berlin State Library/Staatsbibliothek zu Berlin’s Digitized Collections (https://digital.staatsbibliothek-berlin.de/oai) on March 1^st 2019 and converted into common tabular formats on the basis of the provided Dublin Core metadata. It contains 146,000 records.

In addition to the bibliographic metadata, representative images of the works have been downloaded, resized to a 512 pixel maximum thumbnail image and saved in JPEG format. The image data is split into title pages and first pages. Title pages have been derived from structural metadata created by scan operators and librarians. If this information was not available, first pages of the media have been downloaded. In case of multi-volume media, title pages are not available.

In total, 141,206 images title/first pages are available.

Furthermore, the tabular data has been cleaned and extended with geo-spatial coordinates provided by the OpenStreetMap project (https://www.openstreetmap.org). The actual data processing steps are summarized in the next section. For the sake of transparency and reproducibility, the original data taken from the OAI-PMH endpoint is still present in the table.

To conclude with, various graphs in GML file format are available that can be loaded directly into graph analysis tools such as Gephi (https://gephi.org/).

The implementation of the data processing steps (incl. graph creation) are available as a Jupyter notebook provided at https://github.com/elektrobohemian/SBBrowse2018/blob/master/DataProcessing.ipynb.

Tabular Metadata

The metadata is available in Excel (cleanedData.xlsx) and CSV (cleanedData.csv) file formats with equal content.

The table contains the following columns. Italique columns have not been processed.

· title The title of the medium

· creator Its creator (family name, first name)

· subject A collection’s name as provided by the library

· type The type of medium

· format A MIME type for full metadata download

· identifier An additional identifier (most often the PPN)

· language A 3-letter language code of the medium

· date The date of creation/publication or a time span

· relation A relation to a project or collection a medium has been digitized for.

· coverage The location of publication or origin (ranging from cities to continents)

· publisher The publisher of the medium.

· rights Copyright information.

· PPN The unique identifier that can be used to find more information about the current medium in all information systems of Berlin State Library/Staatsbibliothek zu Berlin.

· spatialClean In case of multiple entries in coverage, only the first place of origin has been extracted. Additionally, characters such as question marks, brackets, or the like have been removed. The entries have been normalized regarding whitespaces and writing variants with the help of regular expressions.

· dateClean As the original date may contain various format variants to indicate unclear creation dates (e.g., time spans or question marks), this field contains a mapping to a certain point in time.

· spatialCluster The cluster ID determined with the help of the Jaro-Winkler distance on the spatialClean string. This step is needed because the spatialClean fields still contain a huge amount of orthographic variants and latinizations of geographic names.

· spatialClusterName A verbal cluster name (controlled manually).

· latitude The latitude provided by OpenStreetMap of the spatialClusterName if the location could be found.

· longitude The longitude provided by OpenStreetMap of the spatialClusterName if the location could be found.

· century A century derived from the date.

· textCluster A text cluster ID on the basis of a k-means clustering relying on the title field with a vocabulary size of 125,000 using the tf*idf model and k=5,000.

· creatorCluster A text cluster ID based on the creator field with k=20,000.

· titleImage The path to the first/title page relative to the img/ subdirectory or None in case of a multi-volume work.

Other Data

graphs.zip

Various pre-computed graphs.

img.zip

First and title pages in JPEG format.

json.zip

JSON files for each record in the following format:

ppn "PPN57346250X"

dateClean "1625"

title "M. Georgii Gutkii, Gymnasii Berlinensis Rectoris Habitus Primorum Principiorum, Seu Intelligentia; Annexae Sunt Appendicis loco Disputationes super eodem habitu tum in Academia Wittebergensi, tum in Gymnasio Berlinensi ventilatae"

creator "Gutke, Georg"

spatialClusterName "Berlin"

spatialClean "Berolini"

spatialRaw "Berolini"

mediatype "monograph"

subject "Historische Drucke"

publisher "Kallius"

lat "52.5170365"

lng "13.3888599"

textCluster "45"

creatorCluster "5040"

titleImage "titlepages/PPN57346250X.jpg"

United States - Government social benefits: to persons: State and local: Employment and training was 1.35900 Bil. of $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Government social benefits: to persons: State and local: Employment and training reached a record high of 1.90800 in January of 1978 and a record low of 0.15300 in January of 1974. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Government social benefits: to persons: State and local: Employment and training - last updated from the United States Federal Reserve on March of 2025.

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