Warning: the ground truth is missing in certain of these datasets. This was fixed in version 1.0.1, which you should use instead.

Description: this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs:

• SpaceOrigin Conversations [1]: set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages.
• Correlation Clustering Instances [2]: set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].
• European Parliament Roll-Calls [3]: vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3].

These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted.

Software: the software used to train the representation learning methods and classifiers is publicly available online: SWGE.

References:

1. Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. IEEE Transactions on Computational Social Systems, 2019, 6:38-55. DOI: 10.1109/TCSS.2018.2887240 ⟨hal-01999546⟩
2. Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. Journal of Complex Networks, 2020, 8(6):cnaa025. DOI: 10.1093/comnet/cnaa025 ⟨hal-02994011⟩
3. Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. Social Networks, 2020, 60:83-102. DOI: 10.1016/j.socnet.2019.02.001 ⟨hal-02082574⟩
4. Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. IEEE Access, 2024, 12:151303-151316. DOI: 10.1109/ACCESS.2024.3472474 ⟨hal-04712854⟩

Funding: part of this work was funded by a grant from the Provence-Alpes-Côte-d'Azur region (PACA, France) and the Nectar de Code company.

Citation: If you use this data or the associated source code, please cite article [4]:

@Article{Cecillon2024,
author = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat},
title = {Whole-Graph Representation Learning For the Classification of Signed Networks},
journal = {IEEE Access},
year = {2024},
volume = {12},
pages = {151303-151316},
doi = {10.1109/ACCESS.2024.3472474},
}

This repository is part of the Ph.D. thesis of Isabelle M. van Schilt, Delft University of Technology.

This repository is used to generate a graph of open-source sea and airport data. For this, open-source data of the shipping schedules given by MSC, Maersk, HMM, and Evergreen is used. The data is collected from the websites of the shipping companies (see also https://github.com/EwoutH/shipping-data). The data is then processed to generate a graph of the shipping schedules, including the distributions of the shipping schedules. The graph is used to analyze the shipping schedules and to identify the most important ports in the network. Airport data is collected from the open-source OpenFlights database.

As case study, we collect data on CN-HK to main ports in the USA, and mostly MSC data on South America to NL-BE.

This repository is used for developing various graphs on open-source data and automatically running it as a simulation model in the repository: complex_stylized_supply_chain_model_generator

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Numerous types of real-world data can be naturally represented as graphs, such as social networks, trading networks, and biological molecules. This highlights the need for effective graph representations to support various tasks. In recent years, graph neural networks (GNNs) have demonstrated remarkable success in extracting information from graphs and enabling graph-related tasks. However, they still face a series of challenges in solving real-world problems, including scarcity of labeled data, scalability issues, potential bias, etc. These challenges stem from both domain-specific issues and inherent limitations of GNNs. This thesis introduces various strategies to tackle these challenges and empower GNNs on real-world tasks.

For the domain-specific challenges, in this thesis, we especially focus on challenges in the chemistry domain, which plays a pivotal role in the drug discovery process. Considering the significant resources needed for labeling through wet lab experiments, the AI for chemistry domain struggles with the scarcity of labeled datasets. To address this, we present a comprehensive set of strategies that span model-based and data-based strategies alongside a hybrid method. These methods ingeniously utilize the diversity of data, models, and molecular representations to compensate for the lack of labels in individual datasets. For the inherent challenges, this thesis introduces strategies to overcome two main challenges: scalability and degree-based issues, especially in the context of link prediction tasks. Both of these two challenges originate from the mechanism of GNNs, which involves the iterative aggregation of neighboring nodes' information to update each central node. For the scalability issue, our work not only preserves GNNs' prediction performance but also significantly boosts inference speed. Regarding degree bias, our work highly improves the effectiveness of GNNs for underrepresented nodes with very light additional computational costs. These contributions not only address critical gaps in applying GNNs to specific domains but also lay the groundwork for future exploration in the broader field of graph-based real-world tasks.

Attributed graphs are constructed from a public dataset \footnote{http://www.robots.ox.ac.uk/~vgg/research/affine/}, which contains eight sets of pictures, covers five common picture transformations: viewpoint changes, scale changes, image blur, JPEG compression, and illumination.

Nodes and corresponding features are extract by SIFT.

@misc{shen2024csgo,
title={CSGO: Constrained-Softassign Gradient Optimization For Large Graph Matching},
author={Binrui Shen and Qiang Niu and Shengxin Zhu},
year={2024},
eprint={2208.08233},
archivePrefix={arXiv},
primaryClass={math.CO},
url={https://arxiv.org/abs/2208.08233},
}

Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data was reported at 0.014 USD mn in Jan 2025. This records an increase from the previous number of 0.014 USD mn for Dec 2024. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data is updated monthly, averaging 0.012 USD mn from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 0.028 USD mn in Aug 2023 and a record low of 0.005 USD mn in Mar 2023. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH147: Foreign Trade: by HS 8 Digits: Import: HS49: Printed Books, Newspapers, Pictures, and Other Products of Printing Industry, Manuscripts, Typescripts, and Plans.

Global Graph Database market size is expected to reach $9.4 billion by 2029 at 23.8%, ai adoption fuels graph database market growth

Index Time Series for Vanguard FTSE All-World UCITS ETF USD Accumulation. The frequency of the observation is daily. Moving average series are also typically included. NA

The average for 2023 based on 188 countries was 0.53 percent. The highest value was in the USA: 26.3 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

The average for 2019 based on 26 countries was 23.59 mammographs per million people. The highest value was in Greece: 66.78 mammographs per million people and the lowest value was in Poland: 10.11 mammographs per million people. The indicator is available from 1980 to 2021. Below is a chart for all countries where data are available.

Enrolment in tertiary education, all programmes, female (number) in World was reported at 118047202 Persons in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Enrolment in tertiary education, all programmes, female - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

India's S&P All-World Equity Index Volatility is 15.77% which is the 17th highest in the world ranking. Transition graphs on S&P All-World Equity Index Volatility in India and comparison bar charts (USA vs. China vs. Japan vs. India), (China vs. United States of America vs. India) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

The first column gives the resolutions in each hemisphere; the numbers of nodes in the whole graph are 83, 129, 234, 463 and 1015. The second column describes the graph parameter computed: its syntactics is as follows: each parameter-name contains two separating “_” symbols that define three parts of the parameter-name. The first part describe the hemisphere or the whole connectome with the words Left, Right or All. The second part describes the parameter computed, and the third part the weight function used (their definitions are given in section “Materials and methods”). The third column contains the p-values of the first round, the second column the p-values of the second round, and the third column the (very strict) Holm-Bonferroni correction of the p-value. With p = 0.05 all the first 12 rows describe significantly different graph theoretical properties between sexes. One-by-one, each row with italic third column describe significant differences between sexes, with p = 0.05. For the details we refer to the section “Statistical analysis”.

Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm’s performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm’s effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős–Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.

Graph and download economic data for Value of Exports to All Countries from Connecticut (CTWLDA052SCEN) from 1992 to 2017 about CT, exports, and World.

The average for 2023 based on 193 countries was -0.07 points. The highest value was in Liechtenstein: 1.61 points and the lowest value was in Syria: -2.75 points. The indicator is available from 1996 to 2023. Below is a chart for all countries where data are available.

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.

Graph and download economic data for All Sectors; Property Income Received from Rest of World (Net) (IMA), Transactions (BOGZ1FA896150175A) from 1946 to 2024 about receivables, IMA, transactions, sector, World, Net, and income.

The Global Graph Database Market, valued at USD 3.12 billion in 2024, is projected to grow at a 23.56% CAGR from 2025-30, driven by AI tools and low-latency query processing.

The average for 2023 based on 45 countries was 0.57 percent. The highest value was in Germany: 4.29 percent and the lowest value was in Andorra: 0 percent. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.