The Cornell eRulemaking Corpus – CDCP is an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary for readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.

Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-AstroPh.html

ProteinKG25 is a large-scale KG dataset with aligned descriptions and protein sequences respectively to GO terms and proteins entities. ProteinKG25 contains 4,990,097 triplets (4,879,951 Protein-GO triplets and 110,146 GO-GO triplets), 612,483 entities (565,254 proteins and 47,229 GO terms) and 31 relations.

Context There's a story behind every dataset and here's your opportunity to share yours. ### Content What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. ### Acknowledgements We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research. ### Inspiration Your data will be in front of the world's largest data science community. What questions do you want to see answered?

DBLP Temporal is a dataset for temporal entity resolution, based on author profiles extracted from the Digital Bibliography and Library Project (DBLP).

CoDEx comprises a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. Source: CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

CoDEx comprises a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. Source: CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

KG20C is a Knowledge Graph about high quality papers from 20 top computer science Conferences. It can serve as a standard benchmark dataset in scholarly data analysis for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering . For more information and download, please see the dataset homepage.

This is a benchmark set for Traveling salesman problem (TSP) with characteristics that are different from the existing benchmark sets. In particular, it focuses on small instances which prove to be challenging for one or more state-of-the-art TSP algorithms. These instances are based on difficult instances of Hamiltonian cycle problem (HCP). This includes instances from literature, specially modified randomly generated instances, and instances arising from the conversion of other difficult problems to HCP.

The ACM dataset contains papers published in KDD, SIGMOD, SIGCOMM, MobiCOMM, and VLDB and are divided into three classes (Database, Wireless Communication, Data Mining). An heterogeneous graph is constructed, which comprises 3025 papers, 5835 authors, and 56 subjects. Paper features correspond to elements of a bag-of-words represented of keywords. Source: https://arxiv.org/pdf/1903.07293.pdf

CoDEx comprises a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. Source: CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

The IS-A dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are related by the “is a” relation. For example, ‘acute leukemia’ is a ‘leukemia’. The dataset has 294,693 nodes with 356,541 edges between them. Source: https://arxiv.org/pdf/1906.05939.pdf

Arxiv GR-QC (General Relativity and Quantum Cosmology) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to General Relativity and Quantum Cosmology category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-GrQc.html

The FB15k-237-low dataset is a variation of the FB15k-237 dataset where relations with a low number of triplets are kept. Source: https://arxiv.org/pdf/1911.03091.pdf

The GDELT Project is a remarkable initiative that monitors our world by analyzing global news from various sources. Here are the key aspects of the GDELT dataset: 1. Scope and Purpose: - The GDELT Project aims to create a comprehensive, real-time database of global human society. - It monitors news from broadcasts, print media, and web sources in nearly every country and over 100 languages. - By analyzing this vast dataset, it identifies people, locations, organizations, themes, emotions, and events that shape our global society every second of every day. 2. Data Collection: - GDELT continuously captures and analyzes news articles, broadcasts, and online sources. - Its historical archives date back to January 1, 1979, and it updates every 15 minutes. - The project goes beyond Western media, providing a more global perspective on world events and sentiments. 3. Features: - GDELT uses sophisticated natural language and data mining algorithms, including powerful deep learning techniques. - It extracts over 300 categories of events, millions of themes, thousands of emotions, and the networks connecting them. - The dataset models human interactions at a large scale, making it valuable for research and analysis. 4. Vision: - The GDELT Project envisions using this data to: - Understand the world through others' eyes. - Break down language and access barriers. - Facilitate conversations between societies. - Empower local populations with information for safer lives. - Map happiness, conflict, and potentially forecast global tensions. 5. Global Reach: - GDELT monitors media in over 100 languages across every country, providing a truly global perspective. - It allows us to explore how social media is used worldwide and how people express themselves online. 6. Open Data: - The entire GDELT database is free and open. - Researchers can download raw data, visualize it, or analyze it at scale using tools like Google BigQuery¹²³⁴⁵. Source: Conversation with Bing, 3/12/2024 (1) The GDELT Project. https://www.gdeltproject.org/. (2) The GDELT Database | Aalto Datahub. https://datahub.aalto.fi/en/data-sources/the-gdelt-database. (3) An Introduction to GDELT Data | MongoDB. https://www.mongodb.com/developer/products/mongodb/introduction-to-gdelt-data/. (4) GDELT 2.0: Our Global World in Realtime – The GDELT Project. https://blog.gdeltproject.org/gdelt-2-0-our-global-world-in-realtime/. (5) Data: Querying, Analyzing and Downloading: The GDELT Project. https://www.gdeltproject.org/data.html.

DGraphFin dataset which is pre-processed in TGN Style.

The Dr. Inventor Multi-Layer Scientific Corpus (DRI Corpus) includes 40 Computer Graphics papers, selected by domain experts. Each paper of the Corpus has been annotated by three annotators by providing the following layers of annotations, each one characterizing a core aspect of scientific publications: * Scientific discourse: each sentence has been associated to a specific scientific discourse category (Background, Approach, Challenge, Future Work, etc.). * Subjective statements and novelty: each sentence has been characterized with respect to advantages, disadvantages and novel aspects presented. * Citation purpose: to each citation has been associated a purpose specifying the reason why the authors of the paper cited the specific piece of research. * Summary relevance of sentences and hand written summaries: each sentence of the paper has been characterized by an integer score ranging from 1 to 5, to point out the relevance of the same sentence for its inclusion in the summary of the paper. Sentences rated as 5 are the most relevant ones to summarize a paper. For each paper three hand-written summaries (max 250 words) are provided. Source: Dr. Inventor Multi-layer Scientific Corpus

We introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs from diverse domains, with nodes and edges enriched by dynamically changing text attributes and categories. To facilitate the use of DTGB, we design standardized evaluation procedures based on four real-world use cases: future link prediction, destination node retrieval, edge classification, and textual relation generation. These tasks require models to understand both dynamic graph structures and natural language, highlighting the unique challenges posed by DyTAGs.

The dataset contains constructed multi-modal features (visual and textual), pseudo-labels (on heritage values and attributes), and graph structures (with temporal, social, and spatial links) constructed using User-Generated Content data collected from Flickr social media platform in three global cities containing UNESCO World Heritage property (Amsterdam, Suzhou, Venice). The motivation of data collection in this project is to provide datasets that could be both directly applicable for ML communities as test-bed, and theoretically informative for heritage and urban scholars to draw conclusions on for planning decision-making.

The Kinships dataset describes relationships between members of the Australian tribe Alyawarra and consists of 10,686 triples. It contains 104 entities representing members of the tribe and 26 relationship types that represent kinship terms such as Adiadya or Umbaidya.

The Nations dataset is a small knowledge graph with 14 entities, 55 relations, and 1992 triples describing countries and their political relationships. This dataset is available for download from https://github.com/ZhenfengLei/KGDatasets.

The AbstRCT dataset consists of randomized controlled trials retrieved from the MEDLINE database via PubMed search. The trials are annotated with argument components and argumentative relations. Paper: Transformer-Based Argument Mining for Healthcare Applications

The Aristo Tuple KB contains a collection of high-precision, domain-targeted (subject,relation,object) tuples extracted from text using a high-precision extraction pipeline, and guided by domain vocabulary constraints. The dataset was introduced by the paper Domain-Targeted, High Precision Knowledge Extraction.

CVE stands for Common Vulnerabilities and Exposures. CVE is a glossary that classifies vulnerabilities. The glossary analyzes vulnerabilities and then uses the Common Vulnerability Scoring System (CVSS) to evaluate the threat level of a vulnerability. A CVE score is often used for prioritizing the security of vulnerabilities.

The FB1.5M dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations. Source: https://arxiv.org/pdf/1911.03091.pdf

FinDKG: The Global Financial Dynamic Knowledge Graph Dataset FinDKG is an open-source dataset focused on creating a temporally-resolved Financial Dynamic Knowledge Graph. Designed to bridge the gap in industry-specific knowledge graphs, particularly in the financial sector, FinDKG provides a high-touch, temporally-aware representation of global economic and market dynamics. This repository includes comprehensive details about the dataset, methodology, and schema, aiming to facilitate academic research and actionable insights in global financial markets. ## Background While general-purpose knowledge graphs are abundant, industry-specific ones are comparatively rare, especially in the financial sector. FinDKG aims to fill this void by offering a resource for researchers and professionals looking to leverage knowledge graph technology in finance. ## FinDKG Dataset The dataset's foundation lies in an extensive news corpus curated to capture both qualitative and quantitative indicators in the financial landscape. We utilized the Wayback Machine to amass a dataset comprising global financial news. ## Dataset Structure - Temporal Knowledge Graph (TKG) with daily-resolved event triplets - Event triplets are tagged with specific timestamps corresponding to their release dates - Training, validation, and test splits organized chronologically - Weekly aggregation of event triplets as the basic unit of time ### Data Format /FinDKG is the default study dataset folder including the graph dataset and the corresponding data splits. The graph dataset is organized in the following structure: * 'train.txt', 'valid.txt', and 'test.txt': The first four columns correspond to subject, relation, object, and time. The fifth column is ignored. * 'stat.txt': The first two columns correspond to the number of entities and relations, respectively. Test set is held-out for evaluating the model performance. This should match the results of the original paper regarding the Temporal Link Prediction evaluation. /FinDKG-full: The full dataset including a larger size of the event triplets. This graph dataset adopts the same format as /FinDKG while is left for future extended research. * 'time2id.txt': This time mapping table further provided the mapping from time ID to realistic date for real-world application. ## Usage The dataset is designed for graph-based AI methods aiming to generate actionable insights in the financial domain. It is freely available for academic and research purposes. Refer details to our designated FinDKG website. (Description from Dataset Repo)

GO21 is a biomedical knowledge graph that models genes, proteins, drugs, and the hierarchy of the biological processes they participate in. It consists of 806,136 triples with 21 relations and 89127 entities. GO21 can be used for knowledge graph completion tasks (link prediction) as well as hierarchical reasoning tasks, such as ancestor-descendant prediction task proposed in the paper.

HALvest-Geometric is a subset of HALvest: an academic citation network with 238,397 disambiguated authors and 18,662,037 scholarly papers. HALvest-Geometric is made up of a heterogeneous graph. It has 238,397 author nodes, 18,662,037 paper nodes, 96,105 institution nodes, and 16 domain nodes for a total of 18,996,55 nodes, and 23,761,191 edges as of February 2024.

Hypertention Disease Medication dataset.

A large dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 46K entities, 130 relations, 202K triples. Inference graph contains 30K entities, 130 relations, 77K triples. Validation and test triples to predict belong to the inference graph.

A small dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 10K entities, 96 relations, 78K triples. Inference graph contains 7K entities, 96 relations, 21K triples. Validation and test triples to predict belong to the inference graph.

The MAPLE benchmark constructed by us contains 20 datasets across 19 fields for scientific literature tagging. It also has a graph format, which can be used for graph mining tasks (e.g., node classification, link prediction). Refer to its homepage for more details.

ML25m dataset which is pre-processed in TGN Style.

This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications. ## Data Creation The dataset was initially generated by the Myket data team, and later cleaned and subsampled by Erfan Loghmani a master student at Sharif University of Technology at the time. The data team focused on a two-week period and randomly sampled 1/3 of the users with interactions during that period. They then selected install and update interactions for three months before and after the two-week period, resulting in interactions spanning about 6 months and two weeks. We further subsampled and cleaned the data to focus on application download interactions. We identified the top 8000 most installed applications and selected interactions related to them. We retained users with more than 32 interactions, resulting in 280,391 users. From this group, we randomly selected 10,000 users, and the data was filtered to include only interactions for these users. The detailed procedure can be found in here. ## Data Structure The dataset has two main files. - myket.csv: This file contains the interaction information and follows the same format as the datasets used in the "JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks" (ACM SIGKDD 2019) project. However, this data does not contain state labels and interaction features, resulting in associated columns being all zero. - app_info_sample.csv: This file comprises features associated with applications present in the sample. For each individual application, information such as the approximate number of installs, average rating, count of ratings, and category are included. These features provide insights into the applications present in the dataset. ## Dataset Details - Total Instances: 694,121 install interaction instances - Instances Format: Triplets of user_id, app_name, timestamp - 10,000 users and 7,988 android applications - Item features for 7,606 applications For a detailed summary of the data's statistics, including information on users, applications, and interactions, please refer to the Python notebook available at summary-stats.ipynb. The notebook provides an overview of the dataset's characteristics and can be helpful for understanding the data's structure before using it for research or analysis. ### Top 20 Most Installed Applications | Package Name | Count of Interactions | | ---------------------------------- | --------------------- | | com.instagram.android | 15292 | | ir.resaneh1.iptv | 12143 | | com.tencent.ig | 7919 | | com.ForgeGames.SpecialForcesGroup2 | 7797 | | ir.nomogame.ClutchGame | 6193 | | com.dts.freefireth | 6041 | | com.whatsapp | 5876 | | com.supercell.clashofclans | 5817 | | com.mojang.minecraftpe | 5649 | | com.lenovo.anyshare.gps | 5076 | | ir.medu.shad | 4673 | | com.firsttouchgames.dls3 | 4641 | | com.activision.callofduty.shooter | 4357 | | com.tencent.iglite | 4126 | | com.aparat | 3598 | | com.kiloo.subwaysurf | 3135 | | com.supercell.clashroyale | 2793 | | co.palang.QuizOfKings | 2589 | | com.nazdika.app | 2436 | | com.digikala | 2413 | ## Comparison with SNAP Datasets The Myket dataset introduced in this repository exhibits distinct characteristics compared to the real-world datasets used by the project. The table below provides a comparative overview of the key dataset characteristics: | Dataset | #Users | #Items | #Interactions | Average Interactions per User | Average Unique Items per User | | --------------- | ---------------- | --------------- | ------------- | ----------------------------- | ----------------------------- | | Myket | 10,000 | 7,988 | 694,121 | 69.4 | 54.6 | | LastFM | 980 | 1,000 | 1,293,103 | 1,319.5 | 158.2 | | Reddit | 10,000 | 984 | 672,447 | 67.2 | 7.9 | | Wikipedia | 8,227 | 1,000 | 157,474 | 19.1 | 2.2 | | MOOC | 7,047 | 97 | 411,749 | 58.4 | 25.3 | The Myket dataset stands out by having an ample number of both users and items, highlighting its relevance for real-world, large-scale applications. Unlike LastFM, Reddit, and Wikipedia datasets, where users exhibit repetitive item interactions, the Myket dataset contains a comparatively lower amount of repetitive interactions. This unique characteristic reflects the diverse nature of user behaviors in the Android application market environment. ## Citation If you use this dataset in your research, please cite the following preprint: @misc{loghmani2023effect, title={Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks}, author={Erfan Loghmani and MohammadAmin Fazli}, year={2023}, eprint={2308.06862}, archivePrefix={arXiv}, primaryClass={cs.LG} }

The PART-OF dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them. Source: https://arxiv.org/pdf/1906.05939.pdf