Omkar2118/datasets huggingface dataset:
Foursquare

The **Foursquare** dataset consists of check-in data for different cities. One subset contains check-ins in NYC and Tokyo collected for about 10 month (from 12 April 2012 to 16 February 2013). It contains 227,428 check-ins in New York city and 573,703 check-ins in Tokyo. Each check-in is associated with its time stamp, its GPS coordinates and its semantic meaning (represented by fine-grained venue-categories).
Another subset contains long-term (about 18 months from April 2012 to September 2013) global-scale check-in data collected from Foursquare. It contains 33,278,683 checkins by 266,909 users on 3,680,126 venues (in 415 cities in 77 countries). Those 415 cities are the most checked 415 cities by Foursquare users in the world, each of which contains at least 10K check-ins.

Source: [https://sites.google.com/site/yangdingqi/home/foursquare-dataset](https://sites.google.com/site/yangdingqi/home/foursquare-dataset)

Introducing paper: Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks
PeerRead

PearRead is a dataset of scientific peer reviews. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.

Source: [https://github.com/allenai/PeerRead](https://github.com/allenai/PeerRead)

Introducing paper: A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Kinship

This relational database consists of 24 unique names in two families (they have equivalent structures).

Source: [https://archive.ics.uci.edu/ml/datasets/kinship](https://archive.ics.uci.edu/ml/datasets/kinship)

Introducing paper: Learning distributed representations of concepts
Mindboggle

**Mindboggle** is a large publicly available dataset of manually labeled brain MRI. It consists of 101 subjects collected from different sites, with cortical meshes varying from 102K to 185K vertices. Each brain surface contains 25 or 31 manually labeled parcels.

Source: [Graph Convolutions on Spectral Embeddings: Learning of Cortical Surface Data](https://arxiv.org/abs/1803.10336)
Image Source: [https://mindboggle.info/data.html](https://mindboggle.info/data.html)

Introducing paper: Mindboggling morphometry of human brains
Linux
Full name: Linux Program Dependence Graphs

The LINUX dataset consists of 48,747 Program Dependence Graphs (PDG) generated from the **Linux** kernel. Each graph represents a function, where a node represents one statement and an edge represents the dependency between the two statements

Source: [Convolutional Set Matching for Graph Similarity](https://arxiv.org/abs/1810.10866)

Introducing paper: An Efficient Graph Indexing Method
Email-EU

EmailEU is a directed temporal network constructed from email exchanges in a large European research institution for a 803-day period. It contains 986 email addresses as nodes and 332,334 emails as edges with timestamps. There are 42 ground truth departments in the dataset.

Source: [gl2vec: Learning Feature Representation Using Graphlets for Directed Networks](https://arxiv.org/abs/1812.05473)

Introducing paper: Local Higher-Order Graph Clustering
arXiv Astro-Ph

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](https://snap.stanford.edu/data/ca-AstroPh.html)

Introducing paper: SNAP Datasets: Stanford large network dataset collection
BeerAdvocate

BeerAdvocate is a dataset that consists of beer reviews from beeradvocate. The data span a period of more than 10 years, including all ~1.5 million reviews up to November 2011. Each review includes ratings in terms of five "aspects": appearance, aroma, palate, taste, and overall impression. Reviews include product and user information, followed by each of these five ratings, and a plaintext review.

Source: [https://snap.stanford.edu/data/web-BeerAdvocate.html](https://snap.stanford.edu/data/web-BeerAdvocate.html)

Introducing paper: From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Epinion

The **Epinion**s dataset is trust network dataset. For each user, it contains his profile, his ratings and his trust relations. For each rating, it has the product name and its category, the rating score, the time point when the rating is created, and the helpfulness of this rating.

Source: [https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm](https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm)

Introducing paper: eTrust: understanding trust evolution in an online world
Arxiv GR-QC
Full name: General Relativity and Quantum Cosmology collaboration network

**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](https://snap.stanford.edu/data/ca-GrQc.html)

Introducing paper: Graph evolution: Densification and shrinking diameters
Amazon Fine Foods

Amazon Fine Foods is a dataset that consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plaintext review.

Source: [https://snap.stanford.edu/data/web-FineFoods.html](https://snap.stanford.edu/data/web-FineFoods.html)

Introducing paper: From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews