Graph Clustering

The PubMed dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. Source: https://linqs.soe.ucsc.edu/data

The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. Source: https://relational.fit.cvut.cz/dataset/CORA Image Source: https://arxiv.org/abs/1611.08402

The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. Source: https://linqs.soe.ucsc.edu/data

SNAP is a collection of large network datasets. It includes graphs representing social networks, citation networks, web graphs, online communities, online reviews and more. Social networks : online social networks, edges represent interactions between people Networks with ground-truth communities : ground-truth network communities in social and information networks Communication networks : email communication networks with edges representing communication Citation networks : nodes represent papers, edges represent citations Collaboration networks : nodes represent scientists, edges represent collaborations (co-authoring a paper) Web graphs : nodes represent webpages and edges are hyperlinks Amazon networks : nodes represent products and edges link commonly co-purchased products Internet networks : nodes represent computers and edges communication Road networks : nodes represent intersections and edges roads connecting the intersections Autonomous systems : graphs of the internet Signed networks : networks with positive and negative edges (friend/foe, trust/distrust) Location-based online social networks : social networks with geographic check-ins Wikipedia networks, articles, and metadata : talk, editing, voting, and article data from Wikipedia Temporal networks : networks where edges have timestamps Twitter and Memetracker : memetracker phrases, links and 467 million Tweets Online communities : data from online communities such as Reddit and Flickr Online reviews : data from online review systems such as BeerAdvocate and Amazon User actions : actions of users on social platforms. Face-to-face communication networks : networks of face-to-face (non-online) interactions Graph classification datasets : disjoint graphs from different classes Image Source: https://snap.stanford.edu/data/

Orkut is a social network dataset consisting of friendship social network and ground-truth communities from Orkut.com on-line social network where users form friendship each other. Each connected component in a group is regarded as a separate ground-truth community. The ground-truth communities which have less than 3 nodes are removed. The dataset also provides the top 5,000 communities with highest quality and the largest connected component of the network. Source: https://snap.stanford.edu/data/com-Orkut.html

The original ionosphere dataset from UCI machine learning repository is a binary classification dataset with dimensionality 34. There is one attribute having values all zeros, which is discarded. So the total number of dimensions are 33. The ‘bad’ class is considered as outliers class and the ‘good’ class as inliers.

Friendster is an on-line gaming network. Before re-launching as a game website, Friendster was a social networking site where users can form friendship edge each other. Friendster social network also allows users form a group which other members can then join. The Friendster dataset consist of ground-truth communities (based on user-defined groups) and the social network from induced subgraph of the nodes that either belong to at least one community or are connected to other nodes that belong to at least one community. Source: https://snap.stanford.edu/data/com-Friendster.html

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

pathbased is a 3-cluster data set. The data set consists of a circular cluster with an opening near the bottom and two Gaussian distributed clusters inside. Each cluster contains 100 data points. Source: Robust path-based spectral clustering

This is a catalogue and repository of network datasets with the aim of aiding scientific research. This website is meant to be browsed both by humans and machines alike, and can also be accessed via a convenient JSON API, or via the graph-tool library. The network datasets themselves are available in several machine-readable formats, in particular gt, GraphML, GML and CSV. The upstream origin of each dataset is meant to be as transparent as possible. Each dataset contains its own publicly available extraction and parsing script, accessible via a git repository, which also includes the entire code for this website, released as Free Software under the AGPLv3. Users are encouraged to inspect the entire pipeline from original upstream data publication, downloading, parsing and format conversion. Users are also welcome to report problems or omissions with the datasets, as well as suggest new ones, either by opening an issue, or simply by forking the git repository and proposing a merge request.

97 synthetic datasets consists of 97 datasets (as illustrated in the figure) and can be used to test graph-based clustering algorithms. https://github.com/deric/clustering-benchmark

Placenta is a benchmark dataset for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure). Source: A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs Image Source: https://arxiv.org/pdf/2211.06292v1.pdf

SLNET is collection of third party Simulink models. It is curated via mining open source repository (GitHub and Matlab Central) using SLNET-Miner (https://github.com/50417/SLNet_Miner).

The dataset comprises multiple independent events, where each event contains simulated measurements (essentially 3D points) of particles generated in a collision between proton bunches at the Large Hadron Collider at CERN. The goal of the tracking machine learning challenge is to group the recorded measurements or hit for each event into tracks, sets of hits that belong to the same initial particle. A solution must uniquely associate each hit to one track. The training dataset contains the recorded hit, their ground truth counterpart and their association to particles, and the initial parameters of those particles. The test dataset contains only the recorded hits. The dataset was used for the Throughput Phase of the Tracking Machine Learning challenge on Codalab. See more details in the home page url.

Source: Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing

Source: Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells

Source: Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos

The **Perfume Co-Preference Network ** dataset comprises comprehensive user reviews and ratings collected from the Persian retail platform Atrafshan. This dataset, central to our research on community detection in fragrance preferences, includes 36,434 comments from 7,387 unique users, providing insights into consumer sentiment towards various perfumes. It is designed to facilitate the analysis of user preferences through sentiment analysis, allowing for the clustering of perfumes based on shared attributes. The dataset features three main components: 1. User Reviews and Perfume Attributes Dataset: This captures user sentiments expressed in comments, along with metadata such as user IDs, perfume details, and ratings across key attributes (scent, longevity, sillage, and design). 2. Emoji Mapping Dataset: This includes 392 common emojis mapped to their Persian equivalents to enhance sentiment analysis accuracy. 3. Sentiment Classification Results: This section includes three CSV files that detail sentiment classifications biased toward specific perfume attributes: Scent, Longevity, and Sillage. These classifications are derived from user comments using the ParsBert model, integrating user ratings to provide a nuanced understanding of consumer preferences. For access to the dataset and further details, please visit our GitHub repository. Total number of user comments: 36,434 Total number of unique users: 7,387 Number of emojis in mapping: 392 Number of CSV files with sentiment classifications: 3

TPM values together with cell type annotations that were obtained from Alex Pollen on 15/10/15 Source: Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

Source: Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq

Source: Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells