Community Detection

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

The DBLP is a citation network dataset. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources. The first version contains 629,814 papers and 632,752 citations. Each paper is associated with abstract, authors, year, venue, and title. The data set can be used for clustering with network and side information, studying influence in the citation network, finding the most influential papers, topic modeling analysis, etc. Source: https://www.aminer.org/citation

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

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

Yeast dataset consists of a protein-protein interaction network. Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. Source: http://vlado.fmf.uni-lj.si/pub/networks/data/bio/Yeast/Yeast.htm

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.

Models character profiles and gives dialogue agents the ability to learn characters' language styles through their HLAs. Source: ALOHA: Artificial Learning of Human Attributes for Dialogue Agents

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

This dataset is a collection of undirected and unweighted LFR benchmark graphs as proposed by Lancichinetti et al. [1]. We generated the graphs using the code provided by Santo Fortunato on his personal website [2], embedded in our evaluation framework [3], with two different parameter sets. Let N denote the number of vertices in the network, then Maximum community size: 0.2N (Set A); 0.1N (Set B) Minimum community size: 0.05N (Set A); 10 (Set B) Maximum node degree: 0.19N (Set A); 0.19N (Set B) Community size distribution exponent: 1.0 (Set A); 1.0 (Set B) Degree distribution exponent: 2.0 (Set A); 2.0 (Set B). All other parameters assume default values. We provide graphs with different combinations of average degree, network size and mixing parameter for the given parameter sets: Set A: For average degrees in {15, 25, 50} we provide network sizes in {300, 600, 1200}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs. Set A: For average degrees in {15, 25, 50} we provide mixing parameters in {0.35, 0.45, 0.55}, each with network sizes in {300, 450, 600, 900, 1200, 1800, 2400, 3600, 4800, 6200, 9600, 19200}. For each configuration we provide 50 benchmark graphs. Set B: For average degrees in {20} we provide network sizes in {300, 600, 1200, 2400}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs. Benchmark graphs are given in edge list format. Further, for each benchmark graph we provide ground truth communities as membership list and as structured datatype (.json), its generating random seeds and basic network statistics. [1] Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical Review E 78(4):046110,https://doi.org/10.1103/PhysRevE.78.046110 [2] https://www.santofortunato.net/resources, Accessed: 19 Jan 2021 [3] https://github.com/synwalk/synwalk-analysis, Accessed: 19 Jan 2021

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

Twitter-HyDrug is a real-world hypergraph data that describes the drug trafficking communities on Twitter. We first crawl the metadata (275,884,694 posts and 40,780,721 users) through the official Twitter API from Dec 2020 to Aug 2021. Afterward, we generate a drug keyword list that covers 21 drug types that may cause drug overdose or drug addiction problems to filter the tweets that contain drug-relevant information. Based on the keyword list, we obtain 266,975 filtered drug-relevant posts by 54,680 users. Moreover, we define six types of drug communities, i.e., cannabis, opioid, hallucinogen, stimulant, depressant, and others communities, based on the drug functions. Six researchers spent 62 days annotating these Twitter users into six communities based on the annotation rules discussed in the next section. With the specific criteria, six researchers annotated the filtered metadata separately. For these Twitter users with disagreed labels, we conducted further discussion among annotators for cross-validation. To conclude, we obtained the drug hypergraph including 2,936 user nodes and 33,892 hyperedges.