Gowalla is a location-based social networking website where users share their locations by checking-in. The friendship network is undirected and was collected using their public API, and consists of 196,591 nodes and 950,327 edges. We have collected a total of 6,442,890 check-ins of these users over the period of Feb. 2009 - Oct. 2010.

Brightkite was once a location-based social networking service provider where users shared their locations by checking-in. The friendship network was collected using their public API, and consists of 58,228 nodes and 214,078 edges. The network is originally directed but we have constructed a network with undirected edges when there is a friendship in both ways. We have also collected a total of 4,491,143 checkins of these users over the period of Apr. 2008 - Oct. 2010.

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

https://snap.stanford.edu/data/index.html

Epinions social network

Dataset information

This is who-trust-whom online social network of a a general consumer review
site Epinions.com. Members of the site can decide whether to ''trust'' each
other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user.

Dataset statistics

Nodes 131828
Edges 841372
Nodes in largest WCC 119130 (0.904)
Edges in largest WCC 833695 (0.991)
Nodes in largest SCC 41441 (0.314)
Edges in largest SCC 693737 (0.825)
Average clustering coefficient 0.2424
Number of triangles 4910076
Fraction of closed triangles 0.08085
Diameter (longest shortest path) 14
90-percentile effective diameter 4.9

Source (citation)

J. Leskovec, D. Huttenlocher, J. Kleinberg: Signed Networks in Social Media.
28th ACM Conference on Human Factors in Computing Systems (CHI), 2010.
http://cs.stanford.edu/people/jure/pubs/triads-chi10.pdf

Files
File Description
soc-sign-epinions.txt.gz Directed Epinions signed social network

Link Function: information

Youtube social network and ground-truth communities Dataset information Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

more info : https://snap.stanford.edu/data/com-Youtube.html

Gowalla Dataset

The Gowalla dataset, sourced from the Stanford Network Analysis Project (SNAP), contains user check-ins and social network information from the now-defunct location-based social networking platform Gowalla.

  Key features:

Check-in data: records of user check-ins at various locations with timestamps and geographical coordinates (latitude, longitude). Social graph: user relationships represented as a graph, where edges denote friendships between… See the full description on the dataset page: https://huggingface.co/datasets/habedi/gowalla-dataset.

This dataset includes graph theory indicators (centrality and clustering coefficients) for the Stanford Network Analysis Project (SNAP) "email-Eu-core-temporal" network, a well-known reference dataset for Social Network Analysis (SNA) of e-mail traffic.

This dataset contains complementary data to the paper "A Hybrid Matheuristic for the Spread of Influence on Social Networks" [1], which proposes a matheuristic for combinatorial optimization problems involving the spread of information in social networks.

For the computational experiments discussed in that paper, we provide:

• Two sets of instances, originally obtained from [2-6];
• The solutions attained by exact and heuristic methods;
• The collected results;
• The matheuristic source code;

The directories "benchmark_*/instances/" contain files that describe the sets of instances. Each instance is associated with a graph containing

The first

This dataset contains complementary data to the paper "A Row Generation Algorithm for Finding Optimal Burning Sequences of Large Graphs" [1], which proposes an exact algorithm for the Graph Burning Problem, an NP-hard optimization problem that models a form of contagion diffusion on social networks.

Concerning the computational experiments discussed in that paper, we make available:

• Four sets of instances;
• The optimal (or best known) solutions obtained;
• The source code;
• An Appendix with additional details about the results.

The "delta" input sets include graphs that are real-world networks [1,2], while the "grid" input set contains graphs that are square grids.

The directories "delta_10K_instances", "delta_100K_instances", "delta_4M_instances" and "grid_instances" contain files that describe the sets of instances. The first two lines of each file contain:

where