8 datasets tagged as the tag “Temporal network”.
8 datasets.
A YouTube (user social network growth over time) dataset of 3,223,585 nodes and 9,375,374 edges, collected by A. Mislove et al. via YouTube API. Each record contains directed: False, weighted: True, temporal: True. Temporal undirected YouTube friendship network showing how the social graph grew over time — 9.4M edges with Unix timestamps.. Suitable for temporal network analysis, social network growth modeling, YouTube social dynamics.
A StackExchange SuperUser (technical Q&A community) dataset of 194,085 users and 1,443,339 directed interaction edges, collected by Paranjape, Benson & Leskovec via SNAP (2017). Each record contains source user ID, target user ID, Unix timestamp of interaction. Temporal directed interaction network from SuperUser covering answers, comments on answers, and comments on questions — three types of user interactions collapsed into a single edge list with Unix timestamps.. Suitable for temporal network analysis, Q&A community structure, motif detection, information flow in technical communities.
A Gnutella peer-to-peer network dataset combining the 9 SNAP snapshots of August 2002 (Ripeanu et al.): 191,327 nodes and 506,442 directed edges in all. SNAP numbers the hosts of each snapshot separately, so the snapshots sit side by side in one graph with node IDs of the form snapshot:host, and no host is linked across snapshots. Suitable for temporal network analysis, graph evolution, and peer-to-peer network resilience.
A StackExchange Ask Ubuntu (Linux/Ubuntu Q&A community) dataset of 137,517 users and 280,102 directed interaction edges, collected by Paranjape, Benson & Leskovec via SNAP (2017). Each record contains source user ID, target user ID, Unix timestamp of interaction. Temporal directed network of answer-to-question interactions on Ask Ubuntu — edges connect answering users to question-asking users with Unix timestamps, capturing the flow of technical knowledge.. Suitable for Q&A network analysis, temporal interaction patterns, community detection, knowledge flow in technical communities.
A Math Overflow (ALL) dataset of 24,818 users and 239,978 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (all) between users with Unix timestamps. The dataset is notable for its ALL interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A Math Overflow (A2Q) dataset of 21,688 users and 90,489 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (a2q) between users with Unix timestamps. The dataset is notable for its A2Q interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A Math Overflow (C2Q) dataset of 16,836 users and 101,329 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (c2q) between users with Unix timestamps. The dataset is notable for its C2Q interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A MOOC online learning platform dataset of 7,047 students, 97 course content items, and 411,749 student actions, collected by Kumar, Zhang & Leskovec via SNAP (2019). Each record contains action ID, student user ID, course content item ID, relative timestamp, dropout label (0/1), and 4 pre-computed action feature dimensions. Bipartite temporal network of student interactions with MOOC course content — 1.0% dropout rate across 396,633 actions. Includes pre-computed embedding features per action, enabling direct use in temporal graph learning without feature engineering.. Suitable for student dropout prediction, temporal interaction networks, dynamic graph embedding, online learning behavior analysis.
YouTube
Stack Exchange