wiki_talk_temporal — English Wikipedia talk page edits, temporal (2001-2008)
- Description
A temporal network of users of the English Wikipedia editing each other's user talk pages, as distributed by SNAP and used by Paranjape, Benson and Leskovec in their study of motifs in temporal networks. Nodes are users, and each directed edge from user u to user v with a timestamp t means that u edited the talk page of v at time t; repeated edits between the same users are separate edges. The data span 2320 days, from August 2001 to January 2008, and were extracted from the Wikipedia edit history used by Leskovec, Huttenlocher and Kleinberg in their study of the Wikipedia promotion process. Edits of users on their own talk page appear as self-loops (about 1.7 million of the edges). This extraction differs from the static
wikipedia-en-talknetwork; a later extraction, from the dumps of 2015 (with messages until December 2015), is the networkenof the entrywiki_talk.The network has the following properties:
- Nodes:
name, the Wikipedia user name. - Edges:
time, the time of the edit (Unix time, in seconds).
- Nodes:
- Tags
- Social Communication Unweighted Timestamps Multigraph
- Citation
-
- A. Paranjape, A. R. Benson, J. Leskovec, "Motifs in Temporal Networks." Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM), 601-610 (2017) 10.1145/3018661.3018731 Sci-Hub
- J. Leskovec, D. Huttenlocher, J. Kleinberg, "Governance in Social Media: A Case Study of the Wikipedia Promotion Process." Proceedings of the International AAAI Conference on Web and Social Media (ICWSM) 4(1), 98-105 (2010) 10.1609/icwsm.v4i1.14013 Sci-Hub
- Upstream URL OK
- https://snap.stanford.edu/data/wiki-talk-temporal.html
- Networks
-
Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties