Link prediction based on Subgraph evolution in dynamic social networks

This source preferred by Marcin Budka

Authors: Juszczyszyn, K., Musial, K. and Budka, M.

http://eprints.bournemouth.ac.uk/18410/

http://www.scopus.com/inward/record.url?eid=2-s2.0-84856207916&partnerID=40&md5=49ae46f23d1e36ba6461ac595846c202

Pages: 27-34

DOI: 10.1109/PASSAT/SocialCom.2011.15

This data was imported from DBLP:

Authors: Juszczyszyn, K., Musial, K. and Budka, M.

http://eprints.bournemouth.ac.uk/18410/

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6112285

Journal: SocialCom/PASSAT

Pages: 27-34

Publisher: IEEE

ISBN: 978-1-4577-1931-8

DOI: 10.1109/PASSAT/SocialCom.2011.15

This data was imported from Scopus:

Authors: Juszczyszyn, K., Musiał, K. and Budka, M.

http://eprints.bournemouth.ac.uk/18410/

Journal: Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011

Pages: 27-34

ISBN: 9780769545783

DOI: 10.1109/PASSAT/SocialCom.2011.15

We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed. © 2011 IEEE.

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