TY - GEN
T1 - An extension of the Owen-value interaction index and its application to inter-links prediction
AU - Szczepański, Piotr L.
AU - Michalak, Tomasz P.
AU - Rahwan, Talal
AU - Wooldridge, Michael
N1 - Funding Information:
Piotr Szczepański was funded by the Polish National Science Centre based on the decision DEC-2013/09/N/ST6/04095. Tomasz Michalak and Michael Wooldridge were supported by the European Research Council under Advanced Grant 291528 ("RACE").
Publisher Copyright:
© 2016 The Authors and IOS Press.
PY - 2016
Y1 - 2016
N2 - Link prediction is a key problem in social network analysis: it involves making suggestions about where to add new links in a network, based solely on the structure of the network. We address a special case of this problem, whereby the new links are supposed to connect different communities in the network; we call it the interlinks prediction problem. This is particularly challenging as there are typically very few links between different communities. To solve this problem, we propose a local node-similarity measure, inspired by the Owen-value interaction index - A concept developed in cooperative game theory and fuzzy systems. Although this index requires an exponential number of operations in the general case, we show that our local node-similarity measure is computable in polynomial time. We apply our measure to solve the inter-links prediction problem in a number of real-life networks, and show that it outperforms all other local similarity measures in the literature.
AB - Link prediction is a key problem in social network analysis: it involves making suggestions about where to add new links in a network, based solely on the structure of the network. We address a special case of this problem, whereby the new links are supposed to connect different communities in the network; we call it the interlinks prediction problem. This is particularly challenging as there are typically very few links between different communities. To solve this problem, we propose a local node-similarity measure, inspired by the Owen-value interaction index - A concept developed in cooperative game theory and fuzzy systems. Although this index requires an exponential number of operations in the general case, we show that our local node-similarity measure is computable in polynomial time. We apply our measure to solve the inter-links prediction problem in a number of real-life networks, and show that it outperforms all other local similarity measures in the literature.
UR - http://www.scopus.com/inward/record.url?scp=85013029069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013029069&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-672-9-90
DO - 10.3233/978-1-61499-672-9-90
M3 - Conference contribution
AN - SCOPUS:85013029069
T3 - Frontiers in Artificial Intelligence and Applications
SP - 90
EP - 98
BT - Frontiers in Artificial Intelligence and Applications
A2 - Kaminka, Gal A.
A2 - Dignum, Frank
A2 - Hullermeier, Eyke
A2 - Bouquet, Paolo
A2 - Dignum, Virginia
A2 - Fox, Maria
A2 - van Harmelen, Frank
PB - IOS Press
T2 - 22nd European Conference on Artificial Intelligence, ECAI 2016
Y2 - 29 August 2016 through 2 September 2016
ER -