TY - GEN
T1 - Significant edge detection in target network by exploring multiple auxiliary networks
AU - Du, Nan
AU - Gao, Jing
AU - Ge, Liang
AU - Gopalakrishnan, Vishrawas
AU - Jia, Xiaowei
AU - Li, Kang
AU - Zhang, Aidong
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - Despite the ability to model many real world settings as a network, one major challenge in analyzing network data is that important and reliable links between objects are usually obscured by noisy information and hence not readily discernible. In this paper, we propose to detect these important and reliable links - significant edges, from a target network by using multiple auxiliary networks and a limited amount of labelled information. In this process, we first abstract the community knowledge learnt across target and auxiliary networks to detect significant patterns. The mined community knowledge captures the key profile of network relationships and thus can be used to determine whether an existing edge indicates a true or false relationship. Experiments on real world network data show that our two staged solution - a joint matrix factorisation procedure followed by edge significance score ranking, accurately predicts significant edges in target network by jointly exploring the underlying knowledge embedded in both target and auxiliary networks.
AB - Despite the ability to model many real world settings as a network, one major challenge in analyzing network data is that important and reliable links between objects are usually obscured by noisy information and hence not readily discernible. In this paper, we propose to detect these important and reliable links - significant edges, from a target network by using multiple auxiliary networks and a limited amount of labelled information. In this process, we first abstract the community knowledge learnt across target and auxiliary networks to detect significant patterns. The mined community knowledge captures the key profile of network relationships and thus can be used to determine whether an existing edge indicates a true or false relationship. Experiments on real world network data show that our two staged solution - a joint matrix factorisation procedure followed by edge significance score ranking, accurately predicts significant edges in target network by jointly exploring the underlying knowledge embedded in both target and auxiliary networks.
KW - Auxiliary networks
KW - Link prediction
KW - Significant edge detection
KW - Social network
UR - https://www.scopus.com/pages/publications/84962584812
U2 - 10.1145/2808797.2809302
DO - 10.1145/2808797.2809302
M3 - Conference contribution
AN - SCOPUS:84962584812
T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
SP - 210
EP - 217
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
A2 - Pei, Jian
A2 - Tang, Jie
A2 - Silvestri, Fabrizio
PB - Association for Computing Machinery, Inc
T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Y2 - 25 August 2015 through 28 August 2015
ER -