TY - GEN
T1 - Node classification in signed social networks
AU - Tang, Jiliang
AU - Aggarwal, Charu
AU - Liu, Huan
N1 - Funding Information:
This material is based upon work supported by, or in part by, the U.S. Army Research Office (ARO) under contract/grant number 025071, the Office of Naval Re-search(ONR) under grant number N000141010091, and the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
Copyright © by SIAM.
PY - 2016
Y1 - 2016
N2 - Node classification in social networks has been proven to be useful in many real-world applications. The vast majority of existing algorithms focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks. It is evident from recent developments in signed social network analysis that negative links have added value over positive links. Therefore, the incorporation of negative links has the potential to benefit various analytical tasks. In this paper, we study the novel problem of node classification in signed social networks. We provide a principled way to mathematically model positive and negative links simultaneously and propose a novel framework NCSSN for node classification in signed social networks. Experimental results on real-world signed social network datasets demonstrate the effectiveness of the proposed framework NCSSN. Further experiments are conducted to gain a deeper understanding of the importance of negative links for NCSSN.
AB - Node classification in social networks has been proven to be useful in many real-world applications. The vast majority of existing algorithms focus on unsigned social networks (or social networks with only positive links), while little work exists for signed social networks. It is evident from recent developments in signed social network analysis that negative links have added value over positive links. Therefore, the incorporation of negative links has the potential to benefit various analytical tasks. In this paper, we study the novel problem of node classification in signed social networks. We provide a principled way to mathematically model positive and negative links simultaneously and propose a novel framework NCSSN for node classification in signed social networks. Experimental results on real-world signed social network datasets demonstrate the effectiveness of the proposed framework NCSSN. Further experiments are conducted to gain a deeper understanding of the importance of negative links for NCSSN.
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U2 - 10.1137/1.9781611974348.7
DO - 10.1137/1.9781611974348.7
M3 - Conference contribution
AN - SCOPUS:84991577502
T3 - 16th SIAM International Conference on Data Mining 2016, SDM 2016
SP - 54
EP - 62
BT - 16th SIAM International Conference on Data Mining 2016, SDM 2016
A2 - Venkatasubramanian, Sanjay Chawla
A2 - Meira, Wagner
PB - Society for Industrial and Applied Mathematics Publications
T2 - 16th SIAM International Conference on Data Mining 2016, SDM 2016
Y2 - 5 May 2016 through 7 May 2016
ER -