TY - GEN
T1 - NetTrans
T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
AU - Zhang, Si
AU - Tong, Hanghang
AU - Xia, Yinglong
AU - Xiong, Liang
AU - Xu, Jiejun
N1 - Funding Information:
7 ACKNOWLEDGEMENTS This work is supported by National Science Foundation under grant No. 1947135, and 1715385, by the NSF Program on Fairness in AI in collaboration with Amazon under award No. 1939725, by the United States Air Force and DARPA under contract number FA8750-17-C-0153 3, Department of Homeland Security under Grant Award Number 17STQAC00001-03-03. The content of the information in this document does not necessarily reflect the position or the policy of the Government or Amazon, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2020 ACM.
PY - 2020/8/23
Y1 - 2020/8/23
N2 - Finding node associations across different networks is the cornerstone behind a wealth of high-impact data mining applications. Traditional approaches are often, explicitly or implicitly, built upon the linearity and/or consistency assumptions. On the other hand, the recent network embedding based methods promise a natural way to handle the non-linearity, yet they could suffer from the disparate node embedding space of different networks. In this paper, we address these limitations and tackle cross-network node associations from a new angle, i.e., cross-network transformation. We ask a generic question: Given two different networks, how can we transform one network to another? We propose an end-to-end model that learns a composition of nonlinear operations so that one network can be transformed to another in a hierarchical manner. The proposed model bears three distinctive advantages. First (composite transformation), it goes beyond the linearity/consistency assumptions and performs the cross-network transformation through a composition of nonlinear computations. Second (representation power), it can learn the transformation of both network structures and node attributes at different resolutions while identifying the cross-network node associations. Third (generality), it can be applied to various tasks, including network alignment, recommendation, cross-layer dependency inference. Extensive experiments on different tasks validate and verify the effectiveness of the proposed model.
AB - Finding node associations across different networks is the cornerstone behind a wealth of high-impact data mining applications. Traditional approaches are often, explicitly or implicitly, built upon the linearity and/or consistency assumptions. On the other hand, the recent network embedding based methods promise a natural way to handle the non-linearity, yet they could suffer from the disparate node embedding space of different networks. In this paper, we address these limitations and tackle cross-network node associations from a new angle, i.e., cross-network transformation. We ask a generic question: Given two different networks, how can we transform one network to another? We propose an end-to-end model that learns a composition of nonlinear operations so that one network can be transformed to another in a hierarchical manner. The proposed model bears three distinctive advantages. First (composite transformation), it goes beyond the linearity/consistency assumptions and performs the cross-network transformation through a composition of nonlinear computations. Second (representation power), it can learn the transformation of both network structures and node attributes at different resolutions while identifying the cross-network node associations. Third (generality), it can be applied to various tasks, including network alignment, recommendation, cross-layer dependency inference. Extensive experiments on different tasks validate and verify the effectiveness of the proposed model.
KW - network alignment
KW - network representation learning
KW - network transformation
KW - node associations
KW - social recommendation
UR - http://www.scopus.com/inward/record.url?scp=85090414941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090414941&partnerID=8YFLogxK
U2 - 10.1145/3394486.3403141
DO - 10.1145/3394486.3403141
M3 - Conference contribution
AN - SCOPUS:85090414941
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 986
EP - 996
BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 23 August 2020 through 27 August 2020
ER -