TY - GEN
T1 - Robust multi-network clustering via joint cross-domain cluster alignment
AU - Liu, Rui
AU - Cheng, Wei
AU - Tong, Hanghang
AU - Wang, Wei
AU - Zhang, Xiang
N1 - Funding Information:
ACKNOWLEDGMENT: This work was partially supported by the National Science Foundation grants IIS-1162374, IIS-1218036, IIS-1313606 and IIS-1017415, by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, by National Institutes of Health under the grant number R01LM011986 and U54GM114833-01, Region II University Transportation Center under the project number 49997-33 25.
Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/5
Y1 - 2016/1/5
N2 - Network clustering is an important problem thathas recently drawn a lot of attentions. Most existing workfocuses on clustering nodes within a single network. In manyapplications, however, there exist multiple related networks, inwhich each network may be constructed from a different domainand instances in one domain may be related to instances in otherdomains. In this paper, we propose a robust algorithm, MCA, formulti-network clustering that takes into account cross-domain relationshipsbetween instances. MCA has several advantages overthe existing single network clustering methods. First, it is ableto detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networksby leveraging the duality between clustering individual networksand inferring cross-network cluster alignment. Finally, it providesa multi-network clustering solution that is more robust to noiseand errors. We perform extensive experiments on a variety ofreal and synthetic networks to demonstrate the effectiveness andefficiency of MCA.
AB - Network clustering is an important problem thathas recently drawn a lot of attentions. Most existing workfocuses on clustering nodes within a single network. In manyapplications, however, there exist multiple related networks, inwhich each network may be constructed from a different domainand instances in one domain may be related to instances in otherdomains. In this paper, we propose a robust algorithm, MCA, formulti-network clustering that takes into account cross-domain relationshipsbetween instances. MCA has several advantages overthe existing single network clustering methods. First, it is ableto detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networksby leveraging the duality between clustering individual networksand inferring cross-network cluster alignment. Finally, it providesa multi-network clustering solution that is more robust to noiseand errors. We perform extensive experiments on a variety ofreal and synthetic networks to demonstrate the effectiveness andefficiency of MCA.
KW - Graph Clustering
KW - Multi-network
UR - http://www.scopus.com/inward/record.url?scp=84963600729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963600729&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2015.13
DO - 10.1109/ICDM.2015.13
M3 - Conference contribution
AN - SCOPUS:84963600729
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 291
EP - 300
BT - Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Data Mining, ICDM 2015
Y2 - 14 November 2015 through 17 November 2015
ER -