TY - GEN
T1 - Mining Unstable Communities from Network Ensembles
AU - Rahman, Ahsanur
AU - Jan, Steve
AU - Kim, Hyunju
AU - Prakash, B. Aditya
AU - Murali, T. M.
N1 - Funding Information:
This work was supported by grants to TMM from the Environmental Protection Agency (EPA-RD-83499801), the National Science Foundation grant (DBI-1062380), and the National Institute of General Medical Sciences of the National Institutes of Health grant (R01-GM095955), grants to BAP from the National Science Foundation (IIS-1353346) and from the Maryland Procurement Office (H98230-14-C-0127), and a Facebook faculty gift to BAP. Any opinions, findings and conclusions or recommendations express in this material are those of the author(s) and do not necessarily reflect the views of the respective funding agencies.
Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/29
Y1 - 2016/1/29
N2 - Ensembles of graphs arise in several natural applications, such as mobility tracking, computational biology, socialnetworks, and epidemiology. A common problem addressed by many existing mining techniques is to identify subgraphs of interest in these ensembles. In contrast, in this paper, we propose to quickly discover maximally variable regions of the graphs, i.e., sets of nodes that induce very different subgraphs across the ensemble. We first develop two intuitive and novel definitions of such node sets, which we then show can be efficiently enumerated using a level-wise algorithm. Finally, using extensive experiments on multiple real datasets, we show how these sets capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.
AB - Ensembles of graphs arise in several natural applications, such as mobility tracking, computational biology, socialnetworks, and epidemiology. A common problem addressed by many existing mining techniques is to identify subgraphs of interest in these ensembles. In contrast, in this paper, we propose to quickly discover maximally variable regions of the graphs, i.e., sets of nodes that induce very different subgraphs across the ensemble. We first develop two intuitive and novel definitions of such node sets, which we then show can be efficiently enumerated using a level-wise algorithm. Finally, using extensive experiments on multiple real datasets, we show how these sets capture the main structural variations of the given set of networks and also provide us with interesting and relevant insights about these datasets.
KW - Graph Mining
KW - Scaled Subgraph Divergence
KW - Subgraph Divergence
KW - UC
KW - Unstable Community
UR - http://www.scopus.com/inward/record.url?scp=84964786854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964786854&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2015.87
DO - 10.1109/ICDMW.2015.87
M3 - Conference contribution
AN - SCOPUS:84964786854
T3 - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
SP - 508
EP - 515
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
A2 - Wu, Xindong
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Dy, Jennifer G.
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Cui, Peng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Y2 - 14 November 2015 through 17 November 2015
ER -