TY - GEN
T1 - Unstable communities in network ensembles
AU - Rahman, Ahsanur
AU - Jan, Steve T.K.
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 (DBI-1062380), and the National Institute of General Medical Sciences of the National Institutes of Health (R01-GM095955), grants to BAP from the National Science Foundation (IIS-1353346), the National Endowment for the Humanities (HG-229283-15), ORNL (Task Order 4000143330) 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:
Copyright © by SIAM.
PY - 2016
Y1 - 2016
N2 - Ensembles of graphs arise in several natural applications. Many techniques exist to compute frequent, dense subgraphs in these ensembles. In contrast, in this paper, we propose to 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. Many techniques exist to compute frequent, dense subgraphs in these ensembles. In contrast, in this paper, we propose to 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.
UR - http://www.scopus.com/inward/record.url?scp=84991687022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991687022&partnerID=8YFLogxK
U2 - 10.1137/1.9781611974348.57
DO - 10.1137/1.9781611974348.57
M3 - Conference contribution
AN - SCOPUS:84991687022
T3 - 16th SIAM International Conference on Data Mining 2016, SDM 2016
SP - 504
EP - 512
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 -