TY - GEN
T1 - Community recovery in graphs with locality
AU - Chen, Yuxin
AU - Kamath, Govinda M.
AU - Suh, Changho
AU - Tse, David
PY - 2016
Y1 - 2016
N2 - Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes rather than uniformly sampled between all node pairs, as in most existing models. We present two algorithms that run nearly linearly in the number of measurements and which achieve the information limits for exact recovery.
AB - Motivated by applications in domains such as social networks and computational biology, we study the problem of community recovery in graphs with locality. In this problem, pairwise noisy measurements of whether two nodes are in the same community or different communities come mainly or exclusively from nearby nodes rather than uniformly sampled between all node pairs, as in most existing models. We present two algorithms that run nearly linearly in the number of measurements and which achieve the information limits for exact recovery.
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UR - http://www.scopus.com/inward/citedby.url?scp=84999007148&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84999007148
T3 - 33rd International Conference on Machine Learning, ICML 2016
SP - 1083
EP - 1092
BT - 33rd International Conference on Machine Learning, ICML 2016
A2 - Balcan, Maria Florina
A2 - Weinberger, Kilian Q.
PB - International Machine Learning Society (IMLS)
T2 - 33rd International Conference on Machine Learning, ICML 2016
Y2 - 19 June 2016 through 24 June 2016
ER -