Community Recovery in Graphs with Locality

Yuxin Chen, Govinda Kamath, Changho Suh, David Tse
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:689-698, 2016.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-chena16, title = {Community Recovery in Graphs with Locality}, author = {Chen, Yuxin and Kamath, Govinda and Suh, Changho and Tse, David}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {689--698}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/chena16.pdf}, url = {https://proceedings.mlr.press/v48/chena16.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Community Recovery in Graphs with Locality %A Yuxin Chen %A Govinda Kamath %A Changho Suh %A David Tse %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-chena16 %I PMLR %P 689--698 %U https://proceedings.mlr.press/v48/chena16.html %V 48 %X 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.
RIS
TY - CPAPER TI - Community Recovery in Graphs with Locality AU - Yuxin Chen AU - Govinda Kamath AU - Changho Suh AU - David Tse BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-chena16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 689 EP - 698 L1 - http://proceedings.mlr.press/v48/chena16.pdf UR - https://proceedings.mlr.press/v48/chena16.html 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. ER -
APA
Chen, Y., Kamath, G., Suh, C. & Tse, D.. (2016). Community Recovery in Graphs with Locality. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:689-698 Available from https://proceedings.mlr.press/v48/chena16.html.

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