TY - GEN

T1 - Mining connection pathways for marked nodes in large graphs

AU - Akoglu, Leman

AU - Vreeken, Jilles

AU - Tong, Hanghang

AU - Chau, Duen Horng

AU - Tatti, Nikolaj

AU - Faloutsos, Christos

N1 - Publisher Copyright:
Copyright © SIAM.

PY - 2013

Y1 - 2013

N2 - Suppose we are given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce DOT2DOT, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.

AB - Suppose we are given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce DOT2DOT, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.

UR - http://www.scopus.com/inward/record.url?scp=84960112617&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960112617&partnerID=8YFLogxK

U2 - 10.1137/1.9781611972832.5

DO - 10.1137/1.9781611972832.5

M3 - Conference contribution

AN - SCOPUS:84960112617

T3 - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

SP - 37

EP - 45

BT - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

A2 - Ghosh, Joydeep

A2 - Obradovic, Zoran

A2 - Dy, Jennifer

A2 - Zhou, Zhi-Hua

A2 - Kamath, Chandrika

A2 - Parthasarathy, Srinivasan

PB - Siam Society

T2 - SIAM International Conference on Data Mining, SDM 2013

Y2 - 2 May 2013 through 4 May 2013

ER -