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Computer Science > Social and Information Networks

arXiv:1605.08490 (cs)
[Submitted on 27 May 2016]

Title:A Simple and Strongly-Local Flow-Based Method for Cut Improvement

Authors:Nate Veldt, David F. Gleich, Michael W. Mahoney
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Abstract:Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We introduce and analyze a new method for locally-biased graph-based learning called SimpleLocal, which finds good conductance cuts near a set of seed vertices. An important feature of our algorithm is that it is strongly-local, meaning it does not need to explore the entire graph to find cuts that are locally optimal. This method solves the same objective as existing strongly-local flow-based methods, but it enables a simple implementation. We also show how it achieves localization through an implicit L1-norm penalty term. As a flow-based method, our algorithm exhibits several ad- vantages in terms of cut optimality and accurate identification of target regions in a graph. We demonstrate the power of SimpleLocal by solving problems on a 467 million edge graph based on an MRI scan.
Subjects: Social and Information Networks (cs.SI); Data Structures and Algorithms (cs.DS)
Cite as: arXiv:1605.08490 [cs.SI]
  (or arXiv:1605.08490v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.1605.08490

Submission history

From: Nate Veldt [view email]
[v1] Fri, 27 May 2016 01:55:31 UTC (3,300 KB)
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