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Random walk with restart over dynamic graphs
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Yu, Weiren and McCann, Julie (2017) Random walk with restart over dynamic graphs. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain, 12-15 Dec 2016. Published in: 2016 IEEE 16th International Conference on Data Mining (ICDM) pp. 589-598. ISSN 2374-8486. doi:10.1109/ICDM.2016.0070
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WRAP-Random-walk-restart-dynamic-graphs-Yu-2017.pdf - Accepted Version - Requires a PDF viewer. Download (825Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ICDM.2016.0070
Abstract
Random Walk with Restart (RWR) is an appealing measure of proximity between nodes based on graph structures. Since real graphs are often large and subject to minor changes, it is prohibitively expensive to recompute proximities from scratch. Previous methods use LU decomposition and degree reordering heuristics, entailing O(|ν| 3 ) time and O(|ν| 2 ) memory to compute all (|ν| 2 ) pairs of node proximities in a static graph. In this paper, a dynamic scheme to assess RWR proximities is proposed: (1) For unit update, we characterize the changes to all-pairs proximities as the outer product of two vectors. We notice that the multiplication of an RWR matrix and its transition matrix, unlike traditional matrix multiplications, is commutative. This can greatly reduce the computation of all-pairs proximities from O(|ν| 3 ) to O(|Δ|) time for each update without loss of accuracy, where |Δ| (≪|V| 2 ) is the number of affected proximities. (2) To avoid O(|V| 2 ) memory for all pairs of outputs, we also devise efficient partitioning techniques for our dynamic model, which can compute all pairs of proximities segment-wisely within O(I|V|) memory and O([|V|/l]) I/O costs, where 1 ≤ I ≤ |V| is a user-controlled trade-off between memory and I/O costs. (3) For bulk updates, we also devise aggregation and hashing methods, which can discard many unnecessary updates further and handle chunks of unit updates simultaneously. Our experimental results on various datasets demonstrate that our methods can be 1-2 orders of magnitude faster than other competitors while securing scalability and exactness.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > QA Mathematics | |||||||||
| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Random walks (Mathematics), Graph algorithms, Proximity matrices | |||||||||
| Journal or Publication Title: | 2016 IEEE 16th International Conference on Data Mining (ICDM) | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2374-8486 | |||||||||
| Book Title: | 2016 IEEE 16th International Conference on Data Mining (ICDM) | |||||||||
| Official Date: | 2 February 2017 | |||||||||
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| Page Range: | pp. 589-598 | |||||||||
| DOI: | 10.1109/ICDM.2016.0070 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | 2016 IEEE 16th International Conference on Data Mining (ICDM) | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Barcelona, Spain | |||||||||
| Date(s) of Event: | 12-15 Dec 2016 |
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