Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yu:2017:10.1109/ICDM.2016.0070,
author = {Yu, W and McCann, J},
doi = {10.1109/ICDM.2016.0070},
pages = {589--598},
publisher = {IEEE},
title = {Random walk with restart over dynamic graphs},
url = {http://dx.doi.org/10.1109/ICDM.2016.0070},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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(|V |3) time and O(|V |2) memory to compute all (|V |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(|V |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(l|V |) memory and O(⌈ |V | l ⌉) I/O costs, where 1 ≤ l ≤ |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.
AU - Yu,W
AU - McCann,J
DO - 10.1109/ICDM.2016.0070
EP - 598
PB - IEEE
PY - 2017///
SN - 2374-8486
SP - 589
TI - Random walk with restart over dynamic graphs
UR - http://dx.doi.org/10.1109/ICDM.2016.0070
UR - http://hdl.handle.net/10044/1/45323
ER -