Measuring Proximity on Graphs with Side Information

, and
IEEE International Conference on Data Mining (ICDM)
Pisa, Italy,
Abstract. This paper studies how to incorporate side information (such as users’ feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that Fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations.
author = {Hanghang and Tong and Huiming and Qu and Hani and Jamjoom},
title = {{Measuring Proximity on Graphs with Side Information}},
booktitle = {IEEE International Conference on Data Mining (ICDM)},
address = {Pisa, Italy},
month = {December},
year = {2008}