A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs

TL;DR

This paper introduces an efficient algorithm for finding approximate nearest neighbors based on truncated commute times in large graphs, significantly reducing computation time for applications like social networks and recommender systems.

Contribution

The paper proposes a novel algorithm to efficiently compute approximate nearest neighbors using truncated commute times, avoiding exhaustive pairwise calculations.

Findings

Near-linear scaling in computation time on large graphs

Effective approximation of nearest neighbors in large-scale networks

Applicable to graphs with up to 100,000 entities

Abstract

Recently there has been much interest in graph-based learning, with applications in collaborative filtering for recommender networks, link prediction for social networks and fraud detection. These networks can consist of millions of entities, and so it is very important to develop highly efficient techniques. We are especially interested in accelerating random walk approaches to compute some very interesting proximity measures of these kinds of graphs. These measures have been shown to do well empirically (Liben-Nowell & Kleinberg, 2003; Brand, 2005). We introduce a truncated variation on a well-known measure, namely commute times arising from random walks on graphs. We present a very novel algorithm to compute all interesting pairs of approximate nearest neighbors in truncated commute times, without computing it between all pairs. We show results on both simulated and real graphs of size up to 100; 000 entities, which indicate near-linear scaling in computation time.