Meta-Path Constrained Random Walk Inference for Large-Scale Heterogeneous Information Networks

TL;DR

This paper introduces a new inference framework for large-scale heterogeneous information networks that efficiently learns inference patterns and performs unbiased random walks with minimal user input, improving over previous methods.

Contribution

It proposes a meta-path constrained inference framework that automatically learns inference patterns using a tree structure, reducing user effort and bias in large-scale HINs.

Findings

Achieves state-of-the-art performance on YAGO2 and DBLP datasets.

Effectively learns inference patterns with minimal user guidance.

Performs unbiased random walk inference in large-scale HINs.

Abstract

Heterogeneous information network (HIN) has shown its power of modeling real world data as a multi-typed entity-relation graph. Meta-path is the key contributor to this power since it enables inference by capturing the proximities between entities via rich semantic links. Previous HIN studies ask users to provide either 1) the meta-path(s) directly or 2) biased examples to generate the meta-path(s). However, lots of HINs (e.g., YAGO2 and Freebase) have rich schema consisting of a sophisticated and large number of types of entities and relations. It is impractical for users to provide the meta-path(s) to support the large scale inference, and biased examples will result in incorrect meta-path based inference, thus limit the power of the meta-path. In this paper, we propose a meta-path constrained inference framework to further release the ability of the meta-path, by efficiently learning the HIN inference patterns via a carefully designed tree structure; and performing unbiased random walk inference with little user guidance. The experiment results on YAGO2 and DBLP datasets show the state-of-the-art performance of the meta-path constrained inference framework.