Representation Learning on Heterostructures via Heterogeneous Anonymous Walks

TL;DR

This paper introduces a novel approach for learning representations of heterostructures in heterogeneous networks using heterogeneous anonymous walks, demonstrating superior performance on synthetic and real-world datasets.

Contribution

It proposes the first theoretically guaranteed heterogeneous anonymous walk technique and a data-driven embedding method for heterostructure learning.

Findings

Outperforms classic homogeneous and heterogeneous methods

Effective on large-scale networks

Provides a new benchmark for heterostructure learning

Abstract

Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks is still a void. In this paper, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we firstly propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and its variant coarse HAW (CHAW). Then, we devise the heterogeneous anonymous walk embedding (HAWE) and its variant coarse HAWE in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods, and can be applied on large-scale networks.