Network Sampling Using K-hop Random Walks for Heterogeneous Network Embedding

TL;DR

This paper introduces a K-hop random walk sampling method for heterogeneous network embedding, improving the quality of embeddings for tasks like co-authorship prediction over existing models.

Contribution

It proposes a novel K-hop random walk sampling approach tailored for heterogeneous networks, enhancing embedding quality compared to existing methods.

Findings

The K-hop sampling approach outperforms baselines in co-authorship prediction.

It yields higher quality embeddings than Metapath2vec and Node2vec.

The method is effective for heterogeneous network embedding tasks.

Abstract

Sampling a network is an important prerequisite for unsupervised network embedding. Further, random walk has widely been used for sampling in previous studies. Since random walk based sampling tends to traverse adjacent neighbors, it may not be suitable for heterogeneous network because in heterogeneous networks two adjacent nodes often belong to different types. Therefore, this paper proposes a K-hop random walk based sampling approach which includes a node in the sample list only if it is separated by K hops from the source node. We exploit the samples generated using K-hop random walker for network embedding using skip-gram model (word2vec). Thereafter, the performance of network embedding is evaluated on co-authorship prediction task in heterogeneous DBLP network. We compare the efficacy of network embedding exploiting proposed sampling approach with recently proposed best performing network embedding models namely, Metapath2vec and Node2vec. It is evident that the proposed sampling approach yields better quality of embeddings and out-performs baselines in majority of the cases.