RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved

TL;DR

This paper introduces RWNE, a scalable network embedding framework that explicitly preserves personalized higher-order proximities using random walks with restart, outperforming existing methods.

Contribution

The paper proposes a novel, theoretically grounded framework that incorporates random walks with restart to preserve personalized higher-order proximities in network embeddings.

Findings

Consistently outperforms state-of-the-art methods on real-world networks.

Effectively preserves personalized higher-order proximities.

Scalable and theoretically sound approach.

Abstract

Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved. In this paper, to address the above issues, we present a general scalable random-walk-based network embedding framework, in which random walk is explicitly incorporated into a sound objective designed theoretically to preserve arbitrary higher-order proximity. Further, we introduce the random walk with restart process into the framework to naturally and effectively achieve personalized-weighted preservation of proximities of different orders. We conduct extensive experiments on several real-world networks and demonstrate that our proposed method consistently and substantially outperforms the state-of-the-art network embedding methods.