Decoupled Variational Embedding for Signed Directed Networks

TL;DR

This paper introduces a decoupled variational embedding method that captures both first-order and high-order topological features in signed directed networks, improving node representation learning for tasks like link sign prediction and node recommendation.

Contribution

It proposes a novel decoupled variational auto-encoder framework that effectively encodes complex network topologies, addressing limitations of existing methods focused only on first-order topology.

Findings

DVE outperforms baseline methods on real-world datasets.

The method improves link sign prediction accuracy.

Node recommendation results are significantly enhanced.

Abstract

Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology which indicates the closeness relationships of nodes. However, these methods generally fail to capture the high-order topology which indicates the local structures of nodes and serves as an essential characteristic of the network topology. In addition, for the first-order topology, the additional value of non-existent links is largely ignored. In this paper, we propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks. In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective and further develop a decoupled variational embedding (DVE) method. DVE leverages a specially designed auto-encoder structure to capture both the first-order and high-order topology of signed directed networks, and thus learns more representative node embedding. Extensive experiments are conducted on three widely used real-world datasets. Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE. Qualitative results and analysis are also given to provide a better understanding of DVE.