SNE: Signed Network Embedding

TL;DR

This paper introduces SNE, a novel signed network embedding model that effectively captures positive and negative relationships in signed networks using a log-bilinear approach and path-based node representations.

Contribution

SNE is the first embedding model specifically designed for signed networks, incorporating signed-type vectors and path-based representations to handle positive and negative links.

Findings

SNE outperforms baseline models in node classification.

SNE achieves higher accuracy in link prediction.

Effective for both directed and undirected signed networks.

Abstract

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.