Capturing Edge Attributes via Network Embedding

TL;DR

This paper introduces a new network embedding method that incorporates edge attributes along with network structure, improving the quality of node representations for tasks like link prediction and node classification.

Contribution

The proposed method jointly models network structure and edge attributes using a deep architecture, capturing complex interactions for enhanced embedding quality.

Findings

Outperforms existing methods on real-world networks

Using edge attributes improves downstream task performance

Effective on collaboration and social networks

Abstract

Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction and node classification. Most existing embedding methods rely solely on network structure. However, in practice we often have auxiliary information about the nodes and/or their interactions, e.g., content of scientific papers in co-authorship networks, or topics of communication in Twitter mention networks. Here we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations. Our method jointly minimizes the reconstruction error for higher-order node neighborhood, social roles and edge attributes using a deep architecture that can adequately capture highly non-linear interactions. We demonstrate the efficacy of our model over existing state-of-the-art methods on a variety of real-world networks including collaboration networks, and social networks. We also observe that using edge attributes to inform network embedding yields better performance in downstream tasks such as link prediction and node classification.