# Attributed Social Network Embedding

## TL;DR

This paper introduces a social network embedding framework that incorporates both network structure and rich attribute information to produce more informative representations, improving tasks like link prediction and node classification.

## Contribution

The paper proposes a novel SNE framework that jointly preserves structural and attribute proximity, enhancing social network embedding performance.

## Key findings

- SNE outperforms node2vec with 8.2% improvement in link prediction.
- SNE achieves 12.7% better accuracy in node classification.
- Extensive experiments on four real-world social networks validate the effectiveness of SNE.

## Abstract

Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Social Network Embedding framework (SNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, SNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, SNE significantly outperforms node2vec with an 8.2% relative improvement on the link prediction task, and a 12.7% gain on the node classification task.