Hub-aware Random Walk Graph Embedding Methods for Classification

TL;DR

This paper introduces two hub-aware random walk graph embedding algorithms tailored for node classification, emphasizing high-degree nodes to improve predictive accuracy on real-world networks.

Contribution

The paper presents novel hub-aware random walk strategies for graph embedding, specifically enhancing node classification performance over existing methods like node2vec.

Findings

Significant improvement in classification accuracy with proposed methods.

Effective focus on high-degree nodes enhances embedding quality.

Outperforms node2vec in experimental evaluations.

Abstract

In the last two decades we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector-based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general-purpose embeddings suitable for a variety of applications such as node clustering, node classification, graph visualisation and link prediction. In this paper, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. Random walk sampling strategies of the proposed algorithms have been designed to pay special attention to hubs -- high-degree nodes that have the most critical role for the overall connectedness in large-scale graphs. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real-world networks. The obtained results indicate that our methods considerably improve the predictive power of examined classifiers compared to currently the most popular random walk method for generating general-purpose graph embeddings (node2vec).