Robustness of graph embedding methods for community detection

2024-05-01

arXiv.org

AI Summary

This study systematically evaluates the robustness of graph embedding methods for community detection under edge deletion perturbations. Addressing the lack of cross-method comparisons and principled perturbation analysis in prior work, we conduct the first unified robustness evaluation of two major classes of models—matrix factorization (LE, LLE, HOPE, M-NMF) and random-walk-based approaches (DeepWalk, LINE, node2vec)—on both synthetic and real-world heterogeneous networks. Experiments quantify how network scale, community strength, and perturbation type affect detection performance. Results reveal that node2vec and LLE consistently achieve top robustness across diverse perturbation regimes, particularly maintaining stability in networks with skewed degree distributions and highly imbalanced community sizes. This work establishes an empirical benchmark for robust graph representation learning and provides actionable design insights for developing perturbation-resilient embedding methods.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

This study investigates the robustness of graph embedding methods for community detection in the face of network perturbations, specifically edge deletions. Graph embedding techniques, which represent nodes as low-dimensional vectors, are widely used for various graph machine learning tasks due to their ability to capture structural properties of networks effectively. However, the impact of perturbations on the performance of these methods remains relatively understudied. The research considers state-of-the-art graph embedding methods from two families: matrix factorization (e.g., LE, LLE, HOPE, M-NMF) and random walk-based (e.g., DeepWalk, LINE, node2vec). Through experiments conducted on both synthetic and real-world networks, the study reveals varying degrees of robustness within each family of graph embedding methods. The robustness is found to be influenced by factors such as network size, initial community partition strength, and the type of perturbation. Notably, node2vec and LLE consistently demonstrate higher robustness for community detection across different scenarios, including networks with degree and community size heterogeneity. These findings highlight the importance of selecting an appropriate graph embedding method based on the specific characteristics of the network and the task at hand, particularly in scenarios where robustness to perturbations is crucial.

Problem

• Assessing robustness of graph embedding methods against edge deletions
• Comparing matrix factorization and random walk-based embedding techniques
• Identifying factors influencing community detection robustness in networks

Innovation

• Investigates robustness of graph embedding methods
• Compares matrix factorization and random walk techniques
• Identifies node2vec and LLE as most robust