Exploring Graph Representations in Machine Learning for Network Robustness Evaluation

Network robustness, which refers to a network's ability to withstand malicious attacks on its vertices and edges, is critical across various natural and industrial domains. This paper delves into the assessment of network robustness through machine learning-based approaches, with a specific focus on structure-based representations and graph embeddings. The evaluation encompasses both synthetic and real-world networks, and three types of representation paradigms are scrutinized: 1) structure-based representations, including adjacency, incidence, and modularity matrices, 2) graph embeddings, including learning feature representation (LFR), DeepWalk, large-scale information network embedding (LINE), node2vec, structural deep network embedding (SDNE), and struc2vec, and 3) one-dimensional graph embeddings. The findings underscore the preference of convolutional neural networks (CNNs) with structure-based representations, highlighting the efficacy of adjacency and modularity matrices. While graph embeddings showcase versatility, their overall performance is comparatively lower, emphasizing the crucial role of representation complexity. This study contributes valuable insights into robustness evaluation methodologies and underscores the significance of tailored graph representations.