Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance - Vibhu Verma (International Journal of Algorithms Design and Analysis Review, 2025)
This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to cluster separation and Modularity regarding community detection. The results show that techniques like HOPE, GraRep, and GAT are very good at capturing community structure. Among these, GraRep seems to be the most balanced for both Silhouette and Modularity scores. Other techniques, while very good at creating well-separated clusters, such as structural deep network embedding (SDNE), perform poorly regarding community detection. In contrast, the approaches of Struc2Vec (simulated) and Poincaré exhibit poor performance along both axes. The current work has important implications for understanding strengths and limitations of different graph embedding methods for future research and applications on network analysis, social media analytics, and recommender systems.
Keywords: Clustering; Community Detection; DeepWalk; GAT; GraRep; Graph embeddings; Machine Learning; Network analysis; SDNE; Silhouette score; community structure; data representation; graph neural networks; hope; intrinsic evaluation; modularity; node2vec