Attributed Graph Embedding with Random Walk Regularization and Centrality-Based Attention

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph network as low-dimensional dense real-valued vectors for the application in practical analysis tasks. In recent years, the study of graph network representation learning has received increasing attention from researchers, and, among them, graph neural networks (GNNs) based on deep learning are playing an increasingly important role in this field. However, the fact that higher-order neighborhood information cannot be used effectively is a problem of most existing graph neural networks. Moreover, it tends to ignore the influence of latent representation and structural properties on graph embedding. In hopes of solving these issues, we introduce centrality encoding to learn the node properties, add an attention mechanism consideration to better distinguish the significance of neighboring nodes, and introduce random walk regularization to make sample neighbors that consistently satisfy predetermined criteria. This allows us to learn a representation of a potential node. We tested the performance of our model on node-clustering and link prediction tasks using three widely recognized benchmark datasets. The outcomes of our experiments demonstrate that our model significantly surpasses the baseline method in both tasks, indicating that the graph embedding it generates is highly expressive.

Automated summary

Graph-embedding learning aims to represent nodes in a graph network as low-dimensional dense vectors for practical analysis tasks. Graph neural networks based on deep learning have gained attention in this field, but they often have limitations in utilizing higher-order neighborhood information effectively and considering structural properties. To address these issues, we propose centrality encoding, attention mechanism, and random walk regularization to improve the node representation. Experimental results on benchmark datasets demonstrate that our model outperforms baseline methods in node-clustering and link prediction tasks, showing highly expressive graph embedding.