Dynamic Graph Embedding with Explainable Attention Mechanisms

Abstract

Graph embeddings have emerged as a powerful technique for representing complex networks in a low-dimensional vector space. However, traditional graph embedding methods often lack interpretability, making it difficult to understand the underlying reasons for the generated embeddings. This paper proposes a novel dynamic graph embedding approach that integrates attention mechanisms to enhance both the accuracy and explainability of graph representations. The algorithm learns node and edge embeddings while simultaneously assigning weights based on learned attention scores, highlighting the most relevant nodes and edges in the graph. This approach allows for a transparent understanding of the embedding process, revealing the key factors influencing node representations. The resulting embeddings demonstrate improved accuracy compared to traditional methods and provide valuable insights into network structure. The core contribution lies in the dynamic weighting process, adapting to the evolving network structure and prioritizing influential components. The methodology utilizes a modified Graph Neural Network (GNN) architecture incorporating attention modules at each layer. The attention mechanism is trained to capture dependencies between nodes and edges, generating attention weights that are then used to scale the node and edge embeddings. This dynamic weighting scheme results in a more nuanced and accurate representation of the graph. The overall framework is designed for flexibility and can be applied to various types of graphs, including social networks, citation networks, and knowledge graphs. This research addresses the critical need for interpretable graph embeddings, paving the way for more effective network analysis and decision-making.