CIAGELP: Clustering Inspired Augmented Graph Embedding based Link Prediction in dynamic networks

For a long time, numerous methods have been explored for the crucial and intricate task of link prediction. Among the most effective approaches are those that involve generating embeddings from various graph components such as nodes, edges, and groups. These representations aim to project the vertex space into a lower-dimensional space, ensuring that vertices and edges with similar contexts are represented closely. While random walk-based embedding (RWE) methods have shown significant improvements, their performance tends to be limited for dynamic networks. To address this, we have introduced CIAGELP (Clustering Inspired Augmented Graph Embedding-based Link Prediction), a distinctive approach that utilizes an augmented graph to generate more promising paths and consequently efficient embeddings. The augmentation of the graph is achieved through a customized pairwise clustering coefficient, which not only captures the local structural context but also strongly influences the strength of connections between pairs of nodes. Additionally, to address the drawbacks of previous RWE approaches on dynamic networks, such as inferior accuracy and high computational cost, our approach employs an enhanced RWE mechanism that considers only the differential graph among subsequent snapshots and generates embeddings efficiently at a low cost. Through comprehensive comparisons with different machine learning methods, various augmentation ratios, and state-of-the-art methods based on random walk embeddings, we demonstrate the superiority of our CIAGELP approach. By leveraging augmented graphs with cluster similarity and considering differential network dynamics for embedding generation in dynamic networks, our approach substantially outperforms previous random walk methods.