Neighbor-Enhanced Link Prediction in Bipartite Networks.

Link prediction in bipartite networks is a challenging task due to their distinct structural characteristics, where edges only exist between nodes of different types. Most existing methods are based on structural similarity, assigning similarity scores to node pairs under the assumption that a higher similarity corresponds to a higher likelihood of connection. Local structural methods, in particular, are widely favored for their simplicity, interpretability, and computational efficiency. However, real-world bipartite networks often exhibit highly heterogeneous node degree distributions, which introduce biases and undermine the effectiveness of traditional local structure-based methods. To address this issue, we propose a novel link prediction framework that explicitly adjusts for the degree heterogeneity of intermediate nodes between unconnected node pairs and incorporates their influence within local connection patterns formed around these pairs. Furthermore, our framework differentiates between the roles of same-type and cross-type nodes by leveraging quadrangle graphs between unconnected nodes. This approach allows for a more nuanced capture of unique properties of bipartite networks and effectively mitigates the inherent degree bias commonly observed in such networks, resulting in considerable improvements in prediction accuracy. Experimental results on ten diverse bipartite networks demonstrate that our framework achieves competitive and robust performance compared to nineteen state-of-the-art link prediction methods.