MCoGCN-motif high-order feature-guided embedding learning framework for social link prediction

Traditional social link prediction models primarily concentrate on the adjacency features of the network, overlooking the rich high-order structural information within. Therefore, the study of effective extraction and encoding of these high-order features, and their integration into prediction models, holds significant theoretical and practical value. To address this challenge, we propose a novel embedding learning framework guided by motif high-order features for social link prediction tasks. Firstly, we utilize the motif adjacency matrix to capture complex patterns in social networks. Through a propagation process, node embeddings can carry the structural information of the network. Subsequently, we design a simplified attention mechanism, allowing embeddings carrying motif high-order features to guide the representation of embeddings based on adjacency features. We then employ a feed-forward neural network to optimize node embeddings. Specifically, this framework addresses the issue of weakly correlated nodes in the network, which struggle to learn effective embeddings due to a lack of direct information. By guiding with high-order motif features, the framework enhances the similarity and predictive power of these node embeddings. Finally, we conducted a detailed evaluation of the predictive performance of our model on four social networks. The experimental results indicate that our model exhibits high accuracy and advantages in predicting social links.