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

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

Blog Article

Abstract 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 read more 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 canon imageclass mf227dw 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.