Link prediction based on node weighting in complex networks

Link prediction is used to predict future links in complex networks. Traditional methods proposed for link prediction make estimates based on similarity measurements, taking into account only the instant topological structure of the network. However, especially in dynamic networks, the activity of nodes varies over time, so it is not enough to measure similarity from topological properties for a good prediction process. Accordingly, the success rate is low in prediction processes where the power of the nodes in the network is not sufficiently reflected. In this study, a novel link prediction model called Link Prediction Based on Node Weighting in Complex Networks is proposed to overcome the mentioned problems. Unlike using weights between nodes, the proposed model is based on calculating the own weights of the nodes and making the link prediction. The weighting process includes factors such as eigenvector centrality, experience, continuity that can reveal the power of nodes over time. The model consists of two parts. The first part is node weighting, which calculates the strength of nodes in the network. The second part is the node-weighted link prediction process, where node weights are used to predict future links. Scientific collaboration data at IEEE Xplore and Australian Open Tennis Tournaments data were used to test the success of the proposed model. In experimental studies conducted in networks created from different time periods, it has been determined that the proposed method gives more successful results than the latest technology methods according to the AUC metric.

Automated summary

Link prediction is crucial for forecasting future links in complex networks, with traditional methods often falling short due to limited consideration of node weighting. This study proposes a novel model based on node weighting, showing superior success rates compared to current technology methods.