Abstract
Link prediction is an important issue in graph data mining. In social networks, link prediction is used to predict missing links in current networks and new links in future networks. This process has a wide range of applications including recommender systems, spam mail classification, and the identification of domain experts in various research areas. In order to predict future node similarity, we propose a new model, Common Influence Set, to calculate node similarities. The proposed link prediction algorithm uses the common influence set of two unconnected nodes to calculate a similarity score between the two nodes. We used the area under the ROC curve (AUC) to evaluate the performance of our algorithm and that of previous link prediction algorithms based on similarity over a range of problems. Our experimental results show that our algorithm outperforms previous algorithms.
Highlights
Social networks are complex, and usually have a large number of nodes and links, and the network structure is constantly changing
Link prediction is an important element in social network analysis, it can be applied to many aspects of social network analysis, such as friend recommendations in social networks, prediction of potential links in biological protein networks, or the prediction the potential relationships in collaborative networks
The area under the ROC curve (AUC) is calculated as follows: First, find an edge from the set Et − E0 and calculate its similarity score as s1, where Et represents the edge set in Gt, E0 represents the edge set in G0, and Et − E0 represents the increased edges of the graph G evolving from G0 to Gt
Summary
Usually have a large number of nodes and links, and the network structure is constantly changing. The feature method differs from the structural method In this case, two scholars who have, for example, published papers relating to link prediction and community clustering, will have a greater probability of cooperating. In order to achieve link prediction, we first need to find a common group, and calculate the influence of the group on two unconnected nodes. Z. Liu et al.: Link Prediction in Evolving Networks Base on Information Propagation approximate model to quickly calculate the influence of a node set on a single node. We propose an algorithm based on the propagation of influence for calculating similarity. The similarity index is calculated by finding the common influence set of two unconnected nodes.
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