Abstract
The existing studies of link prediction mostly focused on using network topology properties to improve the accuracy of link prediction. More broadly, researches on the role of community structures for link prediction have recently received increasing attention. In this study, we propose a succinct algorithm that is built on community structures to improve the performance of link prediction, and it has been verified by both of synthetic benchmarks and real-world networks. More importantly, we introduce different null models to study the role of community structures on link prediction more carefully. Firstly, it is found that clearer community structures correspond to the higher performance of link prediction algorithms that are based on community information. Secondly, the role of links within a community and that between two communities are further distinguished. The edges within one community play a vital role for link prediction of the whole network, and conversely the edges between two communities have a minimal effect on that. At last, we reveal the relationship and dependence between this special meso-scale structure (community) and micro-scale structures of different orders (i.e., degree distribution, assortativity, and transitivity) for link prediction.
Highlights
In the field of complex networks, link prediction is a significant problem of predicting the unknown or fake links from a network which has uncertain structures [1], [2]
It requires less limiting conditions than previous community-based link methods (i.e., Local Community Neighbors, LOCAL COMMUNITY NEIGHBORS (LCN)), so the method of Global Community Neighbors (GCN) can significantly improve the performance of link prediction by fully exploring community information compared to previous studies [26]–[29]
EXPERIMENTAL RESULTS OF LFR NETWORKS We have verified that the information of community structures can significantly improve the accuracy of link prediction algorithms
Summary
In the field of complex networks, link prediction is a significant problem of predicting the unknown or fake links from a network which has uncertain structures [1], [2]. B. LOCAL COMMUNITY NEIGHBORS (LCN) Recently, some novel algorithms attempt to use the information that a pair of nodes and their common neighbors which locate in the same community [35]–[39] to improve the performance of traditional methods for link prediction [25], [26]. Whether a synthetic or real-life network has the characteristic of community structures, the LCN algorithms in previous studies are usually used to predict unknown links. In the GCN algorithm, it is necessary to balance the effects of micro-scale (common neighbors) and meso-scale (common community neighbors) characteristics.To quantify the role of community structures for link prediction, we use αGCN to weight commu nity information. If |α| < 1, the weight of community structure information is less than that of common neighbors; if |α| > 1, community information will play a more prominent role than the number of common neighbors
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