Cross-community shortcut detection based on network representation learning and structural features

Abstract

As social networks continue to expand, an increasing number of people prefer to use social networks to post their comments and express their feelings, and as a result, the information contained in social networks has grown explosively. The effective extraction of valuable information from social networks has attracted the attention of many researchers. It can mine hidden information from social networks and promote the development of social network structures. At present, many ranking node approaches, such as structural hole spanners and opinion leaders, are widely adopted to extract valuable information and knowledge. However, approaches for analyzing edge influences are seldom considered. In this study, we proposed an edge PageRank to mine shortcuts (these edges without direct mutual friends) that are located among communities and play an important role in the spread of public opinion. We first used a network-embedding algorithm to order the spanners and determine the direction of every edge. Then, we transferred the graphs of social networks into edge graphs according to the ordering. We considered the nodes and edges of the graphs of the social networks as edges and nodes of the edge graphs, respectively. Finally, we improved the PageRank algorithm on the edge graph to obtained the edge ranking and extracted the shortcuts of social networks. The experimental results for five different sizes of social networks, such as email, YouTube, DBLP-L, DBLP-M, and DBLP-S, verify whether the inferred shortcut is indeed more useful for information dissemination, and the utility of three sets of edges inferred by different methods is compared, namely, the edge inferred by ER, the edge inferred by the Jaccard index. The ER approach improves by approximately 10%, 9.9%, and 8.3% on DBLP, YouTube, and Orkut. Our method is more effective than the edge ranked by the Jaccard index.