Similarity-based link prediction in social networks: A path and node combined approach

Abstract

With the rapid development of the Internet, the computational analysis of social networks has grown to be a salient issue. Various research analyses social network topics, and a considerable amount of attention has been devoted to the issue of link prediction. Link prediction aims to predict the interactions that might occur between two entities in the network. To this aim, this study proposed a novel path and node combined approach and constructed a methodology for measuring node similarities. The method was illustrated with five real datasets obtained from different types of social networks. An extensive comparison of the proposed method against existing link prediction algorithms was performed to demonstrate that the path and node combined approach achieved much higher mean average precision (MAP) and area under the curve (AUC) values than those that only consider common nodes (e.g. Common Neighbours and Adamic/Adar) or paths (e.g. Random Walk with Restart and FriendLink). The results imply that two nodes are more likely to establish a link if they have more common neighbours of lower degrees. The weight of the path connecting two nodes is inversely proportional to the product of degrees of nodes on the pathway. The combination of node and topological features can substantially improve the performance of similarity-based link prediction, compared with node-dependent and path-dependent approaches. The experiments also demonstrate that the path-dependent approaches outperform the node-dependent appraoches. This indicates that topological features of networks may contribute more to improving performance than node features.

Object-Action Association Extraction from Knowledge Graphs

“…where E kni f e is the set of nodes in the S kni f e subgraph, and E f old is the set of nodes in the S f old subgraph. Recent studies that apply this method, as is or with small variations, are [4,19,20]. The focus is on object identification and on finding the similarity of two nodes in a knowledge graph.…”

A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem

“…These measurements could be considered as the topological features for the underlying network. As in Yu et al [23], the topological features of networks may contribute more to improving performance than other type of features such as node features. Throughout this section, the symbols x , y denote nodes, N denotes number of nodes in the network.…”

“…Feature selection works by removing redundant and irrelevant features and selecting only the most significant according to some criterion such as high prediction quality [23]. There are many of feature selection algorithms such as in Sheydaei et al [30].…”

From words to connections: Word use similarity as an honest signal conducive to employees’ digital communication

“…Although most studies have explored similarities based on socio-demographic variables, several authors have extended their analysis to a wide range of variables -including attitudes, psychological traits and values -that are seen as latent homophily factors [49][50][51][52], and have shown that the 'homophily phenomenon' is complex and is not based solely on socio-demographic factors. Relational aspects, assortative mechanisms based on individual attributes and proximity factors can all influence the way people communicate and the frequency of their communication [37,[53][54][55][56][57]. As for network position, it appears that people occupying similar structural positions in the network [26][27][28][29]58] tend to share similar opinions and behaviours.…”