Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of com...

Link prediction, the problem of identifying missing links among a set of inter-related data entities, is a popular field of research due to its application to graph-like domains. Producing consistent evaluations of the performance of the many link prediction algorithms being proposed can be challenging due to variable graph properties, such as size and density. In this paper we first discuss tr...

An important fact in studying link prediction is that the structural properties of networks have significant impacts on the performance of algorithms. Therefore, how to improve the performance of link prediction with the aid of structural properties of networks is an essential problem. By analyzing many real networks, we find a typical structural property: nodes are preferentially linked to the...

We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency v...

Reliable link quality prediction is an imperative for the efficient operation of mobile ad-hoc wireless networks (MANETs). In this paper it is shown that popular link quality prediction algorithms for 802.11 MANETs perform much more poorly when applied in real urban environments than they do in corresponding simulations. Our measurements show that the best performing prediction algorithm failed...

The problem of link prediction and its variants have driven significant research efforts over the past decade. Real world applications, ranging from social networks (Facebook friend recommendation) to national security (detecting links in terrorist networks), have motivated interest in this problem. Link prediction can be reframed into other varieties, such as missing link prediction or link re...

Recently, many researchers have been attracted in link prediction which is an effective technique to be used in graph based models analysis. By using link prediction method we can understand associations between nodes. To the best of our knowledge, most of previous works in this area have not explored the prediction of links in dynamic Multi-dimension Networks and have not explored the predicti...

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing inte...

Multi-modal Social Networks (MSNs) allow users to form explicit (by adding new friends in their network) or implicit (by similarly co-rating items) social networks. Previous research work was limited either to the prediction of new relationships among users (i.e. Link Prediction problem) or to the prediction of item ratings (i.e. Rating Prediction problem and Item Recommendations). Recent link ...

Link prediction is a key technique in many applications such as recommender systems, where potential links between users and items need to be predicted. A challenge in link prediction is the data sparsity problem. In this paper, we address this problem by jointly considering multiple heterogeneous link prediction tasks such as predicting links between users and different types of items includin...