Most link prediction algorithms only consider local or global characteristics of the graph, so it is difficult to reach equilibrium in the accuracy and the computational complexity. And the research on link prediction in weighted networks is relatively less. A new algorithm STNMP (Similarity based on Transmission Nodes of Multiple Paths) for link prediction in weighted social networks is propos...

Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. Wherein, similarity based algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, ...

Link Prediction, that is, predicting the formation of links or interactions in a network in the future, is an important task in network analysis. Link prediction provides useful insights for other applications, such as recommendation system, disease-gene candidate detection and so on. Most link prediction methods assume that there is only one single type in the network. However, many real-world...

Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. In this context, similaritybased algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link predi...

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives ...

Social Network depicts the relationship like friendship, common interests etc. among various individuals. Social Network Analysis deals with analysis of these social relationships. Link prediction algorithms are used to predict these social relationships. Given a social network graph in which a node represents a user and an edge represents the relationship between the users, link prediction alg...

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique fo...

In the link prediction problem, formulated as a binary classification problem, we want to classify each pair of disconnected nodes in the network whether they will be connected by a link in the future. We study link formation in social networks with two types of links over several time periods. To solve the link prediction problem, we follow the approach of counting 3-node graphlets and suggest...

Traditional methods for link prediction can be categorized into three main types: graph structure feature-based, latent feature-based, and explicit feature-based. Graph structure feature methods leverage some handcrafted node proximity scores, e.g., common neighbors, to estimate the likelihood of links. Latent feature methods rely on factorizing networks’ matrix representations to learn an embe...

In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we propose to use this information to improve the accuracy of prediction. Our strategy involves a joint optimization procedure over the space of adjacency matrices and VAR matrices which takes into accoun...