Link prediction is a problem of predicting future edges of an undirected graph based on a single snapshot of data of that graph. Vertex proximity measures are indicies giving numerical scores for every pair of vertices in a graph that can be used for predicting future edges. This short note describes an R package 'linkprediction' implementing 20 different vertex similarity and proximity measures from the literature. The article provides the definitions of implemented measures, describes the main user-facing functions, and illustrates the use of the methods with a problem of predicting future co-authorship relations between researchers of the University of Warsaw
Many real-world domains are relational in nature since they consist of a set of objects related to e...
In recent years, the study of social networks and the analysis of these networks in various fields h...
International audienceThe prediction of new links in social networks is a challenging task. In this ...
In a social network there can be many different kind of links or edges between the nodes. Those coul...
The automated analysis of social networks has become an important problem due to the pro-liferation ...
Recommendation systems based on historical action logs between users and items are usually formulate...
The problem of link prediction has recently attracted considerable attention in various domains, suc...
© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network resear...
Abstract. Understanding the structures why links are formed is an important and prominent research t...
Given a snapshot of a social network, can we infer which new interactions among its members are like...
The link prediction problem can be used for predicting the link changes that are difficult to unders...
Link prediction aims to uncover the underlying relationship behind networks, which could be utilize...
Link prediction in social networks has a long history in complex network research area. The formatio...
A social network can have many different types of links or margins between nodes. Those, for example...
Link prediction aims to identify unknown or missing connections in a network. The methods based on n...
Many real-world domains are relational in nature since they consist of a set of objects related to e...
In recent years, the study of social networks and the analysis of these networks in various fields h...
International audienceThe prediction of new links in social networks is a challenging task. In this ...
In a social network there can be many different kind of links or edges between the nodes. Those coul...
The automated analysis of social networks has become an important problem due to the pro-liferation ...
Recommendation systems based on historical action logs between users and items are usually formulate...
The problem of link prediction has recently attracted considerable attention in various domains, suc...
© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network resear...
Abstract. Understanding the structures why links are formed is an important and prominent research t...
Given a snapshot of a social network, can we infer which new interactions among its members are like...
The link prediction problem can be used for predicting the link changes that are difficult to unders...
Link prediction aims to uncover the underlying relationship behind networks, which could be utilize...
Link prediction in social networks has a long history in complex network research area. The formatio...
A social network can have many different types of links or margins between nodes. Those, for example...
Link prediction aims to identify unknown or missing connections in a network. The methods based on n...
Many real-world domains are relational in nature since they consist of a set of objects related to e...
In recent years, the study of social networks and the analysis of these networks in various fields h...
International audienceThe prediction of new links in social networks is a challenging task. In this ...