LPmade is a complete cross-platform software solution for multi-core link prediction and related tasks and analysis. Its first principal contribution is a scalable network library supporting highperformance implementations of the most commonly employed unsupervised link prediction methods. Link prediction in longitudinal data requires a sophisticated and disciplined procedure for correct result...

Many real-world domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a joint probabilistic model over the entire link graph — entity attributes and links. The applicat...

Social network analysis has attracted much attention in recent years. Link prediction is a key research direction within this area. In this paper, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisin...

Link prediction provides useful information for a variety of graph models, including communication, biochemical, and social networks. The goal of link prediction is usually to predict novel interactions (modeled as links/edges) between previously unconnected nodes in a graph. Link prediction is used on social networks to suggest future friends and in protein networks to suggest possible undisco...

Link prediction is a key technique in many applications in social networks; where potential links between entities need to be predicted. Typical link prediction techniques deal with either uniform entities, i.e., company to company, applicant to applicant links, or non-mutual relationships, e.g., company to applicant links. However, there is a challenging problem of link prediction among the co...

Link prediction functions are important tools that are used to predict the evolution of a network, to locate hidden or surprising links, and to recommend new connections that should be formed. Multiple link prediction functions have been developed in the past. However, their evaluation has mostly been based on experimental work, which has shown that the quality of a link prediction function var...

Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks re...

Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationships, e.g., people to item links. However, a challenging problem in link prediction is that of heterogeneous a...

Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for e...