Wireless self-organizing networks such as mesh networks strive hard to get rid of mobility and radio propagation effects. Links – the basic elements ensuring connectivity in wireless networks – are impacted first from them. But what happens if one could mitigate these effects by forecasting the links’ future states? In this paper, we propose XCoPred (using Cross-Correlation to Predict), a patte...

There has been much recent interest in generative models for graphs. The intuition behind the study of such link prediction functions is that they provide a succinct description of the process by which networks grow and evolve: a model that accurately predicts small-scale actions such as coauthorships should help us understand the global properties of the network. Previous work in social networ...

Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which conside...

Extremist organizations all over the world increasingly use online social networks as a communication media for recruitment and planning. As such, online social networks are also a source of information utilized by intelligence and counter terror organizations investigating the relationships between suspected individuals. Unfortunately, the data mined from open sources is usually far from being...

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and anti...

In the last years link prediction in complex networks has attracted an ever increasing attention from the scientific community. In this paper we apply link prediction models to a very challenging scenario: predicting the onset of future diseases on the base of the current health status of patients. To this purpose, a comorbidity network where nodes are the diseases and edges represent the conte...

Networks provide a powerful formalism for modeling complex sys-tems, by representing the underlying set of pairwise interactions.But much of the structure within these systems involves interac-tions that take place among more than two nodes at once — forexample, communication within a group rather than person-to-person, collaboration among a team rather than a pair of co-aut...

The link-prediction problem is an open issue in data mining and knowledge discovery, which attracts researchers from disparate scientific communities. A wealth of methods have been proposed to deal with this problem. Among these approaches, most are applied in unweighted networks, with only a few taking the weights of links into consideration. In this paper, we present a weighted model for undi...

As the availability and importance of relational data—such as the friendships summarized on a social networking website—increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine learning community has focused on laten...

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed CAR (Cannistrai-Alanis-Ravai) index shows the power of local link/triangle information in improving link-prediction accuracy. With the information of level-2 links, which are links between common-neighbors, most classical similarity indices can be improved. Ne...