Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

In this paper, we review some advances made recently in the study of mobile phone
datasets. This area of research has emerged a decade ago, with the increasing availability …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Link prediction in complex networks has attracted increasing attention from both physical
and computer science communities. The algorithms can be used to extract missing …

We introduce a variational Bayesian neural network where the parameters are governed via
a probability distribution on random matrices. Specifically, we employ a matrix variate …

Link prediction is an important task for analying social networks which also has applications
in other domains like, information retrieval, bioinformatics and e-commerce. There exist a …

In this paper we investigate multi-task learning in the context of Gaussian Processes (GP).
We propose a model that learns a shared covariance function on input-dependent features …

Complex networks have been used widely to model a large number of relationships. The
outbreak of COVID-19 has had a huge impact on various complex networks in the real …

The evolutionary behavior of temporal networks has gained the attention of researchers with
its ubiquitous applications in a variety of real-world scenarios. Learning evolutionary …