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; …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Information diffusion and virus propagation are fundamental processes taking place in
networks. While it is often possible to directly observe when nodes become infected with a …

Networks and network analysis are arguably one of the largest growth areas of the early
twenty-first century in the quantitative sciences. Despite roots in social network analysis …

A fundamental challenge in developing high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

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 …

Autonomous service robots will have to understand vaguely described tasks, such as “set
the table” or “clean up”. Performing such tasks as intended requires robots to fully, precisely …