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; …

Link prediction methods anticipate the likelihood of a future connection between two nodes
in a given network. The methods are essential in social networks to infer social interactions …

Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …

In the fast-growing scholarly big data background, social network technologies have recently
aroused widespread attention in academia and industry. The concept of academic social …

In social networks, link prediction predicts missing links in current networks and new or
dissolution links in future networks, is important for mining and analyzing the evolution of …

In this paper, we adopt the representation learning approach to align users across multiple
social networks where the social structures of the users are exploited. In particular, we …

Predicting anchor links across social networks has important implications to an array of
applications, including cross-network information diffusion and cross-domain …

Social networks have become very important for networking, communications, and content
sharing. Social networking applications generate a huge amount of data on a daily basis …

Online social networks can often be represented as heterogeneous information networks
containing abundant information about: who, where, when and what. Nowadays, people are …

Heterogeneous social networks, which are characterized by diverse interaction types, have
resulted in new challenges for missing link prediction. Most deep learning models tend to …