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; …

With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph embedding algorithms embed a graph into a vector space where the structure and
the inherent properties of the graph are preserved. The existing graph embedding methods …

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 …

Network embedding has received increasing research attention in recent years. The existing
methods show that the high-order proximity plays a key role in capturing the underlying …

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 analyze the efficiency of Monte Carlo methods for incremental computation
of PageRank, personalized PageRank, and similar random walk based methods (with focus …

Code embedding, as an emerging paradigm for source code analysis, has attracted much
attention over the past few years. It aims to represent code semantics through distributed …

Online social networks play a major role in modern societies, and they have shaped the way
social relationships evolve. Link prediction in social networks has many potential …