The diffusion method is one of the main methods of community detection in complex
networks. In this method, the use of the concept that diffusion within the nodes that are …

Despite the prevalence of recent success in learning from static graphs, learning from time-
evolving graphs remains an open challenge. In this work, we design new, more stringent …

Link prediction is a technique to forecast future new or missing relationships between
entities based on the current network information. Graph theory and network science are …

Link prediction is amongst the most crucial tasks in network science and graph data
analytics. Given the snapshot of a network at a particular instance of time, the study of link …

In order to navigate a complex web of relationships, an individual must learn and represent
the connections between people in a social network. However, the sheer size and …

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful
node embedding methods. In particular, graph AE and VAE were successfully leveraged to …

Social network analysis (SNA) has attracted a lot of attention in several domains in the past
decades. It can be of 2‐folds: one is content‐based, and another one is structured‐based …

Networks derived from various disciplinary of sociality and nature are dynamic and
incomplete, and temporal link prediction has wide applications in recommendation system …

Link prediction in attributed networks has attracted increasing attention recently due to its
valuable real-world applications. Various related methods have been proposed, but most of …

This paper investigates the link prediction in multiplex networks. Multiplex networks that
represent multiple types of interaction between the same group of individuals are a special …