In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …

The wide availability of user-provided content in online social media facilitates the
aggregation of people around common interests, worldviews, and narratives. However, the …

Networks are a fundamental tool for modeling complex systems in a variety of domains
including social and communication networks as well as biology and neuroscience. The …

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …

Weighted signed networks (WSNs) are networks in which edges are labeled with positive
and negative weights. WSNs can capture like/dislike, trust/distrust, and other social …

Networks provide a powerful formalism for modeling complex systems by using a model of
pairwise interactions. But much of the structure within these systems involves interactions …

Due to the fact much of today's data can be represented as graphs, there has been a
demand for generalizing neural network models for graph data. One recent direction that …