Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

\emph {Over-fitting} and\emph {over-smoothing} are two main obstacles of developing deep
Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

Learning graph representations is a fundamental task aimed at capturing various properties
of graphs in vector space. The most recent methods learn such representations for static …

The problem of cross-platform binary code similarity detection aims at detecting whether two
binary functions coming from different platforms are similar or not. It has many security …

The growth of online learning environments entails understanding of how to promote
collaborative knowledge construction processes and create learning environments that …

Throughout the book, we use empirical examples to illustrate the material. Because social
networks are studied in a variety of traditional academic disciplines, we draw our examples …

Relationships and the pattern of relationships have a large and varied influence on both
individual and group action. The fundamental distinction of social network analysis research …