Several research studies have shown that complex networks modeling real-world
phenomena are characterized by striking properties:(i) they are organized according to …

The Blockchain technology has recently attracted increasing interests worldwide because of
its potential to disrupt existing businesses and to revolutionize the way applications will be …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Network intrusion detection systems (NIDS) play a crucial role in maintaining network
security. However, current NIDS techniques tend to neglect the topological structures of …

The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Abstract Graph Anomaly Detection (GAD) has recently become a hot research spot due to its
practicability and theoretical value. Since GAD emphasizes the application and the rarity of …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Graphs are used to model pairwise relations between entities in many real-world scenarios
such as social networks. Graph Neural Networks (GNNs) have shown their superior ability in …

A great variety of systems in nature, society and technology–from the web of sexual contacts
to the Internet, from the nervous system to power grids–can be modeled as graphs of …

Graph theory provides a language for studying the structure of relations, and it is often used
to study interactions over time too. However, it poorly captures the intrinsically temporal and …