Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various
analytics tasks on graph and network data. However, some recent studies raise concerns …

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …

The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Network analysis has been used for many years in ecological research to analyze
organismal associations, for example in food webs, plant-plant or plant-animal interactions …

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain
networks, from molecular to behavioral scales, are ever increasing in size and complexity …