The study of complex networks has significantly advanced our understanding of community
structures which serves as a crucial feature of real-world graphs. Detecting communities in …

Graphs encode important structural properties of complex systems. Machine learning on
graphs has therefore emerged as an important technique in research and applications. We …

Identifying and tracking community structures in complex networks are one of the
cornerstones of network studies, spanning multiple disciplines, from statistics to machine …

Community detection, aiming at partitioning a network into multiple substructures, is
practically importance. Graph convolutional network (GCN), a new deep-learning technique …

As the number of social network users grows exponentially with increasingly complex
profiles, community detection algorithms play a critical role in user portrait analysis. The …

Community detection technologies have the general research significance in complex
networks, in which the topology information of network is worthy to be the focus for its widely …

Hypergraphs naturally represent group interactions, which are omnipresent in many
domains: collaborations of researchers, co-purchases of items, joint interactions of proteins …

The rapid development of community detection algorithms, while serving users in social
networks, also brings about certain privacy problems. In this work, we study community …

As one of the most typical cybercrime types, phishing scams have extended the devil's hand
to the emerging blockchain ecosystem in recent years. Especially huge economic losses …

Community detection can exhibit the aggregation behavior of complex networks. Network
motifs are the fundamental building blocks which can reveal the higher‐order structure of …