In decentralized systems, the quest for heightened security and integrity within blockchain
networks becomes an issue. This survey investigates anomaly detection techniques in …

We study the implications of the modeling choice to use a graph, instead of a hypergraph, to
represent real-world interconnected systems whose constituent relationships are of higher …

Machine learning, deep learning, and NLP methods on knowledge graphs are present in
different fields and have important roles in various domains from self-driving cars to friend …

Network dismantling is a process of identifying influential nodes that can decompose a
network into disconnected sub-networks. This provides a novel approach to understanding …

Node classification is to predict the class label of a node by analyzing its properties and
interactions in a network. We note that many existing solutions for graph-based node …

Graph self-supervised learning (SSL) holds considerable promise for mining and learning
with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature …

Role discovery is an emerging research area in the analysis of social networks, biological
networks, and neural networks. The fundamental idea of role discovery is partitioning the …

Anchor link prediction enhances the effectiveness of digital forensics through the
identification of multiple social network users. The current methods based on deep learning …

Roles are defined as the equivalent classes of isomorphic nodes in the network. They focus
on the local connective patterns and describe the structural similarities between nodes, and …

Geometric Morphometrics can be used to describe morphology as a series of coordinates
after the effects of variation in translation, rotation, and scale have been removed. This can …