Complex networks are frequently employed to model physical or virtual complex systems.
When certain entities exist across multiple systems simultaneously, unveiling their …

Prevailing methods for graphs require abundant label and edge information for learning.
When data for a new task are scarce, meta-learning can learn from prior experiences and …

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data
have been widely used in various domains, ranging from drug discovery to recommender …

A recent study has shown that graph matching models are vulnerable to adversarial
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …

Nowadays, it is common for a person to possess different identities on multiple social
platforms. Social network alignment aims to match the identities that from different networks …

The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of
inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating …

Due to different imaging principles of visible-infrared cameras, there are modal differences
between similar person images. For visible-infrared person re-identification (VI-ReID) …

Network alignment aims to discover nodes in different networks belonging to the same
identity. In recent years, the network alignment problem has aroused significant attentions in …

Network alignment (NA), discovering anchor nodes that represent the same entities across
different networks, plays a fundamental role in information fusion. Most existing embedding …