Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …

Despite achieving remarkable performance, deep graph learning models, such as node
classification and network embedding, suffer from harassment caused by small adversarial …

The proliferation of mobile computing and smartphone technologies has resulted in an
increasing number and range of services from myriad service providers. These mobile …

Recent studies on federated learning (FL) have sought to solve the system heterogeneity
issue by designing customized local models for different clients. However, public dataset …

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 …

Network alignment, which aims at learning a matching between the same entities across
multiple information networks, often suffers challenges from feature inconsistency, high …

Recent literatures have shown that knowledge graph (KG) learning models are highly
vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of …

Recent studies have shown that graph learning models are highly vulnerable to adversarial
attacks, and network alignment methods are no exception. How to enhance the robustness …

Graphs have become increasingly important in many applications and domains such as
querying relationships in social networks or managing rich metadata generated in scientific …