Graph neural networks (GNNs) had shown excellent performance in complex graph data
modelings such as node classification, link prediction and graph classification. However …

Illegitimate access detection systems in hospital logs perform post hoc detection instead of
runtime access restriction to allow widespread access in emergencies. We study the …

Adversarial machine learning (AML) research is concerned with robustness of machine
learning models and algorithms to malicious tampering. Originating at the intersection …

SimRank is one of the most fundamental measures that evaluate the structural similarity
between two nodes in a graph and has been applied in a plethora of data management …

When dealing with large graphs, community detection is a useful data triage tool that can
identify subsets of the network that a data analyst should investigate. In an adversarial …

Social networks arise as a result of complex interactions among people, and homophily
plays an important role in this process. If we view homophily as a dominant force in network …

In this work, we study the the task of graph matching under several scenarios in an
adversarial context. Despite achieving remarkable performance, deep learning based graph …

Abstract Information retrieval is one of the most challenging tasks for the mankind and to
retrieve information interaction is required, which ultimately leads to the formation of …