Graph Kolmogorov-Arnold Networks for Anomaly Detection

The proposed project aims to develop a new method for finding unusual patterns in data represented as graphs, such as social networks, communication networks, or financial transactions. We proposed a novel method Graph Kolmogorov-Arnold Networks(G-KANs) to improve how we detect anomalies, which are important for identifying security threats or fraudulent activities. This project will help our participating institutions collaborate and share knowledge in artificial intelligence and its applications. The knowledge gained will benefit both academic research and industry practices, leading to better tools for analyzing data and solving real-world problems..

Faculty Supervisor:

Abdessamad Ben Hamza

Student:

Partner:

Université Abdelmalek Essaadi

Discipline:

Computer science

Sector:

Education

University:

Concordia University

Program: