Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
IEEE

Integrating Evolutionary and Structural Properties for Protein Interaction Site Prediction Using Graph and Temporal Convolutions

Abstract: Predicting protein interaction sites is crucial for tasks such as constructing protein interaction networks, analyzing protein functions, studying molecular-level pathology, and designing ...
IEEE

GoCLIP: Graph one-class CLassification for Intellectual Property Circuit Identification

Abstract: Circuit Intellectual Property (IP) Identification is crucial to prevent malicious misuse of IP cores/blocks in Integrated Circuits (ICs). The main objective in IP identification is to ...
GitHub

Subgraph-conditioned Graph Information Bottleneck

We aim to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in ...