IEEE

Adversarial Attack and Defense on Discrete Time Dynamic Graphs

Abstract: Graph learning methods have achieved remarkable performance in various domains such as social recommendation, financial fraud detection, and so on. In real applications, the underlying graph ...
GitHub

Semantically Consistent Discrete Diffusion for 3D Biological Graph Generation

This repository contains the official code for our MICCAI 2025 paper, “Semantically Consistent Discrete Diffusion for 3D Biological Graph Generation.” We introduce a discrete diffusion framework that ...
GitHub

PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation

Official PyTorch implementation of "PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation". This work has been accepted to CVPR 2024. Abstract: Existing point cloud ...
IEEE

Gen-GraphEx: Generative In-Distribution Graph Explanations for Time-Efficient Model-Level Interpretability of GNNs

Abstract: Graph neural networks (GNNs) have become the prevailing methodology for addressing graph data-related tasks, permeating critical domains like recommendation systems and drug development. The ...