GraphAC (Graph Adversarial Collaboration) – a novel, principled, task-agnostic, and stable framework for evaluating GNNs through contrastive self-supervision, without the need of handcrafted augmentations
A Robust Active Learning Framework for Graph Anomaly Detection (GAD). Optimized implementation of GraphPart adapted for class-imbalance scenarios, supporting datasets like Weibo, Reddit, and Enron.
Baseline Evaluation for the project "Acceleration of Neural Network Training with Microsoft DeepSpeed"
A DGL implementation of "KPConv: Flexible and Deformable Convolution for Point Clouds" (ICCV 2019).
Course project of SJTU CS3319: Foundations of Data Science, 2023 spring
This repository contains the official implementation of the paper titled Multimodal weighted graph representation for information extraction from visually rich documents.
Official DGL Implementation of "Distributed Graph Data Augmentation Technique for Graph Neural Network". KSC 2023
Graph Attention Networks (GATs) for node classification and regression tasks
This project implements models for Protein-Protein Interaction (PPI) prediction, focusing on graph-based methods such as the SOTA GNN model call GIPA.
DGL Implementation of "Efficient Sampling Techniques for Embedding Large Graphs". KCC 2022
Yet Another Machine Learning package for Chemistry
A deep learning approach for molecular property prediction that introduces hierarchical attention pooling to capture scaffold-aware representations. The model aggregates atom features within functional groups before global pooling, combined with scaffold-based curriculum learning for improved generalization across diverse chemical structures.
Official DGL Implementation of "GraphSAINT-NRW, ERW: Subgraph Sampling Techniques using Random Walk Reflecting Node Degree". KSC 2022
6th place solution to KDD CUP 2020 Graph Adversarial Attacks & Defense
Various GNN implementation using DGL library
Predict CS Paper's Subject Area Using Graph Neural Networks
A deep learning architecture combining spectral graph neural networks with curriculum learning for HOMO-LUMO gap prediction on PCQM4Mv2. Features a dual-view architecture with Chebyshev polynomial-based spectral convolutions and complexity-driven training schedules.
Add a description, image, and links to the dgl topic page so that developers can more easily learn about it.
To associate your repository with the dgl topic, visit your repo's landing page and select "manage topics."