ChandlerBang

awesome-self-supervised-gnn

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

awesome-graph-attack-papers

Adversarial attacks and defenses on Graph Neural Networks.

Pro-GNN

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

GCond

[ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"

SelfTask-GNN

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

Movie-QA-System

基于知识图谱的电影问答系统。 This is a simple question answering and recommender system on movies (based on Knowledge Graph).

SimP-GCN

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

GTrans

[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"

DeCorr

[KDD 2022] Implementation of "Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective"

AutoSSL

[ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"

pytorch-gnn-meta-attack

Pytorch implementation of gnn meta attack (mettack). Paper title: Adversarial Attacks on Graph Neural Networks via Meta Learning.

Awesome-Dataset-Distillation

Awesome Dataset Distillation Papers

ChandlerBang.github.io

Personal website

CSrankings

A web app for ranking computer science departments according to their research output in selective venues.

graph-adversarial-learning-literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

ica

Python implementation of the Iterative Classification Algorithm

Simple-SearchEngine

A simple search engine used for fulltext retrieval, implemented by Python.

CSE847

Homework for CSE847 MSU

dance

DANCE: A Deep Learning Library for Single-Cell Analysis

mvgrl

PairNorm

Source code for PairNorm (ICLR 2020)

pycma

Python implementation of CMA-ES

vim_anywhere

website

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes