Min's Projects
GPT-3: Language Models are Few-Shot Learners
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"
A curated list of adversarial attacks and defenses papers on graph-structured data.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
Graph Sampling is a python package containing various approaches which samples the original graph according to different sample sizes.
Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)
Implementation and experiments of graph embedding algorithms.
Platform for designing and evaluating Graph Neural Networks (GNN)
This is a Pytorch implementation of GraphLIME
Tutorials on Machine Learning for Graphs
The official implementation of Graph Normalization
Representation learning on large graphs using stochastic graph convolutions.
Simple reference implementation of GraphSAGE.
[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).
Graph similarity algorithms based on NetworkX.
GraphVite: A General and High-performance Graph Embedding System
low cost software radio platform
Heterogeneous Graph Neural Network
nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph
diy testing tool for weak memory models, herd design
The Herd toolsuite to deal with .cat memory models (version 7.xx)
A light & simple & responsive page for academic websites on Hexo, crafted from academicpages on Jekyll.