ASAPAAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Stars: ✭ 83 (-62.61%)
SIANCode and data for ECML-PKDD paper "Social Influence Attentive Neural Network for Friend-Enhanced Recommendation"
Stars: ✭ 25 (-88.74%)
LR-GCCFRevisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020
Stars: ✭ 99 (-55.41%)
RioGNNReinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
Stars: ✭ 46 (-79.28%)
visual-compatibilityContext-Aware Visual Compatibility Prediction (https://arxiv.org/abs/1902.03646)
Stars: ✭ 92 (-58.56%)
SpektralGraph Neural Networks with Keras and Tensorflow 2.
Stars: ✭ 1,946 (+776.58%)
pyg autoscaleImplementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch
Stars: ✭ 136 (-38.74%)
kaggle-champsCode for the CHAMPS Predicting Molecular Properties Kaggle competition
Stars: ✭ 49 (-77.93%)
grailInductive relation prediction by subgraph reasoning, ICML'20
Stars: ✭ 83 (-62.61%)
MTAGCode for NAACL 2021 paper: MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences
Stars: ✭ 23 (-89.64%)
demo-routenetDemo of RouteNet in ACM SIGCOMM'19
Stars: ✭ 79 (-64.41%)
mdgradPytorch differentiable molecular dynamics
Stars: ✭ 127 (-42.79%)
EulerA distributed graph deep learning framework.
Stars: ✭ 2,701 (+1116.67%)
SimP-GCNImplementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"
Stars: ✭ 43 (-80.63%)
Deep Learning DrizzleDrench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Stars: ✭ 9,717 (+4277.03%)
GNN-Recommender-SystemsAn index of recommendation algorithms that are based on Graph Neural Networks.
Stars: ✭ 505 (+127.48%)
PyNetsA Reproducible Workflow for Structural and Functional Connectome Ensemble Learning
Stars: ✭ 114 (-48.65%)
NMNSource code and datasets for ACL 2020 paper: Neighborhood Matching Network for Entity Alignment.
Stars: ✭ 55 (-75.23%)
Entity-Graph-VLNCode of the NeurIPS 2021 paper: Language and Visual Entity Relationship Graph for Agent Navigation
Stars: ✭ 34 (-84.68%)
SelfGNNA PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).
Stars: ✭ 24 (-89.19%)
DiGCNImplement of DiGCN, NeurIPS-2020
Stars: ✭ 25 (-88.74%)
AGCNNo description or website provided.
Stars: ✭ 17 (-92.34%)
grbGraph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.
Stars: ✭ 70 (-68.47%)
robust-gcnImplementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".
Stars: ✭ 35 (-84.23%)
how attentive are gatsCode for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
Stars: ✭ 200 (-9.91%)
BGCNA Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).
Stars: ✭ 129 (-41.89%)
3DInfomaxMaking self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.
Stars: ✭ 107 (-51.8%)
EgoCNNCode for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)
Stars: ✭ 16 (-92.79%)
Graph-EmbedddingReimplementation of Graph Embedding methods by Pytorch.
Stars: ✭ 113 (-49.1%)
AC-VRNNPyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"
Stars: ✭ 21 (-90.54%)
StellargraphStellarGraph - Machine Learning on Graphs
Stars: ✭ 2,235 (+906.76%)
SiGATsource code for signed graph attention networks (ICANN2019) & SDGNN (AAAI2021)
Stars: ✭ 37 (-83.33%)
Pytorch geometricGraph Neural Network Library for PyTorch
Stars: ✭ 13,359 (+5917.57%)
DCGCNDensely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)
Stars: ✭ 73 (-67.12%)
DglPython package built to ease deep learning on graph, on top of existing DL frameworks.
Stars: ✭ 8,652 (+3797.3%)
DGNImplementation of Directional Graph Networks in PyTorch and DGL
Stars: ✭ 71 (-68.02%)
Pro-GNNImplementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"
Stars: ✭ 202 (-9.01%)
DOM-Q-NETGraph-based Deep Q Network for Web Navigation
Stars: ✭ 30 (-86.49%)
awesome-efficient-gnnCode and resources on scalable and efficient Graph Neural Networks
Stars: ✭ 498 (+124.32%)
PathConCombining relational context and relational paths for knowledge graph completion
Stars: ✭ 29 (-86.94%)
GAugAAAI'21: Data Augmentation for Graph Neural Networks
Stars: ✭ 139 (-37.39%)
DIN-Group-Activity-Recognition-BenchmarkA new codebase for Group Activity Recognition. It contains codes for ICCV 2021 paper: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition and some other methods.
Stars: ✭ 26 (-88.29%)
GalaXCGalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification
Stars: ✭ 28 (-87.39%)
mtad-gat-pytorchPyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).
Stars: ✭ 85 (-61.71%)
PathConCombining relational context and relational paths for knowledge graph completion
Stars: ✭ 94 (-57.66%)
ntds 2019Material for the EPFL master course "A Network Tour of Data Science", edition 2019.
Stars: ✭ 62 (-72.07%)
SuperGAT[ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Stars: ✭ 122 (-45.05%)
SubGNNSubgraph Neural Networks (NeurIPS 2020)
Stars: ✭ 136 (-38.74%)
GNNLens2Visualization tool for Graph Neural Networks
Stars: ✭ 155 (-30.18%)