Layer-stacked Attention for Heterogeneous Graph Embedding
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Updated
Jun 17, 2021 - Python
Layer-stacked Attention for Heterogeneous Graph Embedding
Implementation for "Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation"
Implementation of Relational Graph Attention operator for heterogeneous graphs in PyTorch
Developed an API which leverages graph-based cross domain ranking , community detection to make recommendations from a heterogeneous graph ecosystems.
Relational Deep Learning and Explainability of Graph Neural Network
A Deep Graph Library Custom HeteroGraph implementation of the LFM1b dataset
An explainable inductive learning model on gene regulatory and toxicogenomic knowledge graph (under development...)
[ICASSP 2023] HeMI: Global and Nodal Mutual Information Maximization in Heterogeneous Graphs
Code for paper "PGRA: Projected Graph Relation-Feature Attention Network for Heterogeneous Information Network Embedding", Information Sciences. 2021.
Transfer Learning within a Heterogeneous Graph
CIKM'23 paper: Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks
✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
Implementation for "CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation"
This repo is for source code of Expert Systems with Applications paper "SR-HGN: Semantic-and Relation-Aware Heterogeneous Graph Neural Network".
Course project of SJTU CS3319: Foundations of Data Science, 2023 spring
[EMNLP 2021] Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”
[Recsys'2023] "RCL: Multi-Relational Contrastive Learning for Recommendation"
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs (PatternRecognit, 2023)
This repo is for source code of SDM 2023 paper "Heterogeneous Graph Contrastive Multi-view Learning".
SI-HDGNN: Heterogeneous Dynamical Academic Network for Scientific Impact Propagation Learning
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