The official source code for “GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment”, accepted at SIGIR 2022(Short Paper). Overview Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand, recent self-supervised learning paradigm aims to […]

Description Open-source python library for dotbim file format. Read more about dotbim here: https://github.com/paireks/dotbim Installation How it works? For json serialization and deserialization it uses jsonpickle: https://github.com/jsonpickle/jsonpicklejsonpickle license: https://github.com/jsonpickle/jsonpickle/blob/main/LICENSE Examples Pyramid example

Implementation of Heterogeneous Graph Attention Network This is the code repository of paper “Prediction of Metro Ridership During the COVID-19 Pandemic: A Deep Learning Approach Based on Heterogeneous Graph” which is submitted to Transportation Rsearch Part C: Emgerging Technologies. Datasets New York City COVID case number New York City Metro Ridership Number Model Framework How to Run? cd Experiments/ python main.py –epoch 200 –seed 1230 –log_file=’./logs/running_log.log’ Grid Search:

Source code for the paper “A Generalization of Transformer Networks to Graphs” by Vijay Prakash Dwivedi and Xavier Bresson, at AAAI’21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI’21). We propose a generalization of transformer neural network architecture for arbitrary graphs: Graph Transformer. Compared to the Standard Transformer, the highlights of the presented architecture are: The attention mechanism is a function of neighborhood connectivity for each node in the graph. The position encoding is represented by Laplacian eigenvectors, […]

This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Create a virtual python environment using Anaconda: conda create -n inhand python=3.7 conda activate inhand Install the inhandpy python package. From the base directory execute: cd inhandpy/ pip install -e . In inhandpy, download datasets, pre-trained models and other local resources by running: ./download_local_files.sh Stage 1: Tactile images to 3D point clouds To run the example:

This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is built on the source code from keyphrase-generation-rl and fastNLP. Thanks for their work. If you use this code, please cite our paper: @inproceedings{ye2021heterogeneous, title={Heterogeneous Graph Neural Networks for Keyphrase Generation}, author={Ye, Jiacheng and Cai, Ruijian and Gui, Tao and Zhang, Qi}, booktitle={Proceedings of EMNLP}, year={2021} } Dependency python 3.5+ pytorch 1.0+ dgl 0.4.3 sentence_transformers 1.1.0 faiss 1.6.3

GitHub – fuhaitao95/MVGCN: MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. – GitHub – fuhaitao95/MVGCN: MVGCN: a novel multi-view graph convoluti…    

This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph Convolution (AdaptConv) is a point cloud convolution operator presented in our ICCV2021 paper. If you find our work useful in your research, please cite our paper. Installation Classification classification.md Part Segmentation Indoor Segmentation coming soon GitHub https://github.com/hrzhou2/AdaptConv-master    

GNAS-MP Pytorch Implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021) Getting Started 0. Prerequisites Linux NVIDIA GPU + CUDA CuDNN 1. Setup Python Environment # clone Github repo conda install git git clone https://github.com/phython96/GNAS-MP.git cd GNAS-MP # Install python environment conda env create -f environment_gpu.yml conda activate gnasmp 2. Download datasets The datasets are provided by project benchmarking-gnns, you can click here to download all the required datasets. 3. Search Architectures python scripts/search_molecules_zinc.sh [gpu_id] 4. Train & Test […]

ts2vg The Python ts2vg package provides high-performance algorithm implementations to build visibility graphs from time series data. The visibility graphs and some of their properties (e.g. degree distributions) are computed quickly and efficiently, even for time series with millions of observations thanks to the use of NumPy and a custom C backend (via Cython) developed for the visibility algorithms. The visibility graphs are provided according to the mathematical definitions described in: Lucas Lacasa et al., “From time series to complex […]