SMILES converted into Graphs that contains atomic information, bonding informatics. Graphs considered as input for the NN to Predict Melting Pont of Liquid Crystals (LCs)
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Updated
Nov 17, 2022 - Python
SMILES converted into Graphs that contains atomic information, bonding informatics. Graphs considered as input for the NN to Predict Melting Pont of Liquid Crystals (LCs)
In this project I explore an potential approach to estimate a human’s intention in a dyadic collaborative manipulation task by learning to predict the intended future trajectory of the co-manipulated object via the latent graph representation of the system.
Implement GNN models (GAT, GCN)
Literature indexing using Graph Neural Networks and label-guided text embeddings.
Originally the Intersectional Graph Project, SURRF (The Semantic Ubiquitous Relations Research Framework) is an AI-assisted, graph-based research engine, built with the perplexity.ai API and NextJS. SURRF enables researchers to navigate the web while creating knowledge graphs that users can save, organize, compile, and share.
Official PyTorch Implementation of paper 'Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction'
This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric
This repository shows a use case of Graph ML for casinos in marketing: Market Segmentation . Skills: Azure SDK, azure datalake, node2vec, graphml, geometric pytorch, gnn
code & report files for Project of EE394V SPR 2021
The repository is a collection of Jupyter notebooks showcasing various projects related to graph neural networks (GNNs). Each notebook provides a detailed explanation of the project and its implementation, making it easy for users to understand and replicate the results.
with GUG, Let's explore the Graph Neural Network!
Awesome GNN Learning For beginners
An implementation from scratch of Graph Convolutional Networks (GCN) using Numpy
A collection of GNN projects
Listing the research works related to risk control based on GNN and its interpretability. 1. we can learn the application of GNN in risk control (including fraud detection). 2. For possible prediction, we can use the interpretability of GNN to explaine how can we get such results.
A Survey of Learning from Graphs with Heterophily
CFG based program similarity using Graph Neural Networks
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