This repository contains 130th position solution for GE attribution challenge hosted by ALT labs

The aim is to Analyse and predict the possible interactions between the various subreddits present and predict the type of interaction and detect the implicit communities between subreddits This will be through various Network analysis and statistical inferences and predictive model

Some useful GNN models

Graph Neural Network related books, papers and toolboxes

DiffWire: Inductive Graph Rewiring via the Lovász Bound. In Proceedings of the First Learning on Graphs Conference. 2022. Adrian Arnaiz-Rodriguez, Ahmed Begga, Francisco Escolano and Nuria Oliver.

The model identifies chemical components and genes named entities and extracts the relations of the chemical-gene pair jointly. It utilizes the BioBERT model in the named entity recognition and the graphs neural networks for the RE subtasks.

Official code repository for the papers "Anti-Symmetric DGN: a stable architecture for Deep Graph Networks" accepted at ICLR 2023; "Non-Dissipative Propagation by Anti-Symmetric Deep Graph Networks"; and "Non-Dissipative Propagation by Randomized Anti-Symmetric Deep Graph Networks"

[ECCV 2024]Temporary code for "Ad-HGformer: An Adaptive HyperGraph Transformer for Skeletal Action Recognition"

Source code for GraphPOPE (Graph Position-aware Preprocessed Embeddings), developed in cooperation with the University of Amsterdam and Socialdatabase Research.

Developed a specialized RandomWalk algorithm for bipartite graphs on an ingredient dataset to replace allergenic ingredients while maintaining the nutritional value of the dish, and clustered them into 10 main clusters with 6 of them being highly specific.

Build Graph Nets in Tensorflow

Implementation of Federated Learning using Graph Neural Networks

Repository of the paper "On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks" published in ACM CIKM 2023

E(3)-equivariant graph neural networks (e3nn) for Julia

My Part II dissertation looking into extracting concepts from simplified graph neural networks

A Graph neural network is a class of artificial neural networks for processing data that can be represented as graphs. In the more general subject of "Geometric Deep Learning," existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs.

Improving Heterogeneus Graph Transformer archiecture exploiting structural information of graph

This Project Repository Contains the Coding Implementations Related to Graph Based Machine Learning & Deep Learning

DeepWalk - Deep Learning for Graphs

Codes, data, and baselines for CIKM 2023 Long Paper "Dual Intents Graph Modeling for User-centric Group Discovery"