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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
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.
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.
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.
This work addresses the need for deeper insights into the representations learned by GNNs by introducing a novel 1-WL+NN framework for which we prove the equivalence with GNNs and are able to study the representations learned by GNNs.
This repository is the implementation of the paper Semi-Supervised Classification With Graph Convolutional Networks (aka GCN) by Kipf et al., ICLR 2017.
This repository contains the official implementation of the paper titled Multimodal weighted graph representation for information extraction from visually rich documents.