Graph Neural Networks

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CellStrat > Research/Blog > Artificial Intelligence > Graph Neural Networks
21
Jul
Graph Convolutional Networks

Author : Gouthaman Asokan Submitted on : 5 Sep 2020 Abstract – Graphs have a specific format of data structure that makes it challenging to pass through the neural networks. We will be seeing a specified neural network called Graph Convolution Network, which acts as a first order approximation of Spectral Graph Convolutions. This network […]

Posted in: Graph Neural Networks, papers,
27
Aug
Working with Giant Graphs

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Recently, our AI Lab Researcher Gouthaman Asokan presented an excellent session on Giant Graphs, wherein we use specialized Graph Neural Networks to process and mine massive graphs – such as social networks or paper citation graphs. Examples of Real Life Giant Graphs Giant graphs has usually millions or billions of nodes […]

Posted in: Graph Neural Networks, Healthcare, Pharma, Publishing,
Tags: Bigraph, control variate sampling, giant graphs, graph neural networks, graph theory, GraphSage, knowledge graph, Knowledge Graphs, neighbor sampling, Node Flow, PubMed, Receptive Field, reddit dataset, SageNet, social network, social networks,
13
Aug
Generative Models for Graphs

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Recently, our AI Lab Researcher Gouthaman Asokan presented an amazing session on Generative Models for Graphs. This is part of our webinar series on Graphs and Graph Neural Networks. Graph is a nonlinear data structure consisting of nodes and edges. Widely used in applications like Physical system interactions, Knowledge graphs, Molecular interactions, Transportation […]

Posted in: Generative Modeling, Graph Neural Networks, Pharma,
Tags: bernoulli distribution, categorical distribution, deep generative model for graph, DGMG, generative model, generative modelling, GrammarVAE, graph embedding, graph grammar, graph neural network, graph propagation, molecular generation, Simplified Molecular Input Line Entry System, SMILES, synthentic graph, synthetic graph, teacher forcing,
23
Jun
Relational GCN for Heterogeneous Graphs

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Recently, our AI Lab Researcher Gouthaman Asokan presented an amazing session on Relational Graphical Convolutional Networks (Relational GCN). These are particularly useful for mining heterogeneous groups. Table of Contents :- GCN HOMOGENEOUS AND HETEROGENEOUS GRAPHS KEY IDEAS OF RELATIONAL GCN FINDING PAGERANK USING MESSAGE PASSING BUILDING A RELATIONAL GCN FOR THE […]

Posted in: Graph Neural Networks, Publishing, Retail,
Tags: ACM Citation, Entity classification, GCN, GNN, graph analysis, Graph Convolutional Network, graph network, graph neural networks, graph theory, heterogenous graphs, homogeneous graphs, knowledge graph, Link Prediction, Message Passing, ontology, ontology graph, PageRank, pytorch, R-GCN, recommender systems, relational GCN,
28
May
Introduction to Graph Neural Networks

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Recently, our AI Researcher Gouthaman Asokan presented a superb session on introduction to Graph Neural Networks (GNNs). Structured vs Unstructured Data :- Images and text are structured data. Convolutional Networks, Recurrent Neural Networks, Autoencoders work well on the structured data as they can be converted to the matrix or vector like format. […]

Posted in: Graph Neural Networks, Pharma,
Tags: DiGraphs, Drug Interactions, GCN, GNNs, Graph Convolutional Network, graph neural networks, GraphNet, Graphs, Knowledge Graphs, Multigraph, node classification, social network analysis, unstructured data,