AdaGPR
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AdaGPR is a powerful, novel approach to graph convolution that uses adaptive generalized Pageranks to improve performance. It can be…
Automated Graph Learning
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AutoGL, also known as Automated Graph Learning, is a machine learning method that aims to automate the process of discovering…
Temporal Graph Network
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What is TGN?
Temporal Graph Network, or TGN for short, is a type of framework used in deep learning on…
MinCut Pooling
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MinCutPool Overview
If you're interested in computer science, you might have heard of MinCutPool. It's a fancy way of saying…
Instruction Pointer Attention Graph Neural Network
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In simple terms, the Instruction Pointer Attention Graph Neural Network (IPA-GNN) is a type of artificial intelligence that is designed…
BiGG
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BiGG is a new method for generative modeling of sparse graphs. It can create graphs quickly and efficiently through its…
Neural network for graphs
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Neural networks have been around for a while now and are used in many different areas. One area where neural…
Dual Graph Convolutional Networks
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A dual graph convolutional neural network (DualGCN) is a type of artificial intelligence algorithm that is used to help analyze…
Graph Network-based Simulators
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GNS, or Graph Network-Based Simulators, is an innovative approach to modeling complex physical systems. By using a graph neural network…
Symbolic Deep Learning
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Symbolic Deep Learning: An Overview
Symbolic deep learning is a technique that involves converting a neural network into an analytic…
Pseudoinverse Graph Convolutional Network
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PinvGCN: A Graph Convolutional Network for Dense Graphs and Hypergraphs
If you're interested in machine learning and artificial intelligence, you've…
Graph Contrastive Coding
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Graph Contrastive Coding (GCC) is a self-supervised pre-training framework for graph neural networks. Its goal is to capture the universal…
Hierarchical Entity Graph Convolutional Network
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Overview of HEGCN
HEGCN, also known as Hierarchical Entity Graph Convolutional Network, is a machine learning model used for multi-hop…
Spectral Gap Rewiring Layer
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GAP-Layer is a graph neural network layer that helps to optimize the spectral gap of a graph by minimizing or…
Graph Echo State Network
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The Graph Echo State Network, or GraphESN, is a type of neural network that is designed to work with graphs.…
Heterogeneous Molecular Graph Neural Network
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Graph neural networks (GNN) have become very useful in predicting the quantum mechanical properties of molecules as they can model…
Graph Neural Networks with Continual Learning
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Introduction to GNNCL: Solving the Problem of Fake News on Social Media
In today's world, social media has become a…
Group-Aware Neural Network
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What is GAGNN?
GAGNN, or Group-aware Graph Neural Network, is a powerful model for nationwide city air quality forecasting. It…
Hi-LANDER
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What is Hi-LANDER?
Hi-LANDER is a machine learning model that uses a hierarchical graph neural network (GNN) to cluster a…
TaxoExpan
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Overview of TaxoExpan
TaxoExpan is a unique self-supervised taxonomy expansion framework that is designed to automatically generate pairs of query…
Multiplex Molecular Graph Neural Network
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Multiplex Molecular Graph Neural Network (MXMNet): An Overview
The use of artificial intelligence (AI) in drug discovery is becoming increasingly…
Crystal Graph Neural Network
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In the world of computer science, there is a lot of talk about CGNN, or Convolutional Graph Neural Networks. CGNN…
NeuroTactic
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Overview of NeuroTactic: An Innovative Model for Theorem Proving
If you are interested in mathematics or computer science, you may…
GCNII
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Understanding Graph Convolutional Neural Networks with GCNII
If you are interested in Deep Learning and Neural Networks, you have probably…
Graph Attention Network v2
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Graph Attention Networks (GATs) are a type of neural network used for processing graph data, which is data with complex…
Graph Transformer
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Graph Transformer: A Generalization of Transformer Neural Network Architectures for Arbitrary Graphs
The Graph Transformer is a method proposed as…
LightGCN
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LightGCN is a type of neural network that is used for making recommendations in collaborative filtering. This is a process…
Commute Times Layer
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Overview of CT-Layer: A Differentiable and Learnable Rewiring Layer
CT-Layer is a graph neural network layer that is able to…
MeshGraphNet
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Introduction to MeshGraphNet
MeshGraphNet is a framework that helps machines learn about a new form of simulations to produce accurate…
Principal Neighbourhood Aggregation
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Principal Neighborhood Aggregation (PNA) is a powerful and versatile architecture for graphs that combines multiple aggregators with degree-scalers. This architecture…
Point-GNN
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What is Point-GNN?
Point-GNN, or Point-based Graph Neural Network, is a technology that can detect objects in a LiDAR point…
Cluster-GCN
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Cluster-GCN is an algorithm developed to make graph convolutional networks (GCN) more efficient and effective. It does so by exploiting…
Recurrent Event Network
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The Future of Predictive Analysis: RE-NET
In the world of predictive analysis, Recurrent Event Network, or RE-NET, is gaining popularity…
Graph Convolutional Networks for Fake News Detection
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Overview of GCNFN
Social media has become a major news source for millions of people around the world due to…
Deep Graph Convolutional Neural Network
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DGCNN: An Overview of a Revolutionary Neural Network Model
DGCNN is a cutting-edge neural network model specifically designed for graph…
ARMA GNN
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Introduction to ARMA
ARMA is a term that is often used in the field of signal processing and machine learning.…
PGC-DGCNN
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Introduction to PGC-DGCNN
PGC-DGCNN is a new development in the field of graph convolutional filters that seeks to improve the…
Graph Isomorphism Network
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Gin has become the latest trend in the world of data science and artificial intelligence. It is an acronym for…
Deep Graph Infomax
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Deep Graph Infomax (DGI) is a new approach for learning about nodes within graphs, which are structures where different things…
Stochastic Steady-state Embedding
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What is SSE?
SSE, which stands for Stochastic Steady-state Embedding, is an algorithm used to learn steady-state algorithms on graphs.…
Learnable graph convolutional layer
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Are you curious about what Learnable Graph Convolutional Layer (LGCL) is? You've come to the right place! In this article,…
DiffPool
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What is DiffPool?
DiffPool is a novel pooling module used to create hierarchical representations of graphs using deep graph neural…
ChebNet
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Have you ever heard of ChebNet? ChebNet, short for Chebyshev Neural Networks, is an innovative approach to designing convolutional neural…
CayleyNet
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CayleyNet is a cutting-edge technology that uses a new type of math called parametric rational complex functions, also known as…
Message Passing Neural Network
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Message Passing Neural Networks, commonly abbreviated as MPNN, is a type of neural network framework that is used for machine…
Adaptive Graph Convolutional Neural Networks
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Adaptive Graph Convolutional Neural Networks (AGCN) is a revolutionary algorithm that utilizes spectral graph convolution networks to process and analyze…
FastGCN
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FastGCN: A Faster Way to Learn Graph Embeddings
FastGCN is a recent improvement to the GCN model proposed by Kipf…
Graph Attention Network
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Graph Attention Network (GAT): A Revolutionary Neural Network Architecture
Artificial Intelligence (AI) works on a simple mechanism of feeding data…
GeniePath
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GeniePath is a new approach to machine learning that focuses on processing complex and massive data sets known as permutation…
StoGCN
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StoGCN is an algorithm used in machine learning to help with optimizing data. Specifically, this algorithm is designed to help…