Add to ORKGGraph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems. Inspired by the success of the transformer architecture, there has been an ever-growing body of work on attention variants of GNNs attempting to advance the state of the art in many of these problems. Incorporating "attention" into graph mining has been viewed as a way to overcome the noisiness, heterogenity and complexity associated with graph-structured data as well as to encode soft-inductive bias. It is hence crucial and advantageous to study these variants from a bird's-eye view to assess their strengths and weaknesses. We provide a sys...
When dealing with tabular data, models based on regression and decision trees are a popular choice d...
Since their introduction, graph attention networks achieved outstanding results in graph representat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...
This thesis summarizes the work I have done during my master's study at UCLA. We ranked 38th among a...
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structure...
Graph Neural Network(GNN)is a kind of powerful deep learning network to analyse graph information. T...
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract...
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured...
Recent years have brought progress in the graph machine learning space, with the unsupervised graph...
Interpretable graph learning is in need as many scientific applications depend on learning models to...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world ...
This thesis addresses and investigates the recent development of graph attention network (GAT) model...
Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable att...
When dealing with tabular data, models based on regression and decision trees are a popular choice d...
Since their introduction, graph attention networks achieved outstanding results in graph representat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...
This thesis summarizes the work I have done during my master's study at UCLA. We ranked 38th among a...
Graph neural networks (GNNs) have been widely used to learn vector representation of graph-structure...
Graph Neural Network(GNN)is a kind of powerful deep learning network to analyse graph information. T...
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract...
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured...
Recent years have brought progress in the graph machine learning space, with the unsupervised graph...
Interpretable graph learning is in need as many scientific applications depend on learning models to...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world ...
This thesis addresses and investigates the recent development of graph attention network (GAT) model...
Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable att...
When dealing with tabular data, models based on regression and decision trees are a popular choice d...
Since their introduction, graph attention networks achieved outstanding results in graph representat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...