Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) of the graph or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE to improve downstream tasks such as predicting the unknown labels accurately and efficiently? The knowledge of GNE is valuable for various tasks like node classification and targeted advertising. However, identifying and understanding GNE such as homophily, heterophily or their combination is challenging in real-world graphs due to limited availability of node labels and noisy edges. We propose NetEffect, a graph mining approach to address the above issues, enjoying the following properties: (i) Principled: a statistica...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node...
Network classification aims to group networks (or graphs) into distinct categories based on their st...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
Graph-based deep learning has been successful in various industrial settings and applications. Howev...
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks ...
Graph neural networks (GNNs) are specifically designed for dealing with graph data which have achiev...
Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance...
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that...
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important ...
The graph neural network (GNN) is a type of powerful deep learning model used to process graph data ...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node...
Network classification aims to group networks (or graphs) into distinct categories based on their st...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
Graph-based deep learning has been successful in various industrial settings and applications. Howev...
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks ...
Graph neural networks (GNNs) are specifically designed for dealing with graph data which have achiev...
Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance...
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that...
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important ...
The graph neural network (GNN) is a type of powerful deep learning model used to process graph data ...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node...
Network classification aims to group networks (or graphs) into distinct categories based on their st...
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