how powerful are graph neural network

Description

https://arxiv.org/abs/1810.00826

[1810.00826] How Powerful are Graph Neural Networks?
https://arxiv.org/abs/1810.00826
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes.

https://deepai.org/publication/how-powerful-are-graph-neural-networks

How Powerful are Graph Neural Networks? | DeepAI
https://deepai.org/publication/how-powerful-are-graph-neural-networks
We develop a simple neural architecture, Graph Isomorphism Network (GIN), and show that its discriminative/representational power is equal to the power of the WL test. We validate our theory via experiments on graph classification datasets, where the expressive power of GNNs is crucial to capture graph structures.

https://github.com/weihua916/powerful-gnns

How Powerful are Graph Neural Networks? – GitHub
https://github.com/weihua916/powerful-gnns
How Powerful are Graph Neural Networks? This repository is the official PyTorch implementation of the experiments in the following paper: Keyulu Xu*, Weihua Hu*, Jure Leskovec, Stefanie Jegelka.

https://asail.gitbook.io/hogwarts/graph/how_powerful

How Powerful are Graph Neural Networks? – Notes
https://asail.gitbook.io/hogwarts/graph/how_powerful
GNNs are at most as powerful as the WL test in distinguishing graph structures. Established conditions on the neighbor aggregation and graph pooling functions under which the resulting GNN is as powerful as the WL test.

https://cs.stanford.edu/people/jure/pubs/gin-iclr19.pdf

PDF H Powerful Are Graph Neural Networks
https://cs.stanford.edu/people/jure/pubs/gin-iclr19.pdf
G that helps predict the label of an entire graph, y G = g(h G). Graph Neural Networks. GNNs use the graph structure and node features X v to learn a representa-tion vector of a node, h v, or the entire graph, h G. Modern GNNs follow a neighborhood aggregation strategy, where we iteratively update the representation of a node by aggregating …

https://www.arxiv-vanity.com/papers/1810.00826/

How Powerful are Graph Neural Networks? – arXiv Vanity
https://www.arxiv-vanity.com/papers/1810.00826/
If a graph neural network A: G → Rd following the neighborhood aggregation scheme maps G1 and G2 to different embeddings, the Weisfeiler-Lehman graph isomorphism test also decides G1 and G2 are not isomorphic. Hence, any aggregation-based GNN is at most as powerful as the WL test in distinguishing different graphs.

https://paperswithcode.com/paper/how-powerful-are-graph-neural-networks

How Powerful are Graph Neural Networks? | Papers With Code
https://paperswithcode.com/paper/how-powerful-are-graph-neural-networks
Upload an image to customize your repository’s social media preview. Images should be at least 640×320px (1280×640px for best display).

https://openreview.net/forum?id=ryGs6iA5Km

How Powerful are Graph Neural Networks? | OpenReview
https://openreview.net/forum?id=ryGs6iA5Km
Abstract: Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes.

https://www.section.io/engineering-education/an-introduction-to-graph-neural-network/

An Introduction to Graph Neural Networks | Section
https://www.section.io/engineering-education/an-introduction-to-graph-neural-network/
What is a Graph? Graphs are powerful data structures that model a set of objects and their relationships. These objects represent the nodes and the relationships represent edges. … The earliest studies of Graph Neural Networks fall under this model. These neural networks aim to learn node representations using Recurrent Neural Networks (RNNs …

https://proceedings.neurips.cc/paper/2020/file/a32d7eeaae19821fd9ce317f3ce952a7-Paper.pdf

PDF Building powerful and equivariant graph neural networks …
https://proceedings.neurips.cc/paper/2020/file/a32d7eeaae19821fd9ce317f3ce952a7-Paper.pdf
type of graph neural network that is strictly more powerful than MPNNs, while also sharing the attractive inductive bias of message-passing architectures. SMP inherits its power from its ability to manipulate node identifiers. However, in contrast to previous studies that relied on identifiers [18, 19],