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Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks formance on challenging benchmarks. To achieve this, they used largely synthetic graphs. Our contribution complements the existing ones by pro- viding a dataset and experimental results based on a new domain.

Graph Neural Network is a type of Neural Network which directly operates on the Graph structure. A typical application of GNN is node ...

A graph neural network (GNN) is a class of neural networks for processing data represented by graph data structures. ... They were popularized by their use in ...

Abstract: Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, ...

07/06/20 - We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes ...

( Discuss) Proposed since July 2021. A graph neural network (GNN) is a class of neural networks for processing data represented by graph data structures. They were popularized by their use in supervised learning on properties of various molecules.

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural ...

Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks. The dataset is constructed from Wikipedia categories, specifically 10 classes ...

(2017). Available from: http://en. wikipedia.org/wiki/Pantograph_(rail) 8. Zhu J, Liao S, Lei Z, Li SZ (2017) Multi-label convolutional neural network based ...

Design and create neural networks using deep learning and artificial intelligence ... the simple moving average (https://en.wikipedia.org/wiki/Moving_ ...

We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes ...

We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding ...

A common criticism of neural networks, particularly in robotics, is that they require a large diversity of training samples for real-world operation. This is not surprising, since any learning machine needs sufficient representative examples in order to capture the underlying structure that allows it to generalize to new cases. Dean Pomerleau, in his research presented in the paper "Knowledge-based Training of Artificial Neural Networks for Autonomous Robot Driving," uses a …