Graph neural networks for ranking Web pages

By Franco Scarselli, Sweah Liang Yong, Marco Gori, Markus Hagenbuchner, Ah Chung Tsoi, Marco Maggini. 2005

118 citations recorded by the source corpus.

An artificial neural network model, capable of processing general types of graph structured data, has recently been proposed. This paper applies the new model to the computation of customised page ranks problem in the World Wide Web. The class of customised page ranks that can be implemented in this way is very general and easy because the neural network model is learned by examples. Some preliminary experimental findings show that the model generalizes well over unseen Web pages, and hence, may be suitable for the task of page rank computation on a large Web graph.

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, background): “Models in the graph neural network family (Gori et al., 2005; Scarselli et al., 2005, 2009a; Li et al., 2016) have been explored in a diverse range of problem domains, across supervised, semi-supervised, unsupervised, and reinforcement learning settings.”
Graph networks as learnable physics engines for inference and control (2018, methodology): “Our core model implementation is based on the broader class of graph neural networks (GNNs) (Scarselli et al., 2005; 2009a;b; Bruna et al., 2013; Li et al., 2015; Henaff et al., 2015; Du-venaud et al., 2015; Dai et al., 2016; Defferrard et al., 2016; Niepert et al., 2016; Kipf & Welling, 2016;…”
The Graph Neural Network Model (2009, unknown): “More results achieved on a snapshot of the web are available in [79].”
The Pascal Visual Object Classes Challenge 2006 ( VOC 2006 ) Results (2006, methodology): “The local features we experimented with were Yan Ke's PCA-SIFT [2] and actual patches cut out of images at proper scales in Gaussian pyramids.”
A Comparison between Recursive Neural Networks and Graph Neural Networks (2006, background): “Recursive Neural Networks (RNNs) were originally proposed in [15], [16], [20].”
Neural networks for relational learning: an experimental comparison (2011, background): “More details can be found in Scarselli et al. (2009a).”
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology (2022, methodology): “Many such approaches utilize Graph Neural Networks (Gori et al., 2005; Scarselli et al., 2005), conditioning the policy on a graph representation of the agent’s morphology.”