
...to the computation of customised page ranks problem in the world wide web. the class of customised page ranks that can be implemented in...model generalizes well over unseen web pages, and hence, may be suitable for the task of page rank computation on a large web graph....

...paper proposes an alternative attack graph ranking
scheme based on a recent approach to machine learning in a structured graph domain, namely, graph neural networks (gnns).
evidence is presented in this paper that the gnn is suitable for the task of ranking attack graphs by learning a ranking function
from examples and generalizes...

...learning method such as the graph neural network (gnn) is able to learn and estimate google's page ranking algorithm. this paper shows that...suitable to learn any arbitrary web page ranking scheme, and hence, may be more flexible than any other existing web page ranking scheme. the significance of this...

...to the task of detecting web spam, a combination of the best of its breed algorithms for processing
graph domain input data, namely, probability mapping graph self organizing maps and graph neural networks. the two connectionist
models are...

...the net work represents a web page, and the connections between neurons represent the hyperlinks between web pages. web content analy sis and web link analysis are also incorporated...also be useful in other web applications such as web page clustering and search result ranking....

...developments in the area of neural networks provided new models which are capable of processing general types of graph structures. neural networks are wellknown for their generalization capabilities. this paper explores the idea of applying a novel neural network model to a web graph to compute an adaptive ranking of pages. some early experimental results indicate...

...paper we propose a method
for information retrieval and web page ranking by analyzing hyperlink structure on the web graph and the weight of keywords...is proposed and evaluated to rank web pages. in the evaluation, we take into consideration both the importance and
relevance of a page.
...

...from sample random recurrent cortical networks
and corresponding simulations. inspired by the pagerank and the hubs&authorities algorithms for networked data, we introduce the neuronrank...to each neuron in the network. source and sink values are used as
structural features for predicting the activity dynamics of biological neural networks. our results show that neuronrank...
Published in 2007.

...the more recent developments in web graph processing using the classic
google page rank equation as popularized by brins and page [1], and its modifications, to handle page rank and personalized
page rank determinations. it is shown that...

...recursive neural networks (rnns) and graph neural networks (gnns) are two connectionist models that can directly process graphs. rnns and gnns exploit a...instead of a supervised one. graph neural networks have been recently proposed to process very general types of graphs and can be considered an...