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May 14, 2005 · ABSTRACT. Recent developments in the area of neural networks provided new models which are capable of processing general types of graph.
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 ...
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 ...
Jun 14, 2020 · A new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information ...
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This algorithm, which we call Adaptive PageRank, speeds up the computation of PageRank by nearly 30%. 1. Introduction. One of the best-known algorithms in web ...
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Nov 30, 2023 · In this paper, we propose an adaptive generalized PageRank graph neural network (AGP-GNN) for traffic flow forecasting, which jointly models spatial, temporal, ...
It is demonstrated experimentally on a relatively large web data set, viz., the WT10G, that it is possible to modify the PageRanks of the web pages through ...
We propose an enhanced adjustable method that attends to important hops via independent learnable weights and includes an initial connection method.
We propose a novel decoupled GNN model called Dual Adaptive PageRank Graph Neural Network with Structural Augmentation (DAPRGNN) in this work.
In this paper we formulate a general class of neural net- work based filters, where each node is a morphological/rank operation.