M
Markus Hagenbuchner
Researcher at University of Wollongong
Publications - 84
Citations - 7366
Markus Hagenbuchner is an academic researcher from University of Wollongong. The author has contributed to research in topics: Self-organizing map & Artificial neural network. The author has an hindex of 20, co-authored 78 publications receiving 4479 citations. Previous affiliations of Markus Hagenbuchner include Information Technology University & Queensland University of Technology.
Papers
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Journal ArticleDOI
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
Journal ArticleDOI
Computational Capabilities of Graph Neural Networks
TL;DR: The functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision are described, and includes most of the practically useful functions on graphs.
Journal ArticleDOI
A self-organizing map for adaptive processing of structured data
TL;DR: This work proposes the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled directed acyclic graphs (DAGs) by using the unfolding procedure adopted in recurrent and recursive neural networks.
Proceedings ArticleDOI
Graph Neural Networks for Ranking Web Pages
Franco Scarselli,Sweah Liang Yong,Marco Gori,Markus Hagenbuchner,Ah Chung Tsoi,Marco Maggini +5 more
TL;DR: Some preliminary experimental findings show that the new artificial neural network model generalizes well over unseen Web pages, and hence, may be suitable for the task of page rank computation on a large Web graph.
Journal ArticleDOI
Breast cancer data analysis for survivability studies and prediction
TL;DR: A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient.