Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The Graph Neural Network Model
20085.1k citationsFranco Scarselli, M. Gori et al.IEEE Transactions on Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Markus Hagenbuchner
Since
Specialization
Citations
This map shows the geographic impact of Markus Hagenbuchner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markus Hagenbuchner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Hagenbuchner more than expected).
Fields of papers citing papers by Markus Hagenbuchner
This network shows the impact of papers produced by Markus Hagenbuchner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markus Hagenbuchner. The network helps show where Markus Hagenbuchner may publish in the future.
Co-authorship network of co-authors of Markus Hagenbuchner
This figure shows the co-authorship network connecting the top 25 collaborators of Markus Hagenbuchner.
A scholar is included among the top collaborators of Markus Hagenbuchner based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Markus Hagenbuchner. Markus Hagenbuchner is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tjondronegoro, Dian, et al.. (2018). Deep learning for energy expenditure prediction in pre-school children. QUT ePrints (Queensland University of Technology).6 indexed citations
Tsoi, Ah Chung, et al.. (2013). Cost-Sensitive Cascade Graph Neural Networks. Research Online (University of Wollongong). 527.1 indexed citations
10.
Hagenbuchner, Markus, Giovanni Da San Martino, Ah Chung Tsoi, & Alessandro Sperduti. (2011). Sparsity issues in self-organizing-maps for structures. Research Online (University of Wollongong). 35–40.3 indexed citations
11.
Hagenbuchner, Markus, et al.. (2009). Projection of undirected and non-positional graphs using self organizing maps. Research Online (University of Wollongong).5 indexed citations
Hagenbuchner, Markus, Alessandro Sperduti, & Ah Chung Tsoi. (2008). Self-Organizing Maps for cyclic and unbounded graphs.. The European Symposium on Artificial Neural Networks. 203–208.2 indexed citations
14.
Scarselli, Franco, M. Gori, Ah Chung Tsoi, Markus Hagenbuchner, & Gabriele Monfardini. (2008). Computational Capabilities of Graph Neural Networks. IEEE Transactions on Neural Networks. 20(1). 81–102.133 indexed citations
15.
Aiolli, Fabio, Giovanni Da San Martino, Alessandro Sperduti, & Markus Hagenbuchner. (2007). "Kernelized" Self-Organizing Maps for Structured Data. Research Padua Archive (University of Padua). 19–24.2 indexed citations
16.
Pucci, Augusto, Marco Gori, Markus Hagenbuchner, Franco Scarselli, & Ah Chung Tsoi. (2006). Investigation into the application of graph neural networks to large-scale recommender systems. Systems Science. 32(4). 17–26.4 indexed citations
17.
Hagenbuchner, Markus, Alessandro Sperduti, & Ah Chung Tsoi. (2005). Contextual Processing of Graphs using Self-Organizing Maps.. Research Padua Archive (University of Padua). 399–404.10 indexed citations
18.
Tsoi, Ah Chung, et al.. (2003). A Simple Focused Crawler.. Use Siena air (University of Siena).9 indexed citations
Hagenbuchner, Markus & Ah Chung Tsoi. (1999). A benchmark for testing adaptive systems on structured data.. The European Symposium on Artificial Neural Networks. 63–68.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.