Gabriele Monfardini

11.2k citations
8 papers · 6.4k indexed · 2 hit papers · h-index 7
Topics
Advanced Graph Neural Networks (6 papers)Neural Networks and Applications (4 papers)Graph Theory and Algorithms (4 papers)
Journals
SustainabilityMachine LearningIEEE Transactions on Neural Networks
Partner nations
ItalyBelgiumAustralia

In The Last Decade

Gabriele Monfardini

8 papers receiving 6.2k citations

Hit Papers

The Graph Neural Network Model20062026201220192008200610002.0k3.0k4.0k5.0k

Peers

Gabriele Monfardini
Comparison fields: 5 of 172
  • Artificial Intelligence 3.4k
  • Computer Vision and Pattern Recognition 1.6k
  • Information Systems 770
  • Statistical and Nonlinear Physics 707
  • Computer Networks and Communications 675
Replace Markus Hagenbuchner with:
Markus Hagenbuchner Australia
M. Gori Italy
Franco Scarselli Italy
Cheng Yang China
Zhengyan Zhang China
Shengding Hu China
Ah Chung Tsoi Australia
Sergei Vassilvitskii United States
Ganqu Cui China
Ramón Sangüesa Spain
Gabriele Monfardini relative to Markus Hagenbuchner Australia Markus Hagenbuchner's profile →
Citations per field
00.5×1.5×
Markus Hagenbuchner · 1×
Citations per year

Countries citing papers authored by Gabriele Monfardini

Since Specialization
Citations

This map shows the geographic impact of Gabriele Monfardini'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 Gabriele Monfardini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriele Monfardini more than expected).

Fields of papers citing papers by Gabriele Monfardini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gabriele Monfardini. 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 Gabriele Monfardini. The network helps show where Gabriele Monfardini may publish in the future.

Co-authorship network of co-authors of Gabriele Monfardini

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Monfardini. A scholar is included among the top collaborators of Gabriele Monfardini 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 Gabriele Monfardini. Gabriele Monfardini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
#WorkIndexed citations
1 33
2 19
3 133
4
The Graph Neural Network Modelbreakdown →
5118
5
Graph Neural Networks for Object Localization
6
6
Two connectionist models for graph processing: An experimental comparison on relational data
8
7 25
8
A new model for learning in graph domainsbreakdown →
1048

About Gabriele Monfardini

Gabriele Monfardini is a scholar working on Business and International Management, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 6.4k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (6 papers), Neural Networks and Applications (4 papers) and Graph Theory and Algorithms (4 papers). The work is most often cited by research in Artificial Intelligence (3.4k citations), Computer Vision and Pattern Recognition (1.6k citations) and Statistical and Nonlinear Physics (707 citations). Gabriele Monfardini has collaborated with scholars based in Italy, Belgium and Australia. Frequent co-authors include Franco Scarselli, M. Gori, Ah Chung Tsoi, Markus Hagenbuchner, Marco Gori, Marta Negri, Alessandra Neri, Enrico Cagno, Hendrik Blockeel and Marco Maggini. Their work appears in journals such as Sustainability, Machine Learning and IEEE Transactions on Neural Networks.

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.

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