Pasquale De Meo

4.2k citations
95 papers · 2.0k indexed · 1 hit paper · h-index 26
Topics
Complex Network Analysis Techniques (34 papers)Advanced Graph Neural Networks (13 papers)Opinion Dynamics and Social Influence (12 papers)
Journals
SHILAP Revista de lepidopterologíaPLoS ONEExpert Systems with Applications
Partner nations
ItalyChinaUnited Kingdom

In The Last Decade

Pasquale De Meo

90 papers receiving 1.9k citations

Hit Papers

Web data extraction, applications and techniques: A survey2014202620182022201450100150200

Peers

Pasquale De Meo
Comparison fields: 5 of 132
  • Artificial Intelligence 755
  • Statistical and Nonlinear Physics 658
  • Information Systems 652
  • Computer Networks and Communications 439
  • Sociology and Political Science 354
Replace Przemysław Kazienko with:
Przemysław Kazienko Poland
Xiaolong Jin China
Duen Horng Chau United States
Antonio Picariello Italy
Danai Koutra United States
Derek Greene Ireland
Sen Wu China
Rahul Katarya India
Yun Liu China
Sang‐Wook Kim South Korea
Pasquale De Meo relative to Przemysław Kazienko Poland Przemysław Kazienko's profile →
Citations per field
00.5×1.5×2.4×
Przemysław Kazienko · 1×
Citations per year

Countries citing papers authored by Pasquale De Meo

Since Specialization
Citations

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

Fields of papers citing papers by Pasquale De Meo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pasquale De Meo. 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 Pasquale De Meo. The network helps show where Pasquale De Meo may publish in the future.

Co-authorship network of co-authors of Pasquale De Meo

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 0
3 3
4 6
5 21
6 2
7 0
8 24
9 3
10 5
11 50
12 1
13 32
14 3
15 101
16 26
17 26
18 28
19 23
20 3

About Pasquale De Meo

Pasquale De Meo is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Information Systems, having authored 95 papers that have together received 2.0k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (34 papers), Advanced Graph Neural Networks (13 papers) and Opinion Dynamics and Social Influence (12 papers). The work is most often cited by research in Statistical and Nonlinear Physics (658 citations), Information Systems (652 citations) and Artificial Intelligence (755 citations). Pasquale De Meo has collaborated with scholars based in Italy, China and United Kingdom. Frequent co-authors include Giacomo Fiumara, Emilio Ferrara, Domenico Ursino, Giovanni Quattrone, Domenico Rosaci, Giuseppe M. L. Sarnè, Robert Baumgartner, Alessandro Provetti, Salvatore Catanese and Giorgio Terracina. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

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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