Mario Vega-Barbas

46 total papers · 526 total citations
28 papers, 317 citations indexed

About

Mario Vega-Barbas is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Mario Vega-Barbas has authored 28 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Networks and Communications, 9 papers in Information Systems and 9 papers in Artificial Intelligence. Recurrent topics in Mario Vega-Barbas's work include IoT and Edge/Fog Computing (8 papers), Context-Aware Activity Recognition Systems (8 papers) and Network Security and Intrusion Detection (8 papers). Mario Vega-Barbas is often cited by papers focused on IoT and Edge/Fog Computing (8 papers), Context-Aware Activity Recognition Systems (8 papers) and Network Security and Intrusion Detection (8 papers). Mario Vega-Barbas collaborates with scholars based in Spain and Sweden. Mario Vega-Barbas's co-authors include Víctor A. Villagrá, Mario Sanz, Manuel Álvarez-Campana, Fernando Seoane, Iván Pau, Julio Berrocal, Manuel Álvarez-Campana, Diego López–de–Ipiña, Francisco Flórez‐Revuelta and Diego Casado–Mansilla and has published in prestigious journals such as IEEE Access, Sensors and International Journal of Environmental Research and Public Health.

In The Last Decade

Mario Vega-Barbas

27 papers receiving 303 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Mario Vega-Barbas	185	125	81	66	36	28	317
Puja Padiya	202 1.1×	170 1.4×	98 1.2×	70 1.1×	15 0.4×	25	298
Hasan Tahir	165 0.9×	139 1.1×	100 1.2×	108 1.6×	57 1.6×	33	335
Dayanand Ambawade	161 0.9×	80 0.6×	47 0.6×	113 1.7×	61 1.7×	39	329
Carlos Rabadão	184 1.0×	93 0.7×	92 1.1×	77 1.2×	26 0.7×	30	289
Pierre Parrend	196 1.1×	157 1.3×	114 1.4×	126 1.9×	28 0.8×	42	363
Sibi Chakkaravarthy Sethuraman	179 1.0×	134 1.1×	137 1.7×	107 1.6×	67 1.9×	22	353
Meiyi Ma	93 0.5×	108 0.9×	33 0.4×	44 0.7×	59 1.6×	37	323
Moirangthem Marjit Singh	207 1.1×	127 1.0×	59 0.7×	21 0.3×	32 0.9×	26	300
Niels Alexander Nijdam	153 0.8×	90 0.7×	72 0.9×	115 1.7×	42 1.2×	33	324
Shridatt Sugrim	160 0.9×	102 0.8×	171 2.1×	169 2.6×	20 0.6×	18	352

Countries citing papers authored by Mario Vega-Barbas

Since Specialization

Citations

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

Fields of papers citing papers by Mario Vega-Barbas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Vega-Barbas. 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 Mario Vega-Barbas. The network helps show where Mario Vega-Barbas may publish in the future.

Co-authorship network of co-authors of Mario Vega-Barbas

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

All Works

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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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