Simone Romano

816 total citations
11 papers, 381 citations indexed

About

Simone Romano is a scholar working on Artificial Intelligence, Signal Processing and Statistical and Nonlinear Physics. According to data from OpenAlex, Simone Romano has authored 11 papers receiving a total of 381 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 6 papers in Signal Processing and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Simone Romano's work include Advanced Clustering Algorithms Research (5 papers), Complex Network Analysis Techniques (4 papers) and Data Management and Algorithms (3 papers). Simone Romano is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Complex Network Analysis Techniques (4 papers) and Data Management and Algorithms (3 papers). Simone Romano collaborates with scholars based in Australia, China and Canada. Simone Romano's co-authors include James Bailey, Nguyễn Xuân Vinh, Jeffrey Chan, Karin Verspoor, James C. Bezdek, Xingjun Ma, Yisen Wang, Sarah Erfani, Kotagiri Ramamohanarao and Jian Pei and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Fuzzy Systems and Machine Learning.

In The Last Decade

Simone Romano

11 papers receiving 367 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Simone Romano Australia	9	259	94	57	45	44	11	381
Yiyang Gu China	8	230 0.9×	86 0.9×	44 0.8×	28 0.6×	18 0.4×	15	341
Ayan Acharya United States	9	181 0.7×	87 0.9×	30 0.5×	33 0.7×	27 0.6×	20	359
Lucas Vendramin Brazil	4	248 1.0×	104 1.1×	46 0.8×	19 0.4×	77 1.8×	4	368
Yunzhang Zhu United States	10	174 0.7×	76 0.8×	25 0.4×	75 1.7×	29 0.7×	19	492
Shuai Xiao China	10	133 0.5×	45 0.5×	40 0.7×	57 1.3×	60 1.4×	13	397
Sutanay Choudhury United States	11	131 0.5×	72 0.8×	68 1.2×	24 0.5×	36 0.8×	40	294
Hongliang Fei United States	15	313 1.2×	160 1.7×	35 0.6×	41 0.9×	22 0.5×	43	446
Yanhua Chen China	11	146 0.6×	136 1.4×	35 0.6×	24 0.5×	27 0.6×	30	334
Guo-Xun Yuan Taiwan	7	282 1.1×	205 2.2×	14 0.2×	50 1.1×	38 0.9×	9	516
Inmar E. Givoni Canada	7	196 0.8×	121 1.3×	36 0.6×	31 0.7×	30 0.7×	13	330

Countries citing papers authored by Simone Romano

Since Specialization

Citations

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

Fields of papers citing papers by Simone Romano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simone Romano

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

All Works

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11 of 11 papers shown

Ma, Xingjun, et al.. (2020). Normalized Loss Functions for Deep Learning with Noisy Labels. arXiv (Cornell University). 1. 6543–6553. 59 indexed citations

Wang, Yisen, Simone Romano, Nguyễn Xuân Vinh, et al.. (2017). Unbiased Multivariate Correlation Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 13 indexed citations

Romano, Simone, Nguyễn Xuân Vinh, Karin Verspoor, & James Bailey. (2017). The randomized information coefficient: assessing dependencies in noisy data. Machine Learning. 107(3). 509–549. 8 indexed citations

Romano, Simone, Nguyễn Xuân Vinh, James Bailey, & Karin Verspoor. (2016). Adjusting for chance clustering comparison measures. Journal of Machine Learning Research. 17(1). 4635–4666. 47 indexed citations

Bezdek, James C., et al.. (2016). Extending Information-Theoretic Validity Indices for Fuzzy Clustering. IEEE Transactions on Fuzzy Systems. 25(4). 1013–1018. 33 indexed citations

Romano, Simone, et al.. (2016). Measuring dependency via intrinsic dimensionality. 1207–1212. 7 indexed citations

Bezdek, James C., et al.. (2016). Ground truth bias in external cluster validity indices. Pattern Recognition. 65. 58–70. 40 indexed citations

Vinh, Nguyễn Xuân, Jeffrey Chan, Simone Romano, et al.. (2016). Discovering outlying aspects in large datasets. Data Mining and Knowledge Discovery. 30(6). 1520–1555. 43 indexed citations

Romano, Simone, James Bailey, Nguyễn Xuân Vinh, & Karin Verspoor. (2014). Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance. International Conference on Machine Learning. 1143–1151. 42 indexed citations

10.

Vinh, Nguyễn Xuân, Jeffrey Chan, Simone Romano, & James Bailey. (2014). Effective global approaches for mutual information based feature selection. RMIT Research Repository (RMIT University Library). 512–521. 82 indexed citations

11.

Bezdek, James C., et al.. (2014). Generalized information theoretic cluster validity indices for soft clusterings. RMIT Research Repository (RMIT University Library). 24–31. 7 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.

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