Santo Fortunato
Biographic Data
| ID | 1939937 |
|---|---|
| NAME | Santo Fortunato |
| GIVEN NAMES | Santo |
| FAMILY NAME | Fortunato |
| SIGNATURE | SANTO FORTUNATO |
| AFFILIATIONS | Indiana University Bloomington |
| ORCID | 0000-0002-9039-4730 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2006 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Representing the disciplinary structure of physics
Recent advances in machine learning offer new ways to represent and study scholarly works and the space of knowledge. Graph and text embeddings provide a convenient vector representation of scholarly works based on citations and text. Yet, it is unclear whether their representations are consistent or provide different views of the structure of science. Here, we compare graph and text embedding by testing their ability to capture the hierarchical …
What we should learn from pandemic publishing
Consistency pays off in science
The exponentially growing number of scientific papers stimulates a discussion on the interplay between quantity and quality in science. In particular, one may wonder which publication strategy may offer more chances of success: publishing lots of papers, producing a few hit papers, or something in between. Here we tackle this question by studying the scientific portfolios of Nobel Prize laureates. A comparative analysis of different citation-base…
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their bursty dynamics. We find empirical evidence of a correlation between the number of citati…
Science of science
The whys and wherefores of SciSci The science of science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field. In a Review, Fortunato et al. explain that the underlying rationale is that with a deeper understanding of the precursors of impactful science, it will be possible to deve…
Community detection in networks
Reputation and impact in academic careers
Significance Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation…
Community detection in graphs
Community detection algorithms
Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplif…
Statistical physics of social dynamics
Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social…
Benchmark graphs for testing community detection algorithms
Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e., the question of how good an algorithm is, with respect to others, is still open. Standard tests include the analysis of simple artificial graphs with a built-in community structure, that the algorithm has to recover. However, the special graphs ado…
Universality of citation distributions
We study the distributions of citations received by a single publication within several disciplines, spanning broad areas of science. We show that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator c f = c / c 0 is considered, where c 0 is the average number of citations per article for the discipline. In addition …
Resolution limit in community detection
Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a s…
Computer Simulations of Opinions and their Reactions to Extreme Events
No prominent works on this page.
Computer Simulations of Opinions and their Reactions to Extreme Events
Resolution limit in community detection
Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a s…
Benchmark graphs for testing community detection algorithms
Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e., the question of how good an algorithm is, with respect to others, is still open. Standard tests include the analysis of simple artificial graphs with a built-in community structure, that the algorithm has to recover. However, the special graphs ado…
Universality of citation distributions
We study the distributions of citations received by a single publication within several disciplines, spanning broad areas of science. We show that the probability that an article is cited c times has large variations between different disciplines, but all distributions are rescaled on a universal curve when the relative indicator c f = c / c 0 is considered, where c 0 is the average number of citations per article for the discipline. In addition …
Community detection algorithms
Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplif…
Statistical physics of social dynamics
Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social…
Community detection in graphs
Reputation and impact in academic careers
Significance Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation…
Community detection in networks
Science of science
The whys and wherefores of SciSci The science of science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field. In a Review, Fortunato et al. explain that the underlying rationale is that with a deeper understanding of the precursors of impactful science, it will be possible to deve…
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively. But while the citations accrued by authors are the sum of the citations of their papers, translating the dynamics of citation accumulation from the paper to the author level is not trivial. Here we conduct a systematic study of the evolution of author citations, and in particular their bursty dynamics. We find empirical evidence of a correlation between the number of citati…
Consistency pays off in science
The exponentially growing number of scientific papers stimulates a discussion on the interplay between quantity and quality in science. In particular, one may wonder which publication strategy may offer more chances of success: publishing lots of papers, producing a few hit papers, or something in between. Here we tackle this question by studying the scientific portfolios of Nobel Prize laureates. A comparative analysis of different citation-base…
What we should learn from pandemic publishing
Representing the disciplinary structure of physics
Recent advances in machine learning offer new ways to represent and study scholarly works and the space of knowledge. Graph and text embeddings provide a convenient vector representation of scholarly works based on citations and text. Yet, it is unclear whether their representations are consistent or provide different views of the structure of science. Here, we compare graph and text embedding by testing their ability to capture the hierarchical …
Computer Science (11 works) · Complex Network Analysis Techniques (8 works) · Opinion Dynamics and Social Influence (6 works) · Physics (6 works) · Data science (5 works) · Mathematics (5 works) · scientometrics and bibliometrics research (5 works) · Citation (4 works) · Sociology (4 works) · Theoretical Computer Science (4 works)