Srijan Sengupta
Biographic Data
| ID | 4163951 |
|---|---|
| NAME | Srijan Sengupta |
| GIVEN NAMES | Srijan |
| FAMILY NAME | Sengupta |
| SIGNATURE | SENGUPTA S |
| AFFILIATIONS | North Carolina State University |
| ORCID | 0000-0001-6889-8599 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A label-switching algorithm for fast core-periphery identification
Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While…
A generalized hypothesis test for community structure in networks
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in term…
Statistical evaluation of spectral methods for anomaly detection in static networks
The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to da…
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Statistical evaluation of spectral methods for anomaly detection in static networks
The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to da…
A generalized hypothesis test for community structure in networks
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in term…
A label-switching algorithm for fast core-periphery identification
Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While…
Complex Network Analysis Techniques (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Mathematics (2 works) · Advanced Graph Neural Networks (1 work) · Anomaly (physics (1 work) · Anomaly detection (1 work) · Anomaly Detection Techniques and Applications (1 work) · Baseline (sea (1 work)