Scientometrics and Network Science in the Chacko-Warnow Collaboration

[INTRODUCTION] [PEOPLE] [PUBLICATIONS] [DATASETS] [SOFTWARE]

INTRODUCTION

Professors Warnow and Chacko collaborate on the following problems:
  1. Understanding the organization of scientific communities, and especially emerging trends in biomedical research
  2. Developing novel clustering methods that enable discovery from large citation networks
  3. Developing new methods for understanding community structure in large networks (millions of nodes), including the detection of overlapping communities
We gratefully acknowledge the support of the US National Science Foundation (2402559), Insper-Illinois Partnership, Digital Science, Google, and the Grainger Foundation.

PEOPLE

Directors Senior Collaborators Graduate Students Undergraduate students at UIUC Undergraduate students from Insper
[Back to Top]

GRADUATE COURSE

CS 598: Computational Scientometrics. This graduate course, taught in Fall 2022 and Spring 2025, is centered around applying quantitative analytical techniques to problems in scientometrics that concern research metadata, particularly citations. The course consists of presentations, critical discussions, and research projects. Participating students will explore scientific questions, analyze data, develop new methods or apply existing ones (HTML)

CS 598:Graph Algorithms for Community Structure Detection in Large Networks, Spring 2026. This is a course on applied algorithms, focusing on the use of discrete mathematics, graph theory, probability theory, statistics, machine learning, and simulations, to design and analyze algorithms for community detection, community search, and community extraction in large graphs (e.g., social networks, biological networks, and citation graphs) with millions of nodes, with the goal of making important breakthroughs in either theory or development of improved scalable methods. We will examine these questions from both a theoretical perspective (e.g., computational complexity and design of algorithms for hard optimization problems, resolution limit) as well as from a data-driven perspective.

JOURNAL AND REFEREED CONFERENCE PUBLICATIONS

Copyright Notice: The documents accessible through these links are included by the author as a means to ensure convenient electronic dissemination of technical work on a non-commercial basis. Copyright and all rights therein are maintained by the copyright holders (the authors or the publishers), notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's and publisher's copyright. In particular, these works may not be re-posted without permission of the copyright holders.


23 M. Park, J.A.C. Lamy, E.C.C. Rodrigues, F. Ferreira T-A Vu-Le, T. Warnow, and G. Chacko. Very Large Scale Simulations of Network Growth with the Scalable Agent-based Simulator for Citation Analysis with sampling (SASCA-s) To appear, Proc. Complex Networks and Their Applications, 2025.
22 T-A Vu-Le, J.A. Cardoso Lamy, T. Alessi, I. Chen, M. Park, E. Harb, and T. Warnow, 2025. Dense Subgraph Clustering and a New Cluster Ensemble Method. To appear, Proc. Complex Networks and Their Applications, 2025.
21 T-A Vu-Le* M. Park*, I. Chen, and T. Warnow, 2025 (*: equal contributions). Using Stochastic Block Models for Community Detection. To appear, Applied Network Science
20 M. Park, D.W. Feng, S. Digra, T.A. Vu-Le, L. Anne, G. Chacko, and T. Warnow, 2025. Improved Community Detection using Stochastic Block Models. Proceedings of Complex Networks and their Applications 2024 (HTML)
19 Y. Tabatabaee, E. Wedell, M. Park, and T. Warnow. FastEnsemble: Scalable ensemble clustring on large networks. PLOS Complex Systems, 2025. This is an extended version of a paper that appears in the Proc. Complex Networks and Their Applications, 2024. (HTML)
18 L. Anne, T-A Vu-Le, M. Park, T. Warnow, and G. Chacko. (2024). RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation. Advances in Complex Systems, volume 28, number 5. This is an extended version of a paper that appears in the Proceedings of Complex Networks and their Applications 2024 (HTML)
17 T-A Vu-Le, L. Anne, G. Chacko, and T. Warnow (2025). EC-SBM Synthetic Network Generator. Applied Network Science, vol 10., no. 15. (HTML)
16 J. Willson and T. Warnow (2024). Axioms for Clustering Simple Unweighted Graphs: no impossibility result. PLOS Complex Systems, 1(2): e0000011. https://doi.org/10.1371/journal.pcsy.0000011 (HTML)
15 M. Park et al. (2024). Well-Connectedness and Community Detection (note: this is an extension of the CNA 2023 paper 13.) PLOS Complex Systems 1(3): e0000009. https://doi.org/ 10.1371/journal.pcsy.0000009 (HTML)
14 V. Ramavarapu, F. J. Ayres, M. Park, V.A.K. Pailodi, J.A. C. Lamy, T. Warnow, and G. Chacko (2024). CM++ - A Meta-method for Well-Connected Community Detection. Journal of Open Source Science. DOI: 10.21105/joss.06073 (PDF)
13 M. Park, Y. Tabatabaee, V. Ramavarapu, B. Liu, V. Kamath Pailodi, R. Ramachandran, D. Korobskiy, F. Ayres, G. Chacko, T. Warnow (2024). Well-connectedness and community detection. PLOS Complex Systems, 1(3), e0000009. This is an extended version of a paper that appeared in COMPLEX NETWORKS and their applications 2023. accepted to PLOS Complex Systems.) (HTMO)
12 A. Jakatdar, B. Liu, T. Warnow, and G. Chacko (2022). AOC: Assembling Overlapping Communities. Quantitative Science Studies (QSS), published by MIT Press. (HTML)
11 E. Wedell, M. Park, D. Korobskiy, T. Warnow, and G. Chacko (2022) Center-Periphery Structure in Communities: Extracellular Vesicles. Quantitative Science Studies 3(1):289-314. Also appears in arXiv 2111.07418 (HTML)
10 W. Zhao, D. Korobskiy, and G. Chacko (2021). Delayed Recognition: A Co-Citation Perspective. Front. Res. Metr. Anal., 19 February 2021 | https://doi.org/10.3389/frma.2020.577131 (HTML)
9 J. Bradley, S. Devarakonda, A. Davey, D. Korobskiy, S. Liu, D. Lakhdar-Hamina,T. Warnow, and G. Chacko (2020). Co-citations in context: disciplinary heterogeneity is relevant. Quantitative Science Sciences (MIT Press), 1(1), 264--276, https://doi.org/10.1162/qss_a_00007. (PDF)
8 S. Chandrasekharan, M. Zaka, S. Gallo, W. Zhao, D. Korobskiy, T. Warnow, & G. Chacko (2020). Finding Scientific Communities in Citation Graphs. Quantitative Science Studies (MIT Press), 2(1), Dec 3, 2020. (HTML)
7 S. Devarakonda, D. Korobskiy, T. Warnow, and G. Chacko (2020). Viewing computer science through citation analysis: Salton and Bergmark Redux. Scientometrics (2020). https://doi.org/10.1007/s11192-020-03624-0. (HTML)
6 S. Devarakonda, J. Bradley, D. Korobskiy, T. Warnow, and G. Chacko (2020). Frequently Co-cited Publications: Features and Kinetics, Quantitative Science Studies (MIT Press), 1(1), 264-276, doi:10.1162/qss_a_00075 (HTML)
5 W Zhao, D Korobskiy, S Chandrasekharan, KM Merz Jr, G Chacko (2020). Converging Interests: Chemoinformatics, History, and Bibliometrics Journal of Chemical Information and Modeling 60 (12), 5870-5872 (HTML)
4 L Bornmann, S Devarakonda, A Tekles, G Chacko (2020). Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers. Quantitative Science Studies 1 (3), 1242-1259 (HTML)
3 L Bornmann, S Devarakonda, A Tekles, G Chacko (2020). Disruptive papers published in Scientometrics: meaningful results by using an improved variant of the disruption index originally proposed by Wu, Wang, and Evans (2019) Scientometrics, 1-7 (HTML)
2 S Keserci, E Livingston, L Wan, AR Pico, G Chacko (2017). Research synergy and drug development: Bright stars in neighboring constellations. Heliyon 3 (11), e00442 (HTML)
1 KW Boyack, MC Chen, G Chacko (2014). Characterization of the Peer Review Network at the Center for Scientific Review, National Institutes of Health. PLOS One 9(8): e104244. (HTML)

[Back to Top]

PREPRINTS

SOFTWARE

All software produced in this collaboration is available in open-source form.

COMMUNITY DETECTION

AGENT BASED MODELS

SYNTHETIC NETWORK GENERATORS