Scientometrics and Network Science in the Chacko-Warnow Collaboration

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

INTRODUCTION

Professors Warnow and Chacko collaborate on the following problems:

Understanding the organization of scientific communities, and especially emerging trends in biomedical research
Developing novel clustering methods that enable discovery from large citation networks
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 Digital Science, Google, and the Grainger Foundation.

PEOPLE

George Chacko, Department of Computer Science, University of Illinois Urbana-Champaign
Tandy Warnow, Department of Computer Sciences, The University of Illinois Urbana-Champaign
Dmitriy D. Korobskiy, NTT DATA, Enterprise Architecture Sr. Specialist Advisor (HTML)
Baqiao Liu, UIUC, PhD student in Computer Science, the University of Illinois Urbana-Champaign

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GRADUATE COURSE

CS 598: Computational Scientometrics. This graduate course, taught in Fall 2022, 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)

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.

13	M. Park, Y. Tabatabaee, B. Liu, V. Kamath Pailodi, V. Ramavarapu, R. Ramachandran, D. Korobskiy, F. Ayres, G. Chacko, T. Warnow. Well-Connected Communities in Real-World Networks. Preprint available on arXiv.	(HTML)
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)

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SOFTWARE

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

2022: AOC (Assembling Overlapping Clusters). This software takes as input a network and a set of clusters for the network, and then adds nodes to the clusters if they meet a user-specified criterion that matches the clustering objective, so that the cluster quality does not decrease. For example, augmenting the IKC (iterative k-core) clustering is performed using the minimum core degree (MCD), while augmenting Leiden clustering is performed so that the Constant Potts Model score for the cluster does not decrease. Developer: Baqiao Liu. (paper) (github)
2021: IKC: Iterative K-Core clustering. This clustering method operates using an iterative technique that finds k-cores in a graph, removes then, and iterates. The first k-core that is removed has the largest minimum degree. Developer: Eleanor Wedell. (paper) (github)