About

George Chacko is a Research Associate Professor at The Grainger College of Engineering at the University of Illinois Urbana-Champaign. His scientific interests are centered around community finding techniques that assist in understanding the structure of research communities formed around scientific questions. His work also explores topics related to novelty in science, knowledge diffusion, and peer review, with a focus on method development and discovery in research analytics. Over the last four years, he has shifted from a pronounced scientometric focus to a more nuanced approach emphasizing scalable methods and evaluation, explicitly avoiding commonplace global metrics such as the h-index. Chacko's research involves clustering, community detection, and community search with applications to science mapping, research evaluation, and scientometrics. He is responsible for research analytics at the Grainger College of Engineering and is willing to offer courses related to his expertise for PhD or MS students at UIUC. His work aims to develop and critically interpret methods for understanding research communities, with a strong emphasis on developing students' verbal and written communication skills through collaborative research.

Research topics

• Computer Science
• Artificial Intelligence
• Library science
• Information Retrieval
• Chemistry
• Data science
• Algorithm

Selected publications

• Modeling the Global Citation Network using the Scalable Agent-based Simulator for Citation Analysis with Recency-emphasized Sampling (SASCA-ReS)

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-21

  preprintOpen accessSenior author

  Revised preprint of a manuscript developed by Park, M; Yi, H; Warnow, T; and Chacko, G concerning very large scale simulations of the growth of citation networks using an agent based modeling approach. A document containing supplemental material accompanies the manuscript. The manuscript contains a link to a public Github repository where the code generated for this project is available. Data generated during this project is being made available via the Illinois Data Bank under repository IDB-9265079.

  PublisherDOI

• Well-Connected Community Detection at Extreme Scale: Shared- and Distributed-Memory Parallel Algorithms

  Research Square · 2026-03-13

  preprintOpen access
  PublisherOA PDFDOI

• Dense Subgraph Clustering and a New Cluster Ensemble Method

  Studies in computational intelligence · 2026-01-01

  book-chapterOpen access
  PublisherOA PDFDOI

• Very Large Scale Simulations of Network Growth with the Scalable Agent-Based Simulator for Citation Analysis with Sampling (SASCA-s)

  Studies in computational intelligence · 2026-01-01

  book-chapterSenior authorCorresponding
  PublisherDOI

• On the Optimization of Methods for Establishing Well-Connected Communities

  Studies in computational intelligence · 2026-01-01

  book-chapterOpen access
  PublisherOA PDFDOI

• Synthetic Networks That Preserve Edge Connectivity

  Studies in computational intelligence · 2025-01-01

  book-chapterSenior authorCorresponding
  PublisherDOI

• RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation

  ArXiv.org · 2025-02-04

  preprintOpen accessSenior author

  The limited availability of useful ground-truth communities in real-world networks presents a challenge to evaluating and selecting a "best" community detection method for a given network or family of networks. The use of synthetic networks with planted ground-truths is one way to address this challenge. While several synthetic network generators can be used for this purpose, Stochastic Block Models (SBMs), when provided input parameters from real-world networks and clusterings, are well suited to producing networks that retain the properties of the network they are intended to model. We report, however, that SBMs can produce disconnected ground truth clusters; even under conditions where the input clusters are connected. In this study, we describe the REalistic Cluster Connectivity Simulator (RECCS), which, while retaining approximately the same quality for other network and cluster parameters, creates an SBM synthetic network and then modifies it to ensure an improved fit to cluster connectivity. We report results using parameters obtained from clustered real-world networks ranging up to 13.9 million nodes in size, and demonstrate an improvement over the unmodified use of SBMs for network generation.

  PublisherOA PDFDOI

• EC-SBM Synthetic Network Generator

  ArXiv.org · 2025-02-05

  preprintOpen access

  Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our extensive performance study on large real-world networks shows that EC-SBM has high accuracy in both network and community-specific criteria, and is generally more accurate than current alternative approaches for this problem. Furthermore, EC-SBM is fast enough to scale to real-world networks with millions of nodes.

  PublisherOA PDFDOI

• EC-SBM synthetic network generator

  Applied Network Science · 2025-05-01 · 2 citations

  articleOpen access

  Abstract Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our performance study on large real-world networks shows that EC-SBM is generally more accurate with respect to network and community criteria than currently used approaches for this problem. Furthermore, we demonstrate that EC-SBM can complete analyses on several real-world networks with millions of nodes.

  PublisherOA PDFDOI

• Improved Community Detection using Stochastic Block Models

  ArXiv.org · 2025-02-02

  preprintOpen access

  Identifying edge-dense communities that are also well-connected is an important aspect of understanding community structure. Prior work has shown that community detection methods can produce poorly connected communities, and some can even produce internally disconnected communities. In this study we evaluate the connectivity of communities obtained using Stochastic Block Models. We find that SBMs produce internally disconnected communities from real-world networks. We present a simple technique, Well-Connected Clusters (WCC), which repeatedly removes small edge cuts until the communities meet a user-specified threshold for well-connectivity. Our study using a large collection of synthetic networks based on clustered real-world networks shows that using WCC as a post-processing tool with SBM community detection typically improves clustering accuracy. WCC is fast enough to use on networks with millions of nodes and is freely available in open source form.

  PublisherOA PDFDOI

Frequent coauthors

• Mei‐Ching Chen

  Fu Jen Catholic University

  20 shared

• Dmitriy Korobskiy

  16 shared

• Tandy Warnow

  University of Illinois Urbana-Champaign

  15 shared

• Kevin W. Boyack

  11 shared

• Vikram Ramavarapu

  8 shared

• Minhyuk Park

  University of Illinois Urbana-Champaign

  8 shared

• Sitaram Devarakonda

  NETE (United States)

  6 shared

• Mark D. Lindner

  Center for Scientific Review

  6 shared

Labs

• The Grainger College of EngineeringPI

Education

• PhD, Ohio State Biochemistry Program

  The Ohio State University

  1995

• Master of Veterinary Science (MVSc), Veterinary Pathology

  University of Agricultural Sciences

  1988

• Bachelor of Veterinary Science (BVSc), Veterinary College

  University of Agricultural Sciences

  1986

Awards & honors

• Alumni Award for Distinguished Service