Funded by the US National Science Foundation.
Collaborative Research:
OAC Core Awards OAC:2402559 (7/5/2024) & OAC:2402560 (7/5/2024)
Investigators:
Tandy Warnow, University of Illinois Urbana-Champaign (Principal Investigator OAC-2402559)
David Bader, New Jersey Institute of Technology (Principal Investigator: OAC-2402560)
George Chacko, University of Illinois Urbana-Champaign (co-Principal Investigator OAC-2402559)
Key Personnel:
Minhyuk Park, University of Illinois Urbana-Champaign
Haotian Yi, University of Illinois Urbana-Champaign
Mohammad Dindoost, New Jersey Institute of Technology
Community detection methods enable an understanding of the structure of networks at multiple scales. While many methods exist, only a few are able to scale to large networks and/or are implemented in large computational infrastructure. As we have recently shown, even those that scale to large datasets, fail to reliably produce well-connected clusters. Finally, given that the choice of clustering method depends on both the network being analyzed and the question of interest, providing the domain specialist a choice of multiple clustering methodologies within a common framework for exploratory data analysis, is essential. This project will make substantial advances on these challenges through the coordinated development of advanced cyber-infrastructure, scalable to very large networks, that offers multiple options for community detection, search, and extraction. The infrastructure will be accessible across platforms ranging from laptops to multi-node clusters with distributed memory.
Journal publications supported by the grant
Park et al. (2026) Modeling the Global Citation Network using the Scalable Agent-based Simulator for Citation Analysis with Recency-emphasized Sampling (SASCA-ReS) (peer reviewed on MetaRoR)
Shewarega et al. (2026). MoMo - Combining Neuron Morphology and Connectivity for Interactive Motif Analysis in Connectomes. IEEE Transactions on Visualization and Computer Graphics
Chacko et al. (2025) An agent-based model of citation behavior. Applied Network Science.
Vu-Le et al. (2025). Using stochastic block models for community detection Applied Network Science
Bader et al. (2025). Rocket-crane algorithm for the Feeback Arc Set problem Social Network Analysis and Mining
Bader et al. (2025). Cover Edge-based Novel Triangle Counting. Algorithms
Park et al. (2024) Well-Connectedness and Community Detection PLOS Complex Systems
Conference papers supported by the grant
Park et al. (2025) Very Large Scale Simulations of Network Growth with the Scalable Agent-based Simulator for Citation Analysis with sampling (SASCA-s) (In Press: Proceedings of the 14th International Conference on Complex Networks and their Applications)
Nia et al. (2025) Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis 2025 IEEE High Performance Extreme Computing Conference (HPEC)Dindoost et al. (2025) On the Optimization of Methods for Establishing Well-Connected Communities (In Press: Proceedings of the 14th International Conference on Complex Networks and their Applications)
Dindoost et al. (2025). HiPerMotif: Novel Parallel Subgraph Isomorphism in Large-Scale Property Graphs. 2025 IEEE High Performance Extreme Computing Conference (HPEC)
Li et al. (2025). Designing Parallel Algorithms for Community Detection using Arachne . 2025 IEEE High Performance Extreme Computing Conference (HPEC)
Spaan et al. (2025). Wedge-Parallel Triangle Counting for GPUs. European Conference on Parallel Processing.
Preprints supported by the grant
Bader (2026). Linux and high-performance computing . arXiv preprint
Ramavarapu et al. (2025). Large Scale Community-Aware Network Generation . arXiv preprint
Papers under review supported by the grant
Minhyuk Park, Haotian Yi, Ian Chen, Tandy Warnow, and George Chacko. (2026). Modeling citations and cartels. Submitted for publication; preprint available at https://www.researchsquare.com/article/rs-9270520/v1
The-Anh Vu-Le, Minhyuk Park, Ian Chen, Joao Alfredo Cardoso Lamy, Tomas Alessi, Elfarouk Harb, George Chacko, and Tandy Warnow. Improving Community Detection with CVC, a New Cluster Ensemble Method. Invited submission to Applied Network Science, Special Issue of the 14th International Conference on Complex Networks and their Applications. Under review.
MS theses and PhD dissertations
Oliver Andres Alvarado Rodriguez (2026). PhD dissertation, NJIT. On the Design of a Framework for Large-Scale Exploratory Graph Analytics
Fuhuan Li (2026). PhD dissertation, NJIT. Large-scale graph algorithms and applications with an emphais on fintech data
Related Research (not supported by this award)
Anne et al. (2025) RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network Generation (Advances in Complex Systems)
Anne et al. (2025) Synthetic Networks That Preserve Edge Connectivity, Complex Networks and Their Applications 2024.
Park et al. (2025) Improved Community Detection using Stochastic Block Models. Complex Networks and Their Applications 2024.
Tabatabaee et al. (2025) FastEnsemble: A new scalable ensemble clustering method. Complex Networks and Their Applications 2024.
Tabatabaee et al. (2025) FastEnsemble: Scalable ensemble clustering on large networks. PLOS Complex Systems 2025
Educational materials
Spring 2026, UIUC, Course materials for CS 598: Graph Algorithms for Community Structure in Large Networks tandy.cs.illinois.edu/CS598-Warnow-Sp2026.html
Spring 2025, UIUC, Course materials for CS 598: Methods and Applications in Network Analysis sites.google.com/view/chackogroup/cs-598
Software
Most of our open-source codes can be found at this github site: github.com/illinois-or-research-analytics
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