Assigned reading, CS 598-GA, Spring 2026
Long list
A subset of these will be assigned
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Acer, S., Azad, A., Boman, E.G., Buluc, A., Devine, K.D., Ferdous, S.M., Gawande, N., Ghosh, S., Halappanavar, M., Kalyanaraman, A. and Khan, A., 2021. EXAGRAPH: Graph and combinatorial methods for enabling exascale applications. The International Journal of High Performance Computing Applications, 35(6), pp.553-571.
- Aidara, N.K., Diop, I.M., Diallo, C. and Cherifi, H., 2024, July. A comprehensive Evaluation of Community Detection algorithms. In 2024 IEEE Workshop on Complexity in Engineering (COMPENG) (pp. 1-6). IEEE.
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Airoldi, E.M., Blei, D., Fienberg, S. and Xing, E., 2008. Mixed membership stochastic blockmodels. Advances in neural information processing systems, 21.
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Basu, S., Bera, S.K. and Seshadhri, C., 2024. Spectral triadic decompositions of real-world networks. SIAM Journal on Mathematics of Data Science, 6(3), pp.703-730.
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Basu, S., Paul-Pena, D., Qian, K., Seshadhri, C., Huang, E.W. and Subbian, K., 2024, October. Covering a graph with dense subgraph families, via triangle-rich sets. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 109-119).
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Bodenheimer, T., Halappanavar, M., Jefferys, S., Gibson, R., Liu, S.,
Mucha, P.J., Stanley, N., Parker, J.S., Selitsky, S.R.
FastPG: Fast clustering of millions of single cells.
Published in 2020 on ioRxiv.
(link)
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Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating singlecell transcriptomic data across different conditions, technologies, and species.
Nat Biotechnol 36, 411-420, doi:10.1038/nbt.4096 (2018).
(link)
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Epasto, A., Lattanzi, S. and Paes Leme, R., 2017, August. Ego-splitting framework: From non-overlapping to overlapping clusters. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 145-154).
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- Ferdous, S.M., Neff, R., Peng, B., Shuvo, S., Minutoli, M., Mukherjee, S., Kowalski, K., Becchi, M. and Halappanavar, M., 2024, May. Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing. In 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 241-252). IEEE.
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Fortunato, S. and Barthelemy, M., 2007. Resolution limit in community detection. Proceedings of the national academy of sciences, 104(1), pp.36-41.
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Fortunato, S. and Newman, M.E., 2022. 20 years of network community detection. Nature Physics, 18(8), pp.848-850.
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Fountoulakis, K., Gleich, D.F. and Mahoney, M.W., 2018. A short introduction to local graph clustering methods and software. arXiv preprint arXiv:1810.07324.
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Fountoulakis, K., Liu, M., Gleich, D.F. and Mahoney, M.W., 2023. Flow-based algorithms for improving clusters: A unifying framework, software, and performance. SIAM Review, 65(1), pp.59-143.
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Gou, X., Zheng, W., Wang, Y., Xu, X. and Yu, Z., 2025. A Comprehensive Survey and Experimental Study of Learning-based Community Search. Proceedings of the VLDB Endowment, 18(9), pp.2941-2954.
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Gupta, R., Roughgarden, T. and Seshadhri, C., 2014, January. Decompositions of triangle-dense graphs. In Proceedings of the 5th conference on Innovations in theoretical computer science (pp. 471-482).
- Harb, E., Yassin, Y., Chekuri, C.: Corporate needs you to find the difference: Revisiting submodular and supermodular ratio optimization problems (2025). ArXiv:2505.17443 URL
- Hofmeyr, S., Buluc, A., Riley, R., Egan, R., Selvitopi, O., Oliker, L., Yelick, K., Shakya, M., Youtsey, B. and Azad, A., 2024. Exabiome: Advancing Microbial Science through Exascale Computing. Computing in Science & Engineering, 26(2), pp.8-15.
- Hu, F., Liu, J., Li, L. and Liang, J., 2020. Community detection in complex networks using Node2vec with spectral clustering. Physica A: Statistical Mechanics and its Applications, 545, p.123633.
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- Hussain, M.T., Halappanavar, M., Chatterjee, S., Radicchi, F., Fortunato, S. and Azad, A., 2025. Parallel median consensus clustering in complex networks. Scientific Reports, 15(1), p.3788.
- Kannan R, Vempala S, Vetta A. On clusterings: Good, bad and spectral. Journal of the ACM (JACM). 2004;51(3):497–515.
- Kirkley, A. & Newman, M. Representative community divisions of networks. Commun. Phys. 5, 40 (2022).
- Kleinberg J. An impossibility theorem for clustering. Advances in Neural Information Processing Systems. 2002;15.
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Kojaku, S., Radicchi, F., Ahn, Y.Y. and Fortunato, S., 2024. Network community detection via neural embeddings. Nature Communications, 15(1), p.9446.
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- Lancichinetti, A. & Fortunato, S. Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012).
- Lechekhab, M., Pasadakis, D. and Schenk, O., 2024, September. Multilevel Diffusion Based Spectral Graph Clustering. In 2024 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-7). IEEE.
- Lee, C. and Wilkinson, D.J., 2019. A review of stochastic block models and extensions for graph clustering. Applied Network Science, 4(1), pp.1-50.
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Leskovec, J., Lang, K.J. and Mahoney, M., 2010, April. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World wide web (pp. 631-640).
- Li, Y., He, K., Kloster, K., Bindel, D. and Hopcroft, J., 2018. Local spectral clustering for overlapping community detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(2), pp.1-27.
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Liu, Q.C., Shun, J. and Zablotchi, I., 2024, March. Parallel k-Core Decomposition with Batched Updates and Asynchronous Reads. In Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (pp. 286-300).
- Newman, M.E., 2003. The structure and function of complex networks. SIAM review, 45(2), pp.167-256.
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Palla, G., Derenyi, I., Farkas, I. and Vicsek, T., 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), pp.814-818.
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- Pasadakis, D., Alappat, C.L., Schenk, O. and Wellein, G., 2022. Multiway p-spectral graph cuts on Grassmann manifolds. Machine learning, 111(2), pp.791-829.
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Peel, L., Larremore, D.B. and Clauset, A., 2017. The ground truth about metadata and community detection in networks. Science advances, 3(5), p.e1602548.
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Romano, S., Vinh, N.X., Bailey, J. and Verspoor, K., 2016. Adjusting for chance clustering comparison measures. Journal of Machine Learning Research, 17(134), pp.1-32.
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- Sattar, N.S., Lu, H., Wang, F. and Halappanavar, M., 2024, May. Distributed multi-gpu community detection on exascale computing platforms. In 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 815-824). IEEE.
- Strehl, A. and Ghosh, J., 2002. Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, 3(Dec), pp.583-617.
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Tandon, A., Albeshri, A., Thayananthan, V., Alhalabi, W., Radicchi, F. and Fortunato, S., 2021. Community detection in networks using graph embeddings. Physical Review E, 103(2), p.022316.
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Traag VA, Van Dooren P, Nesterov Y. Narrow scope for resolution-limit-free community detection. Physical Review E. 2011;84(1):016114. Pmid:21867264
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Traag, V.A., Waltman, L. and Van Eck, N.J., 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), pp.1-12.
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Vaca-Ramírez, F. and Peixoto, T.P., 2022. Systematic assessment of the quality of fit of the stochastic block model for empirical networks. Physical Review E, 105(5), p.054311.
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Vu-Le, T. A., Anne, L., Chacko, G., & Warnow, T. (2025). EC-SBM synthetic network generator. Applied Network Science, 10(1), 15.
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Vu-Le, T.A., Park, M., Chen, I., & Warnow, T. (2025). Using stochastic block models for community detection. Applied Network Science.
URL
- Von Luxburg U., Williamson RC, and Guyon I "Clustering: Science or art?." Proceedings of ICML workshop on unsupervised and transfer learning. JMLR Workshop and Conference Proceedings, 2012.
- Willson, J. and Warnow, T., 2024. Axioms for clustering simple unweighted graphs: No impossibility result. PLOS Complex Systems, 1(2), p.e0000011.
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Yang, J. and Leskovec, J., 2012, August. Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM SIGKDD workshop on mining data semantics (pp. 1-8).
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Ying, R., Fu, T., Wang, A., You, J., Wang, Y. and Leskovec, J., 2024. Representation learning for frequent subgraph mining. arXiv preprint arXiv:2402.14367.
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You, J., Ying, R., Ren, X., Hamilton, W. and Leskovec, J., 2018, July. Graphrnn: Generating realistic graphs with deep auto-regressive models. In International conference on machine learning (pp. 5708-5717). PMLR.
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Yu, S., Engels, J., Huang, Y. and Shun, J., 2025. Pecann: Parallel efficient clustering with graph-based approximate nearest neighbor search. In 2025 Proceedings of the Conference on Applied and Computational Discrete Algorithms (ACDA) (pp. 1-17). Society for Industrial and Applied Mathematics.
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Yu, S., Shi, J., Meindl, J., Eisenstat, D., Ju, X., Tavakkol, S.,
Dhulipala, L., Lacki, J., Mirrokni, V. and Shun, J., 2024. The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering. arXiv preprint arXiv:2411.10290.