Assigned reading, CS 598-GA, Spring 2026

Long list

A subset of these will be assigned

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.
Airoldi, E.M., Blei, D., Fienberg, S. and Xing, E., 2008. Mixed membership stochastic blockmodels. Advances in neural information processing systems, 21. (link)
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.
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).
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)
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)
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). (link)
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.
Fortunato, S. and Barthelemy, M., 2007. Resolution limit in community detection. Proceedings of the national academy of sciences, 104(1), pp.36-41.
Fortunato, S. and Newman, M.E., 2022. 20 years of network community detection. Nature Physics, 18(8), pp.848-850.
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. (link)
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. (link)
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. ML
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. ML
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.
Kojaku, S., Radicchi, F., Ahn, Y.Y. and Fortunato, S., 2024. Network community detection via neural embeddings. Nature Communications, 15(1), p.9446. (link)
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.
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.
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.
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. (link)
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.
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. (link)
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. (link)
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.
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. (link)
Traag VA, Van Dooren P, Nesterov Y. Narrow scope for resolution-limit-free community detection. Physical Review E. 2011;84(1):016114. Pmid:21867264
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.
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. (link)
Vu-Le, T. A., Anne, L., Chacko, G., & Warnow, T. (2025). EC-SBM synthetic network generator. Applied Network Science, 10(1), 15.
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.
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).
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. ML
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. ML
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.
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.