The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Performance of modularity maximization in practical contexts
Open accessAbstract
Although widely used in practice, the behavior and accuracy of the popular module identification technique called modularity maximization is not well understood in practical contexts. Here, we present a broad characterization of its performance in such situations. First, we revisit and clarify the resolution limit phenomenon for modularity maximization. Second, we show that the modularity function Q exhibits extreme degeneracies: it typically admits an exponential number of distinct high-scoring solutions and typically lacks a clear global maximum. Third, we derive the limiting behavior of the maximum modularity Qmax for one model of infinitely modular networks, showing that it depends strongly both on the size of the network and on the number of modules it contains. Finally, using three real-world metabolic networks as examples, we show that the degenerate solutions can fundamentally disagree on many, but not all, partition properties such as the composition of the largest modules and the distribution of module sizes. These results imply that the output of any modularity maximization procedure should be interpreted cautiously in scientific contexts. They also explain why many heuristics are often successful at finding high-scoring partitions in practice and why different heuristics can disagree on the modular structure of the same network. We conclude by discussing avenues for mitigating some of these behaviors, such as combining information from many degenerate solutions or using generative models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“One recent study has shown that, even in relatively small networks, a large number of good solutions exist for the standard modularity criterion [51], [52].”
From Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model (2011) · cites this paper as [52]
In the citing paper
“However, as of today, a common method for dealing with this degeneracy is to examine statistics averaged over the 100 repetitions of the optimization, with equal weight being placed on each solution, rather than just on the unique solutions.”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [51]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1979) · Cited by 43,841
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Numerical recipes in C: the art of scientific computing
(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Research works printed but not linked: 4
- M. A. Porter, 2009, Not. Am. Math. Soc., 59, 1082
- L. K. Branting, Proceedings of the 2nd Workshop on Social Network Mining and Analysis
- E. M. Airoldi, 2008, J. Mach. Learn. Res., 9, 1981
- P. Demartines, 1995, Proceedings of 15th workshop GRETSI. Juan-Les-Pins France
Cited by
Sorted by citations
Items 701–800 of 936
Intra-community link formation and modularity in ultracold growing hyperbolic networks
Balogh, S. G., & Palla, G. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129784 · Cited by 4
Bayesian regionalization of urban mobility networks
Morel-Balbi, S., & Kirkley, A. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033307 · Cited by 3
Petrican, R., Chopra, S., Segal, A., Fallon, N., & Fornito, A. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00352-9 · Cited by 3
Kumari, A., Kumar, A., & Kumar, P. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00791-2 · Cited by 3
Efficiency-modularity for finding communities and anticommunities in networks
Bales, K. N., Eager, Z. D., & Harkin, A. A. (2016) · Journal of Complex Networks · doi:10.1093/comnet/cnw012 · Cited by 3
Fast Super-Paramagnetic Clustering
Yelibi, L., & Gebbie, T. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.124049 · Cited by 3
Influence Diffusion, Community Detection, and Link Prediction in Social Network Analysis
Fan, L., Wu, W., Lu, Z., Xu, W., & Du, D. (2013) · Springer proceedings in mathematics & statistics · doi:10.1007/978-1-4614-7582-8_11 · Cited by 3
A spectral algorithm of community identification
Gong, X., Li, K., Li, M., & Lai, C.-H. (2013) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/101/48001 · Cited by 3
A novel criterion for overlapping communities detection and clustering improvement
Berti, A., Sperduti, A., & Burattin, A. (2014) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2014.7008675 · Cited by 3
Detecting community structure using biased random merging
Liu, X., Forrest, J. Y., Luo, Q., & Yi, D. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.09.028 · Cited by 3
Improving Node Similarity for Discovering Community Structure in Complex Networks
Phạm, P. N. H., Nguyen, H. P. N., & Snåšel, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42345-6_7 · Cited by 3
Modeling Over-Dispersion for Network Data Clustering
Wang, L., Zhu, D., Dong, M., & Li, Y. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-180 · Cited by 3
Detecting communities by suspecting the maximum degree nodes
Chen, M., Zhang, M., Li, M., Leng, M., Yang, Z., & Wen, X. (2019) · International Journal of Modern Physics B · doi:10.1142/s0217979219501339 · Cited by 3
Criminal Group Dynamics and Network Methods
Ouellet, M., & Hashimi, S. (2019) · Methods of Criminology and Criminal Justice Research · doi:10.1108/s1521-613620190000024006
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0018 · Cited by 3
Considerations about multistep community detectionPreprint
Bisconti, C., Corallo, A., Fortunato, L., & Gentile, A. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.6508 · Cited by 3
Sampling and inference in complex networks
Berger-Wolf, T. Y., & Maiya, A. S. (2011) · Figshare · Cited by 3
Lee, J., Zhang, Z., Lee, J., Brooks, B. R., & Ahn, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.05100 · Cited by 3
TSCDA: A novel greedy approach for community discovery in networksPreprint
Ferdowsi, A., Khanteymoori, A., & Chenary, M. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.08.463718 · Cited by 3
Detectability constraints on meso-scale structure in complex networks
Arthur, R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317670 · Cited by 2
Measuring group fairness in community detection
de Vink, E., Takes, F. W., & Saxena, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0336212 · Cited by 2
Networks: The Visual Language of Complexity
Vidiella, B., Duran‐Nebreda, S., & Valverde, S. (2025) · SEMA SIMAI Springer series · doi:10.1007/978-3-031-99044-1_6 · Cited by 2
Toumi, L., Slimani, Y., & Ugur, A. (2025) · IEEE Access · doi:10.1109/access.2025.3650063 · Cited by 2
A Characterization of the Modular Structure of Complex Networks Based on Consensual Communities
Keller, I., & Viennet, E. (2012) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2012.109 · Cited by 2
An improvement of PLSA-based community detection algorithm
Zhang, Z., Feng, X., & Huo, W. (2014) · Modern Physics Letters B · doi:10.1142/s0217984914501206 · Cited by 2
Kishore, R., Swayamjyoti, S., Nussinov, Z., & Sahu, K. K. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/872/1/012017 · Cited by 2
Pathak, A., Menon, S. N., & Sitabhra Sinha, A. (2022) · Physical review. E · doi:10.1103/physreve.106.054304 · Cited by 5
A bi-directional approach to comparing the modular structure of networks
Straulino, D., Landman, M., & O’Clery, N. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00269-8 · Cited by 2
Improved brain community structure detection by two-step weighted modularity maximization
Guo, Z., Zhao, X., Yao, L., & Long, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295428 · Cited by 2
Network topology mapping of chemical compounds space
Tsekenis, G., Cimini, G., Kalafatis, M., Giacometti, A., Gili, T., & Caldarelli, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-54594-9 · Cited by 4
An efficient quadratic penalty method for a class of graph clustering problems
Teng, W., & Li, Q. (2025) · Optimization and Engineering · doi:10.1007/s11081-025-10042-9 · Cited by 2
Melo, C., Grand, B. L., & Aufaure, M.-A. (2013) · Lecture notes in business information processing · doi:10.1007/978-3-642-36285-9_30 · Cited by 2
Falcó-Roget, J., Jurek, K., Wojtarowicz, B., Capała, K., & Rycerz, K. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3668505 · Cited by 3
Novel Parallel Anytime A* for Graph and Network Clustering
Mendes, R., de Santiago, R., & Lamb, L. C. (2018) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477840 · Cited by 2
Capobianco, E. (2013) · Systems Biomedicine · doi:10.4161/sysb.25607 · Cited by 2
Efficient Parallel Community Detection in Large Edge-Intensive Networks
Gao, G., Bu, Z., Wu, Z., Li, Y., & Cao, J. (2014) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd.2014.52 · Cited by 2
Detectability Threshold of the Spectral Method for Graph Partitioning
Kawamoto, T., & Kabashima, Y. (2015) · Springer proceedings in complexity · doi:10.1007/978-3-319-20591-5_12 · Cited by 2
Ronhovde, P. (2010) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7gq6vvn · Cited by 2
Predicting Users Behaviours in Distributed Social Networks Using Community Analysis
Ngonmang, B., Viennet, E., & Tchuenté, M. (2014) · Lecture notes in social networks · doi:10.1007/978-3-319-05912-9_6 · Cited by 2
Community detection with the weighted parsimony criterion
Bettinelli, A., Hansen, P., & Liberti, L. (2015) · Journal of Systems Science and Complexity · doi:10.1007/s11424-015-2169-6 · Cited by 2
Optimizing a Hierarchical Community Structure of a Complex Network
Queyroi, F. (2013) · Studies in computational intelligence · doi:10.1007/978-3-319-02999-3_1 · Cited by 2
FAST ACCURATE COMMUNITY DETECTION BASED ON DYNAMICS PROCESS AND STABILITY OPTIMIZATION
ZHAO, Y., MA, J.-L., & Li, H. (2012) · International Journal of Modern Physics B · doi:10.1142/s0217979212501895 · Cited by 2
Brown, C., Ahmed, I., Cai, D., Poole, M. S., Pilny, A. N., & Atouba, Y. C. (2012) · Extreme Science and Engineering Discovery Environment · doi:10.1145/2335755.2335817 · Cited by 2
Efficient Community Detection in Large Scale Networks
Vieira, V. D. F., Xavier, C. R., & Evsukoff, A. G. (2013) · BRICS Congress on Computational Intelligence and Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC) · doi:10.1109/brics-cci-cbic.2013.117 · Cited by 2
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0013 · Cited by 2
On the role of degree influence in suboptimal modularity maximization
de Santiago, R., & Lamb, L. C. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7744379 · Cited by 2
Modeling Implicit Communities in Recommender Systems
Xiao, L., & Gu, Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68786-5_31 · Cited by 2
Proteins across scales through graph partitioning: application to the major peanut allergen Ara h 1
Zhang, H., Salazar, J. D., & Yaliraki, S. N. (2017) · Journal of Complex Networks · doi:10.1093/comnet/cnx052 · Cited by 2
Contextual Polarity and Influence Mining in Online Social Networks
Alzahrani, H., Duverger, P., & Nguyen, N. P. (2017) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2017.172 · Cited by 2
DAOC: Stable Clustering of Large Networks
Lutov, A., Khayati, M., & Cudré-Mauroux, P. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005464 · Cited by 2
A Consensus Clustering Method for Clustering Social Networks
Kheirkhahzadeh, M., & Analoui, M. (2020) · Statistics Optimization & Information Computing · doi:10.19139/soic-2310-5070-716 · Cited by 2
Finding Best Matching Community for Common Nodes in Mobile Social Networks
Tulu, M. M., Hou, R., Gerezgiher, S. A., Younas, T., & Amentie, M. D. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07508-7 · Cited by 2
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0011 · Cited by 3
Discovering Entities Similarities in Biological Networks Using a Hybrid Immune Algorithm
Scollo, R. A., Spampinato, A. G., Fargetta, G., Cutello, V., & Pavone, M. F. (2023) · Informatics · doi:10.3390/informatics10010018 · Cited by 2
Efficient enumeration of the optimal solutions to the correlation clustering problem
Arınık, N., Figueiredo, R., & Labatut, V. (2023) · Journal of Global Optimization · doi:10.1007/s10898-023-01270-3 · Cited by 2
Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection
Sobolevsky, S. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36027-5_51 · Cited by 2
Regional policy coordination of pandemic responses using an iterative mobility-driven algorithm
Baghersad, M., Gumus, G., Huang, C., & Behara, R. S. (2023) · Regional Studies · doi:10.1080/00343404.2023.2220749 · Cited by 2
A Multimodal Optimization and Surprise Based Consensus Community Detection Algorithm
Jia, G., He, S., Zhu, Z., Liu, J., & Tang, K. (2015) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2739482.2764656 · Cited by 2
Moments of Uniform Random Multigraphs with Fixed Degree Sequences
Chodrow, P. S. (2020) · SIAM Journal on Mathematics of Data Science · doi:10.1137/19m1288772 · Cited by 2
A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees
Cohen-Addad, V., Mallmann-Trenn, F., & Saulpic, D. (2022) · ACM Symposium on Principles of Distributed Computing (PODC) · doi:10.1145/3519270.3538449 · Cited by 2
Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis
Hu, H. (2015) · eScholarship (California Digital Library) · Cited by 2
Using social network graph analysis for interest detectionPreprint
Rowe, B. L. Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0316 · Cited by 2
Applications of reformulations in mathematical programming
Costa, A. (2012) · 4OR · doi:10.1007/s10288-012-0220-1 · Cited by 2
Community Detection in Imperfect Networks
Dahlin, J. (2011) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 2
A Scientometric Map of Global Educational ResearchPreprint
Lund, K., Jeong, H., Grauwin, S., & Pablo Jensen, B. (2015) · HAL · Cited by 2
Fukushima, M., Betzel, R. F., He, Y., Zuo, X., & Sporns, O. (2015) · arXiv (Cornell University) · Cited by 2
Ensemble-Based Discovery of Disjoint, Overlapping and Fuzzy Community Structures in NetworksPreprint
Chakraborty, T., & Park, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.02370 · Cited by 2
The Structure of Complex Networks Across Domains
Ikehara, K. (2016) · CU Scholar (University of Colorado Boulder) · Cited by 2
Diffusion in Networks With Overlapping Community StructurePreprint
Reid, F., & Hurley, N. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1105.5849 · Cited by 2
Bad Communities with High ModularityPreprint
Kehagias, A., & Pitsoulis, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.2678 · Cited by 2
constclust: Consistent Clusters for scRNA-seqPreprint
Virshup, I., Choi, J., Cao, K. L., & Wells, C. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.08.417105 · Cited by 2
Protein Organization with Manifold Exploration and Spectral ClusteringPreprint
Dubourg-Felonneau, G., Shams, S., Akiva, E., & Lee, L. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.08.471858 · Cited by 2
Efficient Crawling of Community Structures in Online Social Networks
Kester, S. V. (2011) · Research Repository (Delft University of Technology) · Cited by 2
Decelle, A. (2011) · theses.fr (ABES) · doi:10.70675/ce1da542z1b84z4418z8c13z831148c71330 · Cited by 2
Exact Signed Modularity Density Maximization Solutions and Their Real Meaning
de Santiago, R., & Lamb, L. C. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185736 · Cited by 1
Community membership consistency applied to corporate board interlock networks
van Kuppevelt, D. E., Bakhshi, R., Heemskerk, E. M., & Takes, F. W. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00145-5 · Cited by 1
Siudak, D. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01027-6 · Cited by 1
Strategies for improving the quality of community detection based on modularity optimization
Setiadi, T., Yaakub, M. R., & Bakar, A. A. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp1794-1804 · Cited by 1
Variability and ecological relevance of alternative group structures in food webs
Ohlsson, M., & Eklöf, A. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102696 · Cited by 1
Wang, Z., Hu, W., Zhu, J., Zhao, D., Wang, R., Ma, Z., Zeng, S., & Wang, J. (2025) · Animals · doi:10.3390/ani15040609 · Cited by 1
Lesion-Induced Changes to the Network Controllability of the Right Pars Triangularis in Aphasia
Stoll, H., Kelkar, A. S., Turkeltaub, P. E., Hamilton, R. H., Coslett, H. B., & Medaglia, J. D. (2025) · Neurobiology of Language · doi:10.1162/nol.a.11 · Cited by 1
Exploring network structure with the density of states
Arthur, R. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130742 · Cited by 1
Campbell-Cousins, A., Guazzo, F., Bastin, M. E., Parra, M. A., & Escudero, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328736 · Cited by 1
Flexible modularity in the human brain: How network architecture reconfigures over time
Finc, K., Adamska-Stolarczyk, I., & Bassett, D. S. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106784 · Cited by 1
Sukeda, I., & Matsuda, T. (2026) · Neural Computation · doi:10.1162/neco.a.1536 · Cited by 1
A New Method for Extracting the Hierarchical Organization of Networks
Zhan, W., Guan, J., & Zhang, Z. (2013) · International Journal of Information Technology & Decision Making · doi:10.1142/s021962201450028x · Cited by 1
Community Structure based on Node Traffic in Networks
Anand, A., Sihag, V. K., & Sridhar, P. S. V. S. (2013) · International Journal of Computer Applications · doi:10.5120/11901-7966 · Cited by 1
Detecting alternative graph clusterings
Mandala, S. R., Kumara, S. R. T., & Yao, T. (2012) · Physical Review E · doi:10.1103/physreve.86.016111 · Cited by 1
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0003 · Cited by 1
Age-related changes of the representative modular structure in the brain
File, B., Klimaj, Z., Somogyvári, Z., Kozák, L. R., Gyebnár, G., Tóth, B., Kardos, Z., Ulbert, I., & Molnár, M. (2016) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2016.7552346 · Cited by 1
Refinement of the community detection performance by weighted relationship coupling
MIN, D., Yu, K., & Li, H. (2017) · Pramana · doi:10.1007/s12043-016-1343-2 · Cited by 1
A Model Based Metaheuristic For Hybrid Hierarchical Community Structure In Social Networks
Toujani, R., & Akaichi, J. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1130618 · Cited by 1
Worst-case test network optimization for community detection method
Ľuptáková, I. D., Šimon, M., & Pospı́chal, J. (2017) · Balkan Conference in Informatics · doi:10.1145/3136273.3136297 · Cited by 1
Understanding Behavioral Patterns in Truck Co-driving Networks
de Bruin, G. J., Veenman, C. J., van den Herik, H. J., & Takes, F. W. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-05414-4_18 · Cited by 1
Antimodularity: Pragmatic Consequences of Computational Complexity on Scientific Explanation
Rivelli, L. (2019) · Philosophical studies series · doi:10.1007/978-3-030-01800-9_6 · Cited by 1
Sequential detection of temporal communities in evolving networks by estrangement confinement
Sreenivasan, S., & Kawadia, V. (2013) · Bulletin of the American Physical Society · Cited by 1
Ensemble-based algorithms to detect disjoint and overlapping communities in networks
Chakraborty, T., Park, N., & Subrahmanian, V. S. (2016) · Advances in Social Networks Analysis and Mining · doi:10.5555/3192424.3192438 · Cited by 1
On the Lower Bound of Modularity for Graph Fission
Roth, J. D. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048934 · Cited by 1
Modularity based Community Detection in Social Networks
Meena, S. S., & Tokekar, V. (2020) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.b3382.029320 · Cited by 1
Jesan, T., & Sitabhra Sinha, A. (2022) · Journal of Biosciences · doi:10.1007/s12038-022-00292-5 · Cited by 1
Metadata
- Authors
- Good, B. H., de Montjoye, Y.-A., & Clauset, A.
- Affiliations
- Swarthmore College (US)
- Santa Fe Institute (US)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2010-04-15
- Volume
- 81
- Issue
- 4
- Pages
- 046106-046106
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2061099285
- Cited by
- 937
- References
- 66
- Citations by year
- 2026: 18 · 2025: 38 · 2024: 36 · 2023: 36 · 2022: 35 · 2021: 45 · 2020: 58 · 2019: 57 · 2018: 94 · 2017: 76 · 2016: 59 · 2015: 71 · 2014: 80 · 2013: 79 · 2012: 70
- FWCI
- 52.48
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review E (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- modularity optimization, resolution limit, network module identification, degenerate solutions, modularity function, metabolic networks, community detection
- MeSH terms
- Models, Theoretical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
