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 401–500 of 936
Jia, T., Yu, X., Shi, W., Liu, X., Li, X., & Xu, Y. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.121719 · Cited by 18
Walk modularity and community structure in networksPreprint
Mehrle, D., STROSSER, A., & Harkin, A. A. (2016) · Cited by 18
Benchmarking for Graph Clustering and Partitioning
Bader, D. A., Kappes, A., Meyerhenke, H., Sanders, P., Schulz, C., & Wagner, D. (2017) · Springer eBooks · doi:10.1007/978-1-4614-7163-9_23-1 · Cited by 18
Divisive heuristic for modularity density maximization
Costa, A., Kushnarev, S., Liberti, L., & Sun, Z. (2016) · Computers & Operations Research · doi:10.1016/j.cor.2016.01.009 · Cited by 17
Wan, Y., & Liu, Y. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7040159 · Cited by 17
Huang, Y., Shen, C., Zhao, W., Zhang, H., Li, C., Ju, C., Ouyang, R., & Liu, J. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.10.035 · Cited by 18
Convergent evolution of modularity in metabolic networks through different community structures
Zhou, W., & Nakhleh, L. K. (2012) · BMC Evolutionary Biology · doi:10.1186/1471-2148-12-181 · Cited by 17
Stretch-induced network reconfiguration of collagen fibres in the human facet capsular ligament
Zhang, S., Bassett, D. S., & Winkelstein, B. A. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2015.0883 · Cited by 16
Modularity and projection of bipartite networks
Arthur, R. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124341 · Cited by 17
COMMUNITY STRUCTURE DETECTION IN THE EVOLUTION OF THE UNITED STATES AIRPORT NETWORK
Gegov, E., Postorino, M. N., Atherton, M. A., & Gobet, F. R. (2013) · Advances in Complex Systems · doi:10.1142/s0219525913500033 · Cited by 17
Active discovery of network roles for predicting the classes of network nodes
Peel, L. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu043 · Cited by 17
Permanence and Community Structure in Complex Networks
Chakraborty, T., Srinivasan, S., Ganguly, N., Mukherjee, A., & Bhowmick, S. (2016) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2953883 · Cited by 17
An enhanced MILP-based branch-and-price approach to modularity density maximization on graphs
Sato, K., & Izunaga, Y. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.01.012 · Cited by 17
Density-Based Clustering of Social Networks
Menardi, G., & Stefano, D. D. (2022) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12796 · Cited by 16
Network mutual information measures for graph similarity
Felippe, H., Battiston, F., & Kirkley, A. (2024) · Communications Physics · doi:10.1038/s42005-024-01830-3 · Cited by 17
Modularity-based graph partitioning using conditional expected models
Chang, Y.-T., Leahy, R. M., & Pantazis, D. (2012) · Physical Review E · doi:10.1103/physreve.85.016109 · Cited by 16
Community detection in social networks through similarity virtual networks
AlFalahi, K., Atif, Y., & Harous, S. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2500299 · Cited by 16
Community Detection in Complex Network via BGLL Algorithm
Chaturvedi, P., Dhara, M., & Arora, D. (2012) · International Journal of Computer Applications · doi:10.5120/7315-9916 · Cited by 16
Minimum spanning trees for community detection
Wu, J., Li, X., Jiao, L., Wang, X., & Sun, B. (2013) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2013.01.015 · Cited by 16
Oxytocin modulates local topography of human functional connectome in healthy men at rest
Martins, D., Dipasquale, O., & Paloyelis, Y. (2021) · Communications Biology · doi:10.1038/s42003-020-01610-z · Cited by 15
Statistical Analysis of Networks
Avrachenkov, K. E., & Dreveton, M. (2022) · Emerald Group Publishing Limited eBooks · doi:10.1561/9781638280514 · Cited by 15
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
Zhu, G., Zhu, Z., Wang, W., Xu, Z., Yuan, C., & Huang, Y. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00215 · Cited by 15
Tang, W., Carrette, P., Cai, Y., Williamson, J. M., & Daoutidis, P. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108382 · Cited by 15
Community Detection with the Map Equation and Infomap: Theory and Applications
Smiljanić, J., Blöcker, C., Holmgren, A., Edler, D., Neuman, M., & Rosvall, M. (2025) · ACM Computing Surveys · doi:10.1145/3779648 · Cited by 32
Emergence of polarized opinions from free association networks
File, B., Keczer, Z., Vancsó, A., Bőthe, B., Tóth‐Király, I., Hunyadi, M., Ujhelyi, A., Ulbert, I., Góth, J., & Orosz, G. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1090-z · Cited by 16
Finding communities in bicycle sharing system
Shi, X., Wang, Y., Lv, F., Liu, W., Seng, D., & Lin, F. (2019) · Journal of Visualization · doi:10.1007/s12650-019-00587-0 · Cited by 15
A multiresolution framework for the analysis of community structure in international trade networks
Cho, W., Lee, D., & Kim, B. J. (2023) · Scientific Reports · doi:10.1038/s41598-023-32686-2 · Cited by 15
Swift, C. S., & VanderMolen, K. (2015) · State Politics & Policy Quarterly · doi:10.1177/1532440015608760 · Cited by 15
Locally stable brain states predict suppression of epileptic activity by enhanced cognitive effort
Muldoon, S. F., Costantini, J. G., Webber, W. R., Lesser, R. P., & Bassett, D. S. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.02.027 · Cited by 16
Drago, C. (2018) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-319-55708-3_7 · Cited by 14
Aref, S., Mostajabdaveh, M., & Chheda, H. (2024) · Physical review. E · doi:10.1103/physreve.110.044315 · Cited by 14
Consistency of modularity clustering on random geometric graphs
Davis, E., & Sethuraman, S. (2018) · The Annals of Applied Probability · doi:10.1214/17-aap1313 · Cited by 14
Comparative analysis on the selection of number of clusters in community detection
Kawamoto, T., & Kabashima, Y. (2018) · Physical review. E · doi:10.1103/physreve.97.022315 · Cited by 14
Structure Detection in Mixed-Integer Programs
Khaniyev, T., Elhedhli, S., & Erenay, F. S. (2018) · INFORMS journal on computing · doi:10.1287/ijoc.2017.0797 · Cited by 14
Aref, S., Mostajabdaveh, M., & Chheda, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36027-5_48 · Cited by 14
A Novel Algorithm Infomap-SA of Detecting Communities in Complex Networks
Hu, F., & Liu, Y. (2015) · Journal of Communications · doi:10.12720/jcm.10.7.503-511 · Cited by 14
Learning simultaneous adaptive clustering and classification via MOEA
Luo, J., Jiao, L., Shang, R., & Liu, F. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.004 · Cited by 14
Community Structure Detection for Directed Networks through Modularity Optimisation
Yang, L., Cardoso‐Silva, J., Papageorgiou, L. G., & Tsoka, S. (2016) · Algorithms · doi:10.3390/a9040073 · Cited by 14
Multi-scale Modularity and Dynamics in Complex Networks
Lambiotte, R. (2013) · Modeling and simulation in science, engineering & technology · doi:10.1007/978-1-4614-6729-8_7 · Cited by 14
Two-mode modularity clustering of parts and activities for cell formation problems
Kong, T., Seong, K., Song, K., & Lee, K. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.06.018 · Cited by 14
Total Variation Based Community Detection Using a Nonlinear Optimization Approach
Cristofari, A., Rinaldi, F., & Tudisco, F. (2020) · SIAM Journal on Applied Mathematics · doi:10.1137/19m1270446 · Cited by 13
Wang, M., Wang, L., Yang, B., Yuan, L., Wang, X., Potenza, M. N., & Dong, G. (2022) · Brain Communications · doi:10.1093/braincomms/fcac177 · Cited by 12
Inference of dynamic hypergraph representations in temporal interaction data.
Kirkley, A. (2023) · Physical Review E · doi:10.1103/physreve.109.054306 · Cited by 16
Global disorder transition in the community structure of large-q Potts systems
Ronhovde, P., Hu, D., & Nussinov, Z. (2012) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/99/38006 · Cited by 13
Ma, X., & Gao, L. (2011) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2011/05/p05012 · Cited by 13
Community detection based on significance optimization in complex networks
Xiang, J., Wang, Z.-Z., Li, H.-J., Zhang, Y., Li, F., Dong, L.-P., Li, J.-M., & Guo, L.-J. (2017) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/aa6b2c · Cited by 13
Thijs, B., & Glänzel, W. (2018) · Scientometrics · doi:10.1007/s11192-018-2659-0 · Cited by 13
A neighbour-similarity based community discovery algorithm
Sahu, S., & Rani, T. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117822 · Cited by 13
Comparing local modularity optimization for detecting communities in networks
Xiang, J., Wang, Z., Li, H., Zhang, Y. T., Chen, S., Liu, C., Li, J., & Guo, L. (2017) · International Journal of Modern Physics C · doi:10.1142/s012918311750084x · Cited by 13
Contextual and Developmental Differences in the Neural Architecture of Cognitive Control
Petrican, R., & Grady, C. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0667-17.2017 · Cited by 14
Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling
Faskowitz, J., & Sporns, O. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01984-9 · Cited by 15
Diffusion in Networks with Overlapping Community Structure
Reid, F., & Hurley, N. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.66 · Cited by 13
Software Quality and Community Structure in Java Software Networks
Concas, G., Marchesi, M., Monni, C., Orrù, M., & Tonelli, R. (2017) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194017500401 · Cited by 13
Benefits of Bias: Towards Better Characterization of Network SamplingPreprint
Maiya, A. S., & Berger-Wolf, T. Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1109.3911 · Cited by 13
Liu, L., Gao, C., Zhu, Z., Zhang, S., & Tang, X. (2024) · Climate Dynamics · doi:10.1007/s00382-024-07322-y · Cited by 13
Time-varying synergy/redundancy dominance in the human cerebral cortex
Pope, M., Varley, T. F., Puxeddu, M. G., Faskowitz, J., & Sporns, O. (2025) · Journal of Physics Complexity · doi:10.1088/2632-072x/adbaa9 · Cited by 12
Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network
Caughlin, T. T., Ruktanonchai, N., Acevedo, M. A., Lopiano, K. K., Prosper, O., Eagle, N., & Tatem, A. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056057 · Cited by 12
Graph coarse‐graining reveals differences in the module‐level structure of functional brain networks
Kujala, R., Glerean, E., Pan, R. K., Jääskeläinen, I. P., Sams, M. E., & Saramäki, J. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13392 · Cited by 12
Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks
Luo, S., Zhang, Z., Zhang, Y., & Ma, S. (2019) · Entropy · doi:10.3390/e21010095 · Cited by 12
Strategizing COVID-19 lockdowns using mobility patterns
Buchel, O., Ninkov, A., Cathel, D., Yaneer Bar-yam, A., & Hedayatifar, L. (2021) · Royal Society Open Science · doi:10.1098/rsos.210865 · Cited by 12
Finding communities in weighted networks through synchronization
Lou, X., & Suykens, J. A. K. (2011) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3655371 · Cited by 12
Detecting Communities in Sparse MANETs
Drugan, O. V., Plagemann, T., & Munthe-Kaas, E. (2011) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2011.2112376 · Cited by 12
Social Network Clustering and Visualization using Hierarchical Edge Bundles
Jia, Y., Garland, M., & Hart, J. C. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.02037.x · Cited by 12
Information diffusion model in modular microblogging networks
Xiong, X., Ma, J., Wang, M., Zhou, G., & Xu, K. (2014) · World Wide Web · doi:10.1007/s11280-014-0306-0 · Cited by 12
Cluster Analysis of Weighted Bipartite Networks: A New Copula-Based Approach
Chessa, A., Crimaldi, I., Riccaboni, M., & Trapin, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109507 · Cited by 12
Network topology of symbolic and nonsymbolic number comparison
Conrad, B. N., Wilkey, E. D., Yeo, D. J. S., & Price, G. R. (2020) · Network Neuroscience · doi:10.1162/netn_a_00144 · Cited by 10
Stability-to-instability transition in the structure of large-scale networks
Hu, D., Ronhovde, P., & Nussinov, Z. (2012) · Physical Review E · doi:10.1103/physreve.86.066106 · Cited by 12
Algorithm for parametric community detection in networks
Bettinelli, A., Hansen, P., & Liberti, L. (2012) · Physical Review E · doi:10.1103/physreve.86.016107 · Cited by 12
Zhuo, Z., Cai, S., Tang, M., & Lai, Y. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5025646 · Cited by 12
On the Fourier transform of a quantitative trait: Implications for compressive sensing
Doro, S., & Herman, M. A. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110985 · Cited by 11
Lee, T., & Tramontano, G. (2021) · AIMS neuroscience · doi:10.3934/neuroscience.2021028 · Cited by 8
Cortical timescales and the modular organization of structural and functional brain networks
Lurie, D. J., Pappas, I., & D’Esposito, M. (2024) · Human Brain Mapping · doi:10.1002/hbm.26587 · Cited by 10
Reduced Markovian models of dynamical systems
Giorgini, L. T., Souza, A. N., & Schmid, P. J. (2024) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2024.134393 · Cited by 11
AN ANT-BASED ALGORITHM WITH LOCAL OPTIMIZATION FOR COMMUNITY DETECTION IN LARGE-SCALE NETWORKS
He, D., Liu, J., Yang, B. X., Huang, Y., Liu, D., & Jin, D. (2012) · Advances in Complex Systems · doi:10.1142/s0219525912500361 · Cited by 11
Community detection enhancement using non-negative matrix factorization with graph regularization
Liu, X., Wei, Y., Wang, J., Wang, W., He, D., & Song, Z. (2016) · International Journal of Modern Physics B · doi:10.1142/s0217979216501307 · Cited by 11
Shafiei, S. B., Durrani, M., Jing, Z., Mostowy, M., Doherty, P., Hussein, A. A., Elsayed, A. S., Iqbal, U. H., & Guru, K. A. (2021) · Sensors · doi:10.3390/s21051733 · Cited by 11
Identifying correlation clusters in many-body localized systems
Hémery, K., Pollmann, F., & Smith, A. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.064202 · Cited by 12
Towards scaling community detection on distributed-memory heterogeneous systems
Gawande, N. A., Ghosh, S., Halappanavar, M. M., Tumeo, A., & Kalyanaraman, A. (2022) · Parallel Computing · doi:10.1016/j.parco.2022.102898 · Cited by 11
Ensemble-based algorithms to detect disjoint and overlapping communities in networks
Chakraborty, T., Park, N., & Subrahmanian, V. S. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752216 · Cited by 11
Belgium through the Lens of Rail Travel Requests: Does Geography Still Matter?
Jones, J., Cloquet, C., Adam, A., Decuyper, A., & Thomas, I. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5110216 · Cited by 11
An algorithm Walktrap-SPM for detecting overlapping community structure
Hu, F., Zhu, Y., Shi, Y., Cai, J., Chen, L., & Shen, S. (2017) · International Journal of Modern Physics B · doi:10.1142/s0217979217501211 · Cited by 11
The landscape of NeuroImage-ing research
Dworkin, J. D., Shinohara, R. T., & Bassett, D. S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.005 · Cited by 11
Purposes and challenges of legal citation network analysis on case law
van Kuppevelt, D. E., DIJCK, G. V., & Schaper, M. (2020) · Edward Elgar Publishing eBooks · doi:10.4337/9781788977456.00017 · Cited by 11
Revealing and exploiting hierarchical material structure through complex atomic networks
Ahnert, S. E., Grant, W. P., & Pickard, C. J. (2017) · Apollo (University of Cambridge) · doi:10.17863/cam.12177 · Cited by 11
Soto, F. A., Bassett, D. S., & Ashby, F. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.032 · Cited by 13
Modularity Maximization and Tree Clustering: Novel Ways to Determine Effective Geographic Borders
Grady, D., Brune, R., Thiemann, C., Theis, F. J., & Brockmann, D. (2011) · Springer optimization and its applications · doi:10.1007/978-1-4614-0754-6_7 · Cited by 11
Surveying network community structure in the hidden metric space
Ma, L., Jiang, X., Wu, K., Zhang, Z., Tang, S., & Zheng, Z. M. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.07.057 · Cited by 10
Community structure revealed by phase locking
Zhou, M.-Y., Zhuo, Z., Cai, S., & Fu, Z. (2014) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4894764 · Cited by 11
Community detection in multi-frequency EEG networks
Karaaslanli, A., Ortiz-Bouza, M., Munia, T. T. K., & Aviyente, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-35232-2 · Cited by 10
Bouyer, A., Shahgholi, P., Arasteh, B., & Tırkolaee, E. B. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109502 · Cited by 10
Neural dynamics during the generation and evaluation of creative and non-creative ideas
Kenett, Y. N., Chrysikou, E. G., Bassett, D. S., & Thompson‐Schill, S. L. (2025) · Communications Biology · doi:10.1038/s42003-025-09018-3 · Cited by 10
A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks
Dinh, T. N., Nguyen, N. P., Alim, M. A., & Thai, M. T. (2013) · Journal of Combinatorial Optimization · doi:10.1007/s10878-013-9665-1 · Cited by 10
Critical analysis of (Quasi-)Surprise for community detection in complex networks
Xiang, J., Li, H., Bu, Z., Wang, Z., Bao, M., Tang, L., & Li, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-32582-0 · Cited by 10
Evolution of semantic networks in biomedical texts
Chai, L. R., Zhou, D., & Bassett, D. S. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz023 · Cited by 10
Kishore, R., Gogineni, A. K., Nussinov, Z., & Sahu, K. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-39180-8 · Cited by 10
Intermittent brain network reconfigurations and the resistance to social media influence
Pinto, Í. L. D., Rungratsameetaweemana, N., Flaherty, K., Periyannan, A., Meghdadi, A. H., Richard, C., Berka, C., Bansal, K., & Garcia, J. O. (2022) · Network Neuroscience · doi:10.1162/netn_a_00255 · Cited by 10
Robustness Analysis of Network Modularity
Kim, J., & Cho, K. (2015) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2015.2476197 · Cited by 10
Community detection and analysis of community evolution in Apache Ant class collaboration networks
Savić, M., Radovanović, M., & Ivanović, M. D. (2012) · Balkan Conference in Informatics · doi:10.1145/2371316.2371361 · Cited by 10
Community Detection Based on Topic Distance in Social Tagging Networks
Liu, H., Chen, H., Lin, M., & Wu, Y. C. (2014) · TELKOMNIKA Indonesian Journal of Electrical Engineering · doi:10.11591/telkomnika.v12i5.4170 · Cited by 10
Detecting communities in time-evolving proximity networks
Pandit, S., Yang, Y., Kawadia, V., Sreenivasan, S., & Chawla, N. V. (2011) · IEEE Network Science Workshop (NSW) · doi:10.1109/nsw.2011.6004643 · Cited by 10
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
