Pith. sign in

REVIEW 3 major objections 7 minor 1 cited by

On community structure in complex networks: challenges and opportunities

T0 review · 3 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Community structure should shape epidemic immunization strategy

desk verdict A useful survey and taxonomy of community detection and immunization strategies, but its central comparative claim about immunization performance rests on heterogeneous experiments never run on a common footing. read the letter →

arxiv 1908.04901 v3 pith:M6OEHSPP submitted 2019-08-14 physics.soc-ph cond-mat.stat-mechcs.SI

Hocine Cherifi , Gergely Palla , Boleslaw K. Szymanski , Xiaoyan Lu This is my paper · ORCID
classification physics.soc-phcond-mat.stat-mechcs.SI
keywords communitystructuremodularitystochasticblockmodeldetectiontime-evolvingnetworksimmunizationstrategiesepidemicspreadingcentrality
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This position paper argues that community structure is not a byproduct of network analysis but a load-bearing feature that should drive how we model networks, track their evolution, and control epidemics. The paper synthesizes generative models, chiefly the stochastic block model and its degree-corrected variant, and shows how they connect to modularity maximization, including a recent equivalence result and bounds on the resolution parameter. For time-evolving networks, it catalogs snapshot matching, evolutionary algorithms, and incremental and online methods. Its central synthesis claim, for immunization, is that using more information about community structure improves outbreak control: local strategies outperform global ones in networks with well-separated communities, while global strategies win when community structure is loose. If correct, the practical consequence is that intervention design should begin by measuring community strength rather than applying a default centrality measure.

What carries the argument

The organizing apparatus is a local-versus-global axis of node influence in modular networks, expressed through bridge nodes (nodes carrying inter-community links) and hub nodes (nodes carrying intra-community links). Its formal anchor is modularity $Q$, defined against the configuration model, together with the stochastic block model family; the equivalence between maximizing generalized modularity and maximum-likelihood inference of the degree-corrected planted partition model, with resolution parameter $\gamma = (\omega_1-\omega_0)/(\log\omega_1-\log\omega_0)$, is what lets the paper treat detection quality and immunization choice as two sides of the same community-strength axis. In the immunization section, the load-bearing mechanism is that when few inter-community links exist, outbreaks remain local, so community hubs are the right targets; when many such links exist, bridges carry the outbreak globally.

What would settle it

Run a single controlled benchmark on networks that differ only in community strength, holding degree sequence, size, and epidemic parameters fixed, and compare one local strategy with one global strategy. If the global strategy wins in strongly modular networks, or the local strategy wins in loosely modular networks, the paper's central conditional ranking is falsified; observing no monotone relationship between added community information and epidemic size would also undercut it.

Watch

Extended reading notes

Core claim

The paper's central claim, developed across its three sections, is that community structure is a quantitative, measurable property that should govern both how we detect groups and how we intervene in a network. On detection, it presents the stochastic block model as the principled generative foundation and reports that maximizing generalized modularity is equivalent to maximum-likelihood inference of the degree-corrected planted partition model, with a resolution parameter whose admissible range can be bounded. On dynamics, it argues that time-evolving communities can be recovered by snapshot matching, evolutionary algorithms, or incremental and online methods, each trading off accuracy, smoothness, and information. On immunization, the synthesis claim is that strategy quality rises with the amount of community-structure information used: local strategies outperform global strategies when communities are well separated, global strategies outperform local ones when communities are loose, combined strategies do best overall, and overlapping nodes act as epidemic carriers between modules.

Load-bearing premise

The comparative conclusions about immunization assume that the SIR and SI simulations and synthetic benchmarks used across the many cited studies are consistent with each other and represent real-world contact networks, even though the review itself runs no unified benchmark.

Editorial extensions

If this is right

  • If more community information improves immunization, deterministic strategies with full network knowledge should generally beat stochastic ones, and stochastic strategies should be redesigned to estimate community structure locally.
  • In networks with strong community structure, prioritize local hubs or community core nodes; in loose networks, prioritize bridge nodes; combination strategies that score both dimensions should be the safest default.
  • Overlapping nodes are high-value targets: membership-based and overlap-aware strategies can outperform degree, betweenness, and coreness in dense modular networks.
  • Modular centrality, or any centrality recast as local and global components, is a promising route because it is agnostic to the base centrality and leaves room for tuning the combination.
  • Future work should aim at semi-stochastic strategies that sit between fully local random-walk methods and fully global ranking methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the authors do not develop: use community strength itself as a tunable parameter in a single adaptive strategy, so the same algorithm shifts weight from hubs to bridges as measured modularity decreases.
  • Because the surveyed rankings come from heterogeneous benchmarks, a fair comparison would require stratified benchmarks that vary only the ratio of inter- to intra-community links while holding degree distribution and size fixed; the paper's conditional claims predict that strategy rankings will invert across that axis.
  • The same local/global logic likely transfers to other diffusion processes on modular networks, such as misinformation or computer-virus spread, where intervention costs differ; the paper never makes this analogy explicit.
  • An adaptive stochastic strategy that estimates bridge-ness from short random walks could capture most of the benefit of deterministic strategies at a fraction of the information cost, which would make the semi-stochastic direction concrete.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This manuscript is a review/position paper in three parts. Section 2 reviews generative models for community structure (ER graphs, configuration model, stochastic block model and its degree-corrected variants, planted partition model) and statistical inference, including the connection between modularity maximization and maximum-likelihood estimation of the planted partition model. Section 3 surveys dynamic community detection, organized into snapshot-based, evolutionary, incremental/online, and prediction-oriented approaches. Section 4 reviews immunization strategies for modular networks, classifying them into stochastic and deterministic strategies and, within the latter, into global, local, and combined variants for non-overlapping and overlapping communities. The paper's central synthesis, stated in Section 4.3.5 and echoed in Section 5, is that immunization performance increases as more community-structure information is exploited, that local strategies outperform global strategies in networks with strong community structure, that global strategies outperform local strategies in networks with loose community structure, and that combined strategies generally perform best. The review also identifies open problems, notably the lack of controlled benchmarks for evolving community detection.

Significance. The review is competently assembled and the mathematical core (Eqs. 1-10) is standard and correctly transcribed from the cited literature. It provides a useful taxonomy of the immunization literature and a clear statement of open problems, such as the need for benchmarks for dynamic community detection. The paper's contribution is synthetic rather than novel: it runs no unified experiment, and it is transparent about shipping no code, which is acceptable for a survey. If the Section 4.3.5 synthesis were established on a common benchmark, it would be practically valuable, since it would give practitioners clear guidance on when to prefer community-aware strategies and how much structural information is worth collecting. The main weakness is evidential: the central comparative claims are aggregated from many heterogeneous studies rather than demonstrated on a common footing, so the review's conclusions need to be either strengthened by a systematic comparison or explicitly qualified.

major comments (3)
  1. [§4.3.5 and §4.1.2] The paper's central claim that 'the performance of the immunization strategies increases when more information about the community structure is used' and the conditional local/global ranking are not established by the evidence presented. The strategies being compared (CBF, DCBF, BHD, RWOS, Mod, BVA, NNC, CbM, WCHB, OC, and others) come from independent studies that differ in epidemic model (SIR vs SI vs independent cascade), network generator (LFR, Facebook subnetworks, co-authorship graphs), community-detection algorithm, immunization budget, and baseline strategy. A reported rank can therefore reflect the experimental setup rather than intrinsic method quality; for example, the claimed superiority of BHD and RWOS over CBF, or of WCHB over Comm and CbM, is never tested on a common footing. To make the synthesis load-bearing, the authors should either (i) tabulate for every cited comparison the epidemic model, network type, budget, and baseline and restrict each ranking claim to matched settings, or (ii) explicitly soften the claims to 'within each cited study, more community information helped.' Without this, Section 4.3.5 overstates what the literature supports.
  2. [§4.3.5] The conditional ranking depends on the notion of 'community structure strength,' which is used informally and inconsistently across the review: in §4.1.2 it is identified with high modularity (Q > 0.84), in §4.3.1 with the proportion of intra-community links, and in §4.3.3 with 'medium strength' without any formal threshold. Since the central synthesis says that strategy choice should depend on this quantity, the review needs a working definition, or at least a statement that the cited studies measure it in incompatible ways. As written, the conditional local/global ranking is not falsifiable from the survey data.
  3. [§4.3.5] The phrase 'more information about the community structure' is used in incompatible senses: membership counts (RWOS), inter-community link proportions (WCHB), community sizes (CbC), and bridge-hub identities (BHD) are treated as if they lay on a single scale of information content. The claim that performance increases with more information therefore conflates qualitatively different features. The authors should either define a partial order over the information features used by the discussed strategies or restrict the statement to specific features, otherwise the central synthesis is unfalsifiable.
minor comments (7)
  1. [Figures 1 and 2] Figure 1 cites CBF [3], DCBF [7], BHD [4], and RWOS [8], but the text cites these methods as [11], [89], [12], and [90]; Figure 2 has analogous mismatches (for example, 'Community centrality [2]' whereas the text discusses it as [10]). Please update all figure citations to the manuscript's reference list.
  2. [§5] Section 5 contains the sentence 'Another drawback of this approach is that the stochastic block model requires the selection of the number of communities...' twice in consecutive paragraphs; please delete the duplicate.
  3. [§4.3.5] The acronym 'WCBM' appears in this section, while the same strategy is defined earlier as 'WCHB' (also written 'WCBH' in places); please unify the acronym throughout.
  4. [§4.3.4] The text says the modular centrality work 'has been extended to networks with non-overlapping community structure [109]', but reference [109] is titled 'Centrality in complex networks with overlapping community structure'; please correct the wording.
  5. [Eq. (9)] The Metropolis-Hastings acceptance probability is written as a = min{...} without the upper bound of 1; as written, a can exceed 1. The expression should be min(1, ...).
  6. [Eqs. (2) and (8)] The notation '{i,j}∈r' is ambiguous regarding whether ordered or unordered pairs are summed; because the factor of two matters in the modularity expression, please specify this explicitly.
  7. [Figure 2] The label 'K-sell with community' should read 'k-shell with community'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a literature review whose claims are aggregated from cited experiments, not derived from fitted parameters or self-referential definitions.

full rationale

This manuscript is a position/review paper. It contains no new model, no fitted parameters, and no equation whose output is defined by its own input. Section 2 reviews generative models (ER, configuration model, SBM, planted partition) and re-derives standard likelihoods (Eqs. 1-8); these are textbook results, not predictions extracted from data. Section 3 surveys dynamic community detection methods and explicitly delegates detailed comparison to other surveys. Section 4 summarizes published immunization strategies; the comparative statements in Section 4.3.5, such as 'the performance of the immunization strategies increases when more information about the community structure is used,' are inductive summaries of published SIR/SI experiments, not derivations from a model fitted in this paper. Some supporting references in Section 4 are co-authored by the review's own authors (e.g., refs. 97, 102, 103, 108, 109, 112), but they are cited as external experimental results with their own benchmarks, not as unstated assumptions of the present text, and the same local-vs-global comparative pattern is also supported by non-self references (e.g., refs. 99, 101, 104). The skeptic's concern about heterogeneous SIR/SI setups and non-unified benchmarks is a correctness/validity issue about cross-paper comparability, not a circularity issue: no claim in the review reduces by construction to an input of the review. Accordingly, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no free parameters, fitted values, or invented entities. Its central claims rest on domain assumptions about the relevance of community structure and the validity of the cited generative and epidemic models, which are drawn from the prior literature.

assumptions (3)
  • domain assumption Community structure is a meaningful and practically relevant feature of real-world networks.
    The entire review is predicated on this; Section 1 states that communities are widely observed and important for applications.
  • domain assumption The stochastic block model family provides adequate generative models for community structure.
    Section 2 introduces SBM and its variants as the standard modeling framework without critically examining alternative generative models.
  • domain assumption Epidemic simulations (SIR and SI) on synthetic and empirical networks are faithful proxies for real-world disease spreading.
    All comparative claims about immunization strategies in Section 4 rest on simulations from the cited papers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of On community structure in complex networks: challenges and opportunities." pith.science (2026). https://pith.science/paper/M6OEHSPP

@misc{pith2026190804901,
  author       = {Pith},
  title        = {Pith review of: On community structure in complex networks: challenges and opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M6OEHSPP}},
  note         = {Machine review of arXiv:1908.04901}
}
read the original abstract

Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we overview the Stochastic Block Model, its different variants, and inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear and/or new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models

    cs.SI 2019-08 conditional novelty 6.0 of 10

    A time-dependent stochastic block model with mixed or discrete membership classifies bicycle-sharing stations into home and work roles in Los Angeles, San Francisco, and a Manhattan subnetwork of New York City.

Reference graph

Works this paper leans on

130 extracted references · 61 canonical work pages · cited by 1 Pith paper

  1. [1]

    Newman, Modularity and community structure in networks, Proc

    M.E. Newman, Modularity and community structure in networks, Proc. Nat. Acad. Sci. 103(23), 8577 (2006)

    2006

  2. [2]

    Clauset, M

    A. Clauset, M. Newman, C. Moore, Finding community structure in very large net- works, Phys. Rev. E 70, 066111 (2004)

    2004

  3. [3]

    Blondel, J.L

    V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of commu- nities in large networks, Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008). DOI 10.1088/1742-5468/2008/10/p10008

    doi:10.1088/1742-5468/2008/10/p10008 2008

  4. [4]

    M. Chen, K. Kuzmin, B. Szymanski, Community detection via maximization of mod- ularity and its variants, IEEE Trans. Computational Social Systems 1(1), 46 (2014)

    2014

  5. [5]

    Newman, Equivalence between modularity optimization and maximum likelihood methods for community detection, Phys

    M. Newman, Equivalence between modularity optimization and maximum likelihood methods for community detection, Phys. Rev. E 94(5), 052315 (2016)

    2016

  6. [6]

    X. Lu, B. Szymanski, Asymptotic resolution bounds of generalized modularity and sta- tistically significant community detection, Information Sciences 510 (2020, to appear, available arXiv:1902.04243)

    work page Pith review arXiv 2020

  7. [7]

    X. Lu, B. Szymanski, Adaptive modularity maximization via edge weighting scheme, Scientific Reports 9, 13247 (2019)

    2019

  8. [8]

    Wang, C.T

    Z. Wang, C.T. Bauch, S. Bhattacharyya, A. d’Onofrio, P. Manfredi, M. Perc, N. Perra, M. Salath, D. Zhao, Statistical physics of vaccination, Physics Reports 664, 1 (2016)

    2016

Show all 130 references
  1. [9]

    Helbing, D

    D. Helbing, D. Brockmann, T. Chadefaux, K. Donnay, U. Blanke, O. Woolley-Meza, M. Moussaid, J. Anders, J. Krause, S. Schutte, M. Perc, Saving human lives: What com- plexity science and information systems can contribute, Journal of Statistical Physics 158(3), 735 (2015)

    2015

  2. [10]

    Newman, Finding community structure in networks using the eigenvectors of matrices, Physical review E 74(3), 036104 (2006)

    M.E. Newman, Finding community structure in networks using the eigenvectors of matrices, Physical review E 74(3), 036104 (2006)

    2006

  3. [11]

    Salath´ e, J.H

    M. Salath´ e, J.H. Jones, Dynamics and control of diseases in networks with community structure, PLoS computational biology 6(4), e1000736 (2010)

    2010

  4. [12]

    K. Gong, M. Tang, P.M. Hui, H.F. Zhang, D. Younghae, Y.C. Lai, An efficient immu- nization strategy for community networks, PloS one 8(12), e83489 (2013)

    2013

  5. [13]

    Restrepo, E

    J.G. Restrepo, E. Ott, B.R. Hunt, Weighted percolation on directed networks, Physical review letters 100(5), 058701 (2008)

    2008

  6. [14]

    Ghalmane, M

    Z. Ghalmane, M. El Hassouni, H. Cherifi, in 2018 IEEE Workshop on Complexity in Engineering (COMPENG) (IEEE, 2018), pp. 1–5 Community structure: challenges and opportunities 37

    2018

  7. [15]

    Erdos, A

    P. Erdos, A. Renyi, On random graphs i, Publ. Math. Debrecen 6, 290 (1959)

    1959

  8. [16]

    Gilbert, Random graphs, The Annals of Mathematical Statistics 30(4), 1141 (1959)

    E.N. Gilbert, Random graphs, The Annals of Mathematical Statistics 30(4), 1141 (1959)

    1959

  9. [17]

    Molloy, B

    M. Molloy, B. Reed, A critical point for random graphs with a given degree sequence, Random structures & algorithms 6(2-3), 161 (1995)

    1995

  10. [18]

    Holland, K.B

    P. Holland, K.B. Laskey, S. Leinhardt, Stochastic blockmodels: First steps, Social networks 5(2), 109 (1983)

    1983

  11. [19]

    Karrer, M

    B. Karrer, M. Newman, Stochastic blockmodels and community structure in networks, Phys. Rev. E 83(1), 016107 (2011)

    2011

  12. [20]

    Perry, P.J

    P.O. Perry, P.J. Wolfe, Null models for network data, arXiv preprint arXiv:1201.5871 (2012)

    2012 arXiv

  13. [21]

    McSherry, in Proceedings 2001 IEEE International Conference on Cluster Com- puting (IEEE, 2001), pp

    F. McSherry, in Proceedings 2001 IEEE International Conference on Cluster Com- puting (IEEE, 2001), pp. 529–537

    2001

  14. [22]

    Condon, R

    A. Condon, R. Karp, Algorithms for graph partitioning on the planted partition model, Random Structures & Algorithms 18(2), 116 (2001)

    2001

  15. [23]

    Asratian, T

    A. Asratian, T. Denley, R. H¨ aggkvist,Bipartite graphs and their applications, vol. 131 (Cambridge university press, 1998)

    1998

  16. [24]

    Reichardt, S

    J. Reichardt, S. Bornholdt, Statistical mechanics of community detection, Phys. Rev. E 74(1), 016110 (2006)

    2006

  17. [25]

    Fortunato, M

    S. Fortunato, M. Barthelemy, Resolution limit in community detection, Proc. Nat. Acad. Sci. 104(1), 36 (2007)

    2007

  18. [26]

    Brandes, D

    U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer. Maximizing modularity is hard (2016, arXiv:0608255)

    2016

  19. [27]

    Riolo, G

    M. Riolo, G. Cantwell, G. Reinert, M. Newman, Efficient method for estimating the number of communities in a network, Physical review e 96(3), 032310 (2017)

    2017

  20. [28]

    Newman, G

    M.E. Newman, G. Reiner, Estimating the number of communities in a network, Phys. Rev. Let. 117(7), 078301 (2016)

    2016

  21. [29]

    Peixoto, Entropy of stochastic blockmodel ensembles, Physical Review E 85(5), 056122 (2012)

    T.P. Peixoto, Entropy of stochastic blockmodel ensembles, Physical Review E 85(5), 056122 (2012)

    2012

  22. [30]

    Peixoto, Bayesian stochastic blockmodeling, arXiv preprint arXiv:1705.10225 (2017)

    T.P. Peixoto, Bayesian stochastic blockmodeling, arXiv preprint arXiv:1705.10225 (2017)

    2017 arXiv

  23. [31]

    Ghasemian, H

    A. Ghasemian, H. Hosseinmardi, A. Clauset, Evaluating overfit and underfit in models of network community structure, IEEE Transactions on Knowledge and Data Engi- neering (early access 2019)

    2019

  24. [32]

    Peixoto, Efficient monte carlo and greedy heuristic for the inference of stochastic block models, Phys

    T. Peixoto, Efficient monte carlo and greedy heuristic for the inference of stochastic block models, Phys. Rev. E 89, 012804 (2014)

    2014

  25. [33]

    Metropolis, A

    N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21, 1087 (1953)

    1953

  26. [34]

    Rossetti, R

    G. Rossetti, R. Cazabet, Community discovery in dynamic networks: A survey, ACM Comput. Surv. 51(2), 35:1 (2018). DOI 10.1145/3172867

    2018 doi

  27. [35]

    Hopcroft, O

    J. Hopcroft, O. Khan, B. K, B. Selman, Tracking evolving communities in large linked networks, Proc Natl Acad Sci USA 101(suppl 1), 5249 (2004)

    2004

  28. [37]

    Palla, A.L

    G. Palla, A.L. Barab´ asi, T. Vicsek, Quantifying social group evolution, Nature 446, 664 (2007)

    2007

  29. [38]

    Greene, D

    D. Greene, D. Doyle, P. Cunningham, Tracking the evolution of communities in dy- namic social networks, 2010 International Conference on Advances in Social Networks Analysis and Mining pp. 176–183 (2010)

    2010

  30. [39]

    Rosvall, C.T

    M. Rosvall, C.T. Bergstrom, Mapping change in large networks, PLOS ONE 5(1), 1 (2010). DOI 10.1371/journal.pone.0008694

    2010 doi

  31. [40]

    Br´ odka, S

    P. Br´ odka, S. Saganowski, P. Kazienko, Ged: the method for group evolution discovery in social networks, Social Network Analysis and Mining 3(1), 1 (2013). DOI 10.1007/ s13278-012-0058-8

    2013

  32. [41]

    Danon, A

    L. Danon, A. D´ ıaz-Guilera, J. Duch, A. Arenas, Comparing community structure iden- tification, J. Stat. Mech. (2005)

    2005

  33. [42]

    Lancichinetti, S

    A. Lancichinetti, S. Fortunato, J. Kert´ esz, Detecting the overlapping and hierarchical community structure in complex networks, New J. Phys. 11, 033015 (2009) 38 Cherifi, Palla, Szymanski, Lu

    2009

  34. [43]

    Amelio, C

    A. Amelio, C. Pizzuti, Correction for closeness: Adjusting normalized mutual informa- tion measure for clustering comparison, Computational Intelligence 33(3), 579 (2017). DOI 10.1111/coin.12100

    2017 doi

  35. [44]

    Chakrabarti, R

    D. Chakrabarti, R. Kumar, A. Tomkins, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, NY, USA, 2006), KDD ’06, pp. 554–560. DOI 10.1145/1150402.1150467

    2006

  36. [45]

    Y. Chi, X. Song, D. Zhou, K. Hino, B.L. Tseng, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, NY, USA, 2007), KDD ’07, pp. 153–162. DOI 10.1145/1281192.1281212

    2007

  37. [46]

    Y.R. Lin, Y. Chi, S. Zhu, H. Sundaram, B.L. Tseng, in Proceedings of the 17th Inter- national Conference on World Wide Web (ACM, New York, NY, USA, 2008), WWW ’08, pp. 685–694. DOI 10.1145/1367497.1367590

    2008

  38. [47]

    D. Zhou, I. Councill, H. Zha, C.L. Giles, in In ICDM07 (2007), p. 745750

    2007

  39. [48]

    L. Tang, H. Liu, J. Zhang, Z. Nazeri, in Proceedings of the 14th ACM SIGKDD In- ternational Conference on Knowledge Discovery and Data Mining (ACM, New York, NY, USA, 2008), KDD ’08, pp. 677–685. DOI 10.1145/1401890.1401972

    2008

  40. [49]

    Folino, C

    F. Folino, C. Pizzuti, in Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, NY, USA, 2010), GECCO ’10, pp. 535–

    2010

  41. [50]

    Y. Sun, J. Tang, J. Han, M. Gupta, B. Zhao, in Proceedings of the Eighth Workshop on Mining and Learning with Graphs (ACM, New York, NY, USA, 2010), MLG ’10, pp. 137–146. DOI 10.1145/1830252.1830270

    2010

  42. [51]

    Gong, L.J

    M.G. Gong, L.J. Zhang, J.J. Ma, L.C. Jiao, Community detection in dynamic social networks based on multiobjective immune algorithm, Journal of Computer Science and Technology 27(3), 455 (2012). DOI 10.1007/s11390-012-1235-y

    2012 doi

  43. [52]

    Kawadia, S

    V. Kawadia, S. Sreenivasan, Sequential detection of temporal communities by estrange- ment confinement, Scientific Reports 2, 794 (2012)

    2012

  44. [53]

    Crane, W

    H. Crane, W. Dempsey, Community detection for interaction networks, CoRR abs/1509.09254 (2015). URL http://arxiv.org/abs/1509.09254

    2015 arXiv

  45. [54]

    G¨ orke, P

    R. G¨ orke, P. Maillard, A. Schumm, C. Staudt, D. Wagner, Dynamic graph clustering combining modularity and smoothness, J. Exp. Algorithmics 18, 1.5:1.1 (2013). DOI 10.1145/2444016.2444021

    2013

  46. [55]

    Mucha, T

    P.J. Mucha, T. Richardson, K. Macon, M.A. Porter, J.P. Onnela, Community structure in time-dependent, multiscale, and multiplex networks, Science 328(5980), 876 (2010). DOI 10.1126/science.1184819

    2010 doi

  47. [56]

    Lancichinetti, S

    A. Lancichinetti, S. Fortunato, Consensus clustering in complex networks, Scientific Reports 2, 336 (2012)

    2012

  48. [57]

    T. Yang, Y. Chi, S. Zhu, Y. Gong, R. Jin, A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks (SIMA, 2009), pp. 990–1001. DOI 10.1137/1.9781611972795.85

    2009 doi

  49. [58]

    Peixoto, Inferring the mesoscale structure of layered, edge-valued, and time- varying networks, Phys

    T.P. Peixoto, Inferring the mesoscale structure of layered, edge-valued, and time- varying networks, Phys. Rev. E 92, 042807 (2015). DOI 10.1103/PhysRevE.92.042807

    2015 doi

  50. [59]

    Ghasemian, P

    A. Ghasemian, P. Zhang, A. Clauset, C. Moore, L. Peel, Detectability thresholds and optimal algorithms for community structure in dynamic networks, Phys. Rev. X 6, 031005 (2016). DOI 10.1103/PhysRevX.6.031005

    2016 doi

  51. [60]

    Peixoto, M

    T.P. Peixoto, M. Rosvall, Modelling sequences and temporal networks with dynamic community structures, Nature Communications 8, 582 (2017)

    2017

  52. [61]

    Hoffmann, L

    T. Hoffmann, L. Peel, R. Lambiotte, N.S. Jones, Community detection in networks with unobserved edges (2018). ArXiv:1808.06079

    2018 arXiv

  53. [62]

    Peixoto, Network reconstruction and community detection from dynamics (2019)

    T.P. Peixoto, Network reconstruction and community detection from dynamics (2019). ArXiv:1903.10833

    2019 arXiv

  54. [63]

    Aynaud, E

    T. Aynaud, E. Fleury, J. Guillaume, Q. Wang, Communities in Evolving Networks: Definitions, Detection and Analysis Techniques (Springer, New York, 2013), vol. 2, pp. 159–200

    2013

  55. [64]

    H. Ning, W. Xu, Y. Chi, Y. Gong, T.S. Huang, Incremental spectral clustering by efficiently updating the eigen-system, Pattern Recogn. 43(1), 113 (2010). DOI 10. 1016/j.patcog.2009.06.001 Community structure: challenges and opportunities 39

    2010

  56. [65]

    Bansal, S

    S. Bansal, S. Bhowmick, P. Paymal, in Communications in Computer and Informa- tion Science, Communications in Computer and Information Science , vol. 116 CCIS (2011), Communications in Computer and Information Science , vol. 116 CCIS, pp. 196–207

    2011

  57. [66]

    G¨ orke, P

    R. G¨ orke, P. Maillard, C. Staudt, D. Wagner, inExperimental Algorithms. SEA 2010 , Lecture Notes in Computer Science , vol. 6049, ed. by P. Festa (Springer, Berlin, Hei- delberg, 2010), pp. 436–448

    2010

  58. [67]

    J. Xie, M. Chen, B.K. Szymanski, in Proceedings of the Workshop on Dynamic Net- works Management and Mining (ACM, New York, NY, USA, 2013), DyNetMM ’13, pp. 25–32. DOI 10.1145/2489247.2489249

    2013

  59. [68]

    Cazabet, F

    R. Cazabet, F. Amblard, C. Hanachi, in Proceedings of the 2010 IEEE Second Inter- national Conference on Social Computing (IEEE Computer Society, Washington, DC, USA, 2010), SOCIALCOM ’10, pp. 309–314. DOI 10.1109/SocialCom.2010.51

    2010 doi

  60. [69]

    D. Duan, Y. Li, R. Li, Z. Lu, Incremental k-clique clustering in dynamic so- cial networks, Artificial Intelligence Review 38(2), 129 (2012). DOI 10.1007/ s10462-011-9250-x

    2012

  61. [70]

    Falkowski, A

    T. Falkowski, A. Barth, M. Spiliopoulou, in AMCIS (2008)

    2008

  62. [71]

    Nguyen, T.N

    N.P. Nguyen, T.N. Dinh, S. Tokala, M.T. Thai, in MobiCom (2011)

    2011

  63. [72]

    Cazabet, F

    R. Cazabet, F. Amblard, in Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02 (IEEE Computer Society, Washington, DC, USA, 2011), WI-IAT ’11, pp. 402–408. DOI 10.1109/WI-IAT.2011.50

    2011 doi

  64. [73]

    G¨ orke, T

    R. G¨ orke, T. Hartmann, D. Wagner, Dynamic graph clustering using minimum-cut trees, Journal of Graph Algorithms and Applications 16, 411 (2012)

    2012

  65. [74]

    H.S. Ma, J.W. Huang, in Proceedings of the 7th Workshop on Social Network Mining and Analysis (ACM, New York, NY, USA, 2013), SNAKDD ’13, pp. 6:1–6:8. DOI 10.1145/2501025.2501026

    2013

  66. [75]

    Lee, L.V.S

    P. Lee, L.V.S. Lakshmanan, E.E. Milios, Incremental cluster evolution tracking from highly dynamic network data, 2014 IEEE 30th International Conference on Data En- gineering pp. 3–14 (2014)

    2014

  67. [76]

    Zakrzewska, D.A

    A. Zakrzewska, D.A. Bader, in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (ACM, New York, NY, USA, 2015), ASONAM ’15, pp. 559–564. DOI 10.1145/2808797.2809375

    2015

  68. [77]

    Aggarwal, P.S

    C.C. Aggarwal, P.S. Yu, Online Analysis of Community Evolution in Data Streams (SIAM, 2005), pp. 56–67. DOI 10.1137/1.9781611972757.6

    2005 doi

  69. [78]

    Zanghi, C

    H. Zanghi, C. Ambroise, V. Miele, Fast online graph clustering via erdsrnyi mixture, Pattern Recognition 41(12), 3592 (2008). DOI https://doi.org/10.1016/j.patcog.2008. 06.019

    2008 doi

  70. [79]

    Rossetti, L

    G. Rossetti, L. Pappalardo, D. Pedreschi, F. Giannotti, Tiles: an online algorithm for community discovery in dynamic social networks, Machine Learning 106(8), 1213 (2017). DOI 10.1007/s10994-016-5582-8

    2017 doi

  71. [80]

    B. Tan, F. Zhi, Q. Qu, S. Liu, in Web-Age Information Management: 15th Interna- tional Conference, WAIM 2014 (2014), pp. 633–644

    2014

  72. [81]

    Kairam, D

    S. Kairam, D. Wang, J. Leskovec, in Proceedings of the fifth ACM International Con- ference on Web Search and Data Mining (WSDM12) (2012), pp. 673–682

    2012

  73. [82]

    Patil, J

    A. Patil, J. Liu, J. Gao, in Proceedings of the 22nd International Conference on World Wide Web (WWW13) (2013), pp. 1021–1030

    2013

  74. [83]

    Goldberg, M

    M. Goldberg, M. Magdon-Ismail, S. Nambirajan, J. Thompson, in Proceedings of Pri- vacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Con- ference on Social Computing (SocialCom) (2011), pp. 780–783

    2011

  75. [84]

    Br´ odka, P

    P. Br´ odka, P. Kazienko, B. Ko loszczyk,Predicting Group Evolution in the Social Net- work (Springer, Berlin/Heidelberg, Germany, 2012), pp. 54–67

    2012

  76. [85]

    Gliwa, P

    B. Gliwa, P. Br´ odka, A. Zygmunt, S. Saganowski, P. Kazienko, J. Ko´ zlak, inProceed- ings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2013), pp. 1291–1298

    2013

  77. [86]

    Takaffoli, R

    M. Takaffoli, R. Rabbany, O. Zaiane, in Proceedings of 2013 12th International Con- ference on Machine Learning and Applications (ICMLA) (2013), pp. 191–196 40 Cherifi, Palla, Szymanski, Lu

    2013

  78. [87]

    Saganowski, B

    S. Saganowski, B. Gliwa, P. Br´ odka, A. Zygmunt, P. Kazienko, J. Ko´ zlak, Predicting community evolution in social networks, Entropy 17(5), 3053 (2015). URL https: //www.mdpi.com/1099-4300/17/5/3053

    2015

  79. [88]

    L. L¨ u, D. Chen, X.L. Ren, Q.M. Zhang, Y.C. Zhang, T. Zhou, Vital nodes identification in complex networks, Physics Reports 650, 1 (2016)

    2016

  80. [89]

    Gong, in International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014) (Atlantis Press, 2014)

    K. Gong, in International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014) (Atlantis Press, 2014)

    2014

  81. [90]

    Taghavian, M

    F. Taghavian, M. Salehi, M. Teimouri, A local immunization strategy for networks with overlapping community structure, Physica A: Statistical Mechanics and its Ap- plications 467, 148 (2017)

    2017

  82. [91]

    Lancichinetti, S

    A. Lancichinetti, S. Fortunato, F. Radicchi, Benchmark graphs for testing community detection algorithms, Physical review E 78(4), 046 (2008)

    2008

  83. [92]

    Orman, V

    G.K. Orman, V. Labatut, H. Cherifi, Towards realistic artificial benchmark for commu- nity detection algorithms evaluation, International Journal of Web Based Communities 9(3), 349 (2013)

    2013

  84. [93]

    Masuda, Immunization of networks with community structure, New Journal of Physics 11(12), 123018 (2009)

    N. Masuda, Immunization of networks with community structure, New Journal of Physics 11(12), 123018 (2009)

    2009

  85. [94]

    Mantzaris, Uncovering nodes that spread information between communities in social networks, EPJ Data Science 3(1), 26 (2014)

    A.V. Mantzaris, Uncovering nodes that spread information between communities in social networks, EPJ Data Science 3(1), 26 (2014)

    2014

  86. [95]

    Yoshida, Y

    T. Yoshida, Y. Yamada, A community structure-based approach for network immu- nization, Computational Intelligence 33(1), 77 (2017)

    2017

  87. [96]

    Jensen, M

    P. Jensen, M. Morini, M. Karsai, T. Venturini, A. Vespignani, M. Jacomy, J.P. Coin- tet, P. Merckl´ e, E. Fleury, Detecting global bridges in networks, Journal of Complex Networks 4(3), 319 (2015)

    2015

  88. [97]

    Ghalmane, M.E

    Z. Ghalmane, M.E. Hassouni, H. Cherifi, Immunization of networks with non- overlapping community structure, arXiv preprint arXiv:1806.05637 (2018)

    2018 arXiv

  89. [98]

    Kitromilidis, T.S

    M. Kitromilidis, T.S. Evans, Community detection with metadata in a network of biographies of western art painters, arXiv preprint arXiv:1802.07985 (2018)

    2018 arXiv

  90. [99]

    J.L. He, Y. Fu, D.B. Chen, A novel top-k strategy for influence maximization in com- plex networks with community structure, PloS one 10(12), e0145283 (2015)

    2015

  91. [100]

    Chan, I.X

    S.Y. Chan, I.X. Leung, P. Li` o, inProceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management (ACM, 2009), pp. 31–38

    2009

  92. [101]

    Z. Zhao, X. Wang, W. Zhang, Z. Zhu, A community-based approach to identifying influential spreaders, Entropy 17(4), 2228 (2015)

    2015

  93. [102]

    Gupta, A

    N. Gupta, A. Singh, H. Cherifi, Centrality measures for networks with community structure, Physica A: Statistical Mechanics and its Applications 452, 46 (2016)

    2016

  94. [103]

    Gupta, A

    N. Gupta, A. Singh, H. Cherifi, in Communication Systems and Networks (COM- SNETS), 2015 7th International Conference on (IEEE, 2015), pp. 1–6

    2015

  95. [104]

    M.M. Tulu, R. Hou, T. Younas, Identifying influential nodes based on community structure to speed up the dissemination of information in complex network, IEEE ACCESS 6, 7390 (2018)

    2018

  96. [105]

    S.L. Luo, K. Gong, L. Kang, Identifying influential spreaders of epidemics on commu- nity networks, CoRR abs/1601.07700 (2016)

    2016 arXiv

  97. [106]

    Salavati, A

    C. Salavati, A. Abdollahpouri, Z. Manbari, Ranking nodes in complex networks based on local structure and improving closeness centrality, Neurocomputing 336, 36 (2019)

    2019

  98. [107]

    Berahmand, A

    K. Berahmand, A. Bouyer, N. Samadi, in Computing (2018), pp. 1–23

    2018

  99. [108]

    Ghalmane, M

    Z. Ghalmane, M. El Hassouni, C. Cherifi, H. Cherifi, Centrality in modular networks, EPJ Data Science 8(1), 15 (2019)

    2019

  100. [109]

    Ghalmane, C

    Z. Ghalmane, C. Cherifi, H. Cherifi, M. El Hassouni, Centrality in complex networks with overlapping community structure, Scientific Reports 9(1) (2019)

    2019

  101. [110]

    H´ ebert-Dufresne, A

    L. H´ ebert-Dufresne, A. Allard, J.G. Young, L.J. Dub´ e, Global efficiency of local im- munization on complex networks, Scientific reports 3, 2171 (2013)

    2013

  102. [111]

    Chakraborty, A

    D. Chakraborty, A. Singh, H. Cherifi, in International Conference on Computational Social Networks (Springer, 2016), pp. 62–73

    2016

  103. [112]

    Kumar, A

    M. Kumar, A. Singh, H. Cherifi, in Companion of the The Web Conference 2018 on The Web Conference 2018 (International World Wide Web Conferences Steering Committee, 2018), pp. 1269–1275 Community structure: challenges and opportunities 41

    2018

  104. [113]

    H. Wei, Z. Pan, G. Hu, L. Zhang, H. Yang, X. Li, X. Zhou, Identifying influential nodes based on network representation learning in complex networks, PloS one 13(7), e0200091 (2018)

    2018

  105. [114]

    Y. Zhao, S. Li, F. Jin, Identification of influential nodes in social networks with com- munity structure based on label propagation, Neurocomputing 210, 34 (2016)

    2016

  106. [115]

    Raghavan, R

    U.N. Raghavan, R. Albert, S. Kumara, Near linear time algorithm to detect community structures in large-scale networks, Physical review E 76(3), 036106 (2007)

    2007

  107. [116]

    X. Lu, K. Kuzmin, M. Chen, B. Szymanski, Adaptive modularity maximization via edge weighting scheme, Information Sciences 424, 55 (2018)

    2018

  108. [117]

    Lewis, N

    A. Lewis, N. Jones, M. Porter, D. Deane, The function of communities in protein interaction networks at multiple scales, BMC Sys. Biol. 4(1), 100 (2010)

    2010

  109. [118]

    Simon, in Facets Sys

    H. Simon, in Facets Sys. Sci. (Springer, 1991), pp. 457–476

    1991

  110. [119]

    Porter, J.P

    M. Porter, J.P. Onnela, P. Mucha, Communities in networks, Notices AMS 56(9), 1082 (2009)

    2009

  111. [120]

    Lancichinetti, S

    A. Lancichinetti, S. Fortunato, Limits of modularity maximization in community de- tection, Phys. Rev. E 84(6), 066122 (2011)

    2011

  112. [121]

    M. Chen, K. Kuzmin, B. Szymanski, in Proceedings of the IEEE/ACM ASONAM, 4th Social Network Analysis and Applications (SNAA) Workshop (IEEE, 2014), pp. 856–863

    2014

  113. [122]

    Porter, M

    M. Porter, M. McDonald, S. Williams, N. Johnson, N. Jones, Dynamic communities in multichannel data: An application to the foreign exchange market during the 20072008 credit crisis, Chaos: Interdisciplinary J. Nonlinear Sci, 19(3), 033119 (2009)

    2009

  114. [123]

    Mucha, T

    P. Mucha, T. Richardson, K. Macon, M. Porter, J.P. Onnela, Community structure in time-dependent, multiscale, and multiplex networks., Science 328(5980), 876878 (2010)

    2010

  115. [124]

    Traag, G

    V. Traag, G. Krings, P. Van Dooren, Significant scales in community structure, Sci. Rep. 3, 2930 (2013)

    2013

  116. [125]

    Peel, D.B

    L. Peel, D.B. Larremore, A. Clauset, The ground truth about metadata and community detection in networks, Science Advances 3(5) (2017). DOI 10.1126/sciadv.1602548

    2017 doi

  117. [126]

    Girvan, M.E.J

    M. Girvan, M.E.J. Newman, Community structure in social and biological networks, Proc. Nat. Acad. Sci. 99(12), 7821 (2002). DOI 10.1073/pnas.122653799

    2002 doi

  118. [127]

    Granell, R.K

    C. Granell, R.K. Darst, A. Arenas, S. Fortunato, S. G´ omez, Benchmark model to assess community structure in evolving networks, Phys. Rev. E 92, 012805 (2015). DOI 10.1103/PhysRevE.92.012805

    2015 doi

  119. [128]

    Bazzi, L.G.S

    M. Bazzi, L.G.S. Jeub, A. Arenas, S.D. Howison, M.A. Porter, Generative bench- mark models for mesoscale structures in multilayer networks, CoRR abs/1608.06196 (2016). URL http://arxiv.org/abs/1608.06196

    2016 arXiv

  120. [129]

    Rossetti, RDYN: graph benchmark handling community dynamics, Journal of Com- plex Networks 5(6), 893 (2017)

    G. Rossetti, RDYN: graph benchmark handling community dynamics, Journal of Com- plex Networks 5(6), 893 (2017). DOI 10.1093/comnet/cnx016

    2017 doi

  121. [130]

    Orman, V

    G.K. Orman, V. Labatut, H. Cherifi, An empirical study of the relation between community structure and transitivity, CoRR abs/1207.3234 (2012). URL http: //arxiv.org/abs/1207.3234

    2012 arXiv

  122. [536]

    DOI 10.1145/1830483.1830580

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.