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REVIEW 5 major objections 6 minor 40 references

A model for generating temporal networks with dynamic community structure guided by mutual information

T0 review · 5 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read A generative model that turns a prescribed community-similarity trajectory into a timestamped temporal network with controlled node churn.

desk verdict A genuinely useful dynamic community generator with a real gap to fill, but the GA control loop has a concrete bug and the real-data validation is partly self-feeding; still deserves a referee. read the letter →

arxiv 2607.15855 v1 pith:W6GSK7ZV submitted 2026-07-17 cs.SI

Peijie Zhong , Raúl Mondragón , Richard Clegg This is my paper · ORCID
classification cs.SI
keywords temporalnetworksgenerativemodeldynamiccommunitiesgeneticalgorithmmutualinformationnodechurnstochasticblockcommunitydetectionbenchmark
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 paper proposes a two-stage generative model for temporal networks in which the experimenter can specify, in advance, how much the community structure changes between consecutive time steps and how many nodes are present at each step. The first stage runs a genetic algorithm that searches for a sequence of node partitions whose adjacent-step similarity, measured by a mutual-information variant that tolerates changing node sets, matches a prescribed target. The second stage converts those partitions into timestamped edges using a stochastic block model, with an inter-community connection probability bounded from below to keep the network connected. If the method works as claimed, it gives researchers a tunable null model and benchmark for testing dynamic community detection algorithms under controlled community-split rates and node churn.

What carries the argument

The load-bearing object is the union-adjusted mutual information (UAMI), a similarity score between two partitions that remains well-defined when the two node sets differ; it is used as the fitness target inside a genetic algorithm. The genetic algorithm's mutation operators are restricted to four community-evolution events — node addition, node removal, community splitting, and community merging — with add/remove probabilities adaptively adjusted toward the desired node count. A secondary but essential piece is the lower bound on the inter-community edge probability p_out, derived from Boole's inequality and a Taylor-expansion bound, which guarantees with probability at least 1-epsilon that

What would settle it

Run the genetic algorithm on a 1000-node source partition with target UAMI 0.7 and a prescribed node-count change of 20%; if, after a fixed iteration budget, the best candidate's UAMI remains below 0.6 or the node-count error exceeds 5%, the explicit-control claim for that regime is falsified.

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Extended reading notes

Core claim

The central claim is that community evolution in a temporal network can be generated to order: rather than letting similarity between snapshots emerge indirectly from hidden parameters, the model takes a target inter-snapshot similarity — the union-adjusted mutual information (UAMI) between consecutive partitions — and a target node count per snapshot as explicit inputs, then uses a genetic algorithm whose mutations are exactly the four elementary community events (node addition, node removal, community split, community merge) to find a partition sequence with the requested structure. Edges are then placed within each snapshot with intra-community and inter-community probabilities, where the

Load-bearing premise

The whole scheme rests on the genetic algorithm actually finding a partition that meets the requested similarity and node count; the paper's only evidence is two convergence runs (node additions and removals around 400–500 nodes), with no guarantee for larger or more complex settings.

Editorial extensions

If this is right

  • Researchers can generate temporal networks with a prescribed rate of community change and node churn, which existing generators control only indirectly.
  • The same generator can be configured from real data (snapshot sizes and step-to-step similarities) to produce synthetic counterparts that reproduce short-range community-evolution statistics.
  • Dynamic community detection algorithms can be stress-tested against controlled churn: the paper shows increasing churn rate degrades detection accuracy, most strongly for random-walk-based methods.
  • The model offers a memoryless baseline: because only adjacent snapshots influence each other, deviation of a real network from this baseline reveals higher-order temporal dependencies.

Reading between the lines

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

  • If the UAMI-guided genetic search scales, the same two-stage design could be applied to multilayer networks with millions of nodes by parallelising the population evaluation, a direction the paper mentions but does not implement.
  • The explicit similarity target opens a direct way to measure an algorithm's sensitivity to community-evolution speed: one could sweep target UAMI values at fixed churn and plot detection accuracy, effectively separating the effects of churn from the effects of merge/split rate.
  • Because the connectivity bound treats small communities as ignorable, the model embeds a decision about minimum community size that could be exposed as a parameter and tested for its effect on downstream detection.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper presents a two-stage generative model for temporal networks with dynamic community structure. Stage one uses a genetic algorithm to build a sequence of node partitions in which (i) adjacent partitions have a prescribed UAMI similarity (a mutual-information measure for variable node sets) and (ii) each partition has a prescribed number of nodes; split, merge, add, and remove operators are used as mutations. Stage two generates timestamped edges via a stochastic block model with intra- and inter-community probabilities p_in and p_out, where p_out may be set from a lower bound that aims to keep the network connected. The model is applied to emails, citations, subreddit interactions, and NFT transactions; it is also used as a benchmark to study how node churn and community-change rate affect dynamic community detection algorithms. The central claim is that a user can explicitly dial the inter-snapshot community similarity and the node count while preserving connectivity.

Significance. The idea of controlling temporal community similarity directly through a similarity measure that handles changing node sets is attractive, and the two-stage design cleanly separates community evolution from edge/timestamp generation. If the genetic algorithm could be shown to reliably meet its targets, the model would fill a real gap: existing multilayer generators do not guarantee a specified similarity, and dynamic SBM extensions do not explicitly model node churn. The benchmark on churn and similarity is a useful contribution. The p_out lower-bound calculation is a reasonable start for connectivity control, though the claim as stated is stronger than what the mathematics proves. The paper would benefit from a fuller, less circular validation and a more rigorous convergence study; the present evidence does not yet support the abstract's 'explicit control' assertion. The underlying architecture is sound enough to be repaired.

major comments (5)
  1. [§3.2.2] The adaptive mutation probabilities are inverted. When n(t)_{i,j} < nhat(t), the text sets premove=(nhat-n)/nhat>0 and padd=0, so only removals are applied, driving the partition away from the target; when n>nhat, only additions occur. This contradicts the stated purpose and the convergence results in Figure 9, which cannot be produced by the algorithm as written. Please correct the formulas and confirm that the experiments were run with the corrected rule.
  2. [§3.3] The text says the bound 'guarantees global connectivity with high probability', but the union-bound argument only bounds the probability that some pair of communities has zero inter-community edges. It does not address isolated nodes within a community, and an edge between every community pair does not make the graph connected. Please revise the claim: at most this is a sufficient condition for no completely disconnected community pairs. If global connectivity is needed, add a condition on p_in or a per-node degree check.
  3. [§4.2] The real-data validation is partly self-referential. The UAMI similarities and node counts extracted from real networks are used as direct inputs to the generator, and the synthetic network is then evaluated by running Louvain and recomputing UAMI on the detected partitions. If the planted communities are detectable, the comparison is largely by construction. Please add a baseline control (e.g., randomizing the target similarities) and report the recovery accuracy of the planted partitions; preferably also compare statistics other than the fitted quantities.
  4. [§4.4] The only evidence for the central 'explicit control' claim is two single-run convergence experiments (Figure 9) for one similarity value (0.5) and two node-count transitions. There are no repeated runs, error bars, scalability tests, or failure cases. Moreover, the experiments do not verify that the final partitions realize the intended split/merge events rather than reaching the UAMI target through label shuffling or node churn. Please provide a systematic GA evaluation across the parameter ranges used in §4.3 and discuss failure modes.
  5. [§4.3] The definition of μ is inconsistent with the following sentence. μ=M_inter/(M_intra+M_inter) implies M_intra/M_inter=(1−μ)/μ, but the text states the ratio is μ/(1−μ). This inversion would change the interpretation of the benchmark sweeps. Please correct the formula and verify that the implementation matches the intended definition.
minor comments (6)
  1. [§4.4] Figure 9 caption: 'shrank' should be 'shrunk'.
  2. [§2] In the D-DCSBM paragraph, 'yet it the model treats' is a typo; also 'absence nodes' should be 'absent nodes'.
  3. [§3.3] The line 'will makes the network broken' is ungrammatical. Also, the 'Taylor expansion' label is misleading: the derivation uses the expansion of −ln(1−p), not a Taylor approximation of the probability itself.
  4. [§3.3] The threshold for ignoring small communities is derived as if the two communities have similar size; clarify that the condition is on the product n_i n_j, and state how 'ignoring' is implemented.
  5. [§4.2] 'empty model' should be 'null model'. Figure 3 contains duplicated text for 'Pair-to-pair similarity in community structures in the real temporal network'.
  6. [§3.2] The abstract's 'explicit control of community evolution including splits and merges' is stronger than what the model actually offers: the user can prescribe the UAMI similarity and node counts, but not the sequence or type of split/merge events. Please temper the wording.

Circularity Check

1 steps flagged · score 4.0 of 10

Real-data validation is partly by construction: the GA is given the real UAMI trajectory and node counts as targets, then the same UAMI is reported as a match; the rest of the pipeline is not circular.

  1. fitted input called prediction [Section 4.2 ('Simulation') and Figure 3 workflow]
    "These parameters, the number of nodes in each snapshot and the similarity of community structures across consecutive snapshots are then provided as inputs to a genetic algorithm–guided community structure generator. Edges are subsequently inserted according to the the dynamic partitions generated by the genetic algorithm, and parameters p (t) in , p (t) out , and the link–node ratio are extracted from the real data. Finally, we apply the same community detection algorithm to the generated network and compute the step-to-step similarity of its community structures for comparison."

    The step-to-step UAMI values used for comparison are the same values supplied to the GA as optimization targets. The fitness function (Section 3.2.3) is 1 - |UAMI(C_ij,C_prev)-s| - lambda*|n-nhat|/nhat, so matching s (and nhat) is the objective being optimized, not an independent prediction of the model. Therefore the claim that the model 'effectively captures the community evolution characteristics' at short range is enforced by construction. Node counts are likewise inputs. The long-range (non-adjacent) UAMI behavior is not input and remains an emergent, non-circular result.

full rationale

The core generative pipeline (GA search, SBM edge insertion, connectivity bound in Section 3.3) is not circular. The lower bound for p_out is derived self-containedly from Boole's inequality and a Taylor expansion. The benchmark experiments in Section 4.3 compare detector output to ground-truth labels via mAMI and do not reuse the fitted values as predictions. The convergence tests in Section 4.4 only show that the optimizer can reach its own objective in two small cases, which is weak evidence of the 'explicit control' claim but is not a circularity. The only substantial circularity is in the real-data validation of Section 4.2: the empirical UAMI trajectory and node counts are extracted, fed into the GA as targets, and then re-detected and compared on the same UAMI measure, making the short-range agreement by construction. This raises the score to 4. The apparent inversion of premove/padd in Section 3.2.2 is a correctness issue separate from circularity and is noted here for completeness rather than scored.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The load-bearing inputs are GA hyperparameters, per-snapshot edge probabilities fitted to real data, and the UAMI metric from the authors' prior work. The first-order Markov assumption and hard-partition representation are explicit domain choices that constrain the model's scope.

free parameters (8)
  • λ fitness balance weight = not specified
    Balances UAMI similarity error against node-count error in the GA fitness function; no value or sensitivity analysis given.
  • Dirichlet concentration α = not specified
    Controls the initial community-mixing proportions in Section 3.1; no value given.
  • GA population size r and number of iterations = not specified
    Genetic algorithm search parameters are never listed; reproducibility depends on them.
  • Mutation probabilities for add/remove/split/merge = not specified
    The four mutation operators in Section 3.2.2 require rates that are not reported.
  • p_in and p_out per snapshot = extracted from real data in simulations
    In Section 4.2 these are 'extracted from the real data' and then used to generate edges, fitting the model to the data being reproduced.
  • Poisson edge rate γ(t) / link-node ratio = extracted from real data
    Controls timestamp density and is set to match the target network's link-node ratio.
  • Connectivity tolerance ε = not specified
    Used in the p_out lower bound; no numerical value or sensitivity analysis.
  • Benchmark parameters similarity, μ, churn = varied, ranges not fully stated
    Section 4.3 varies these to generate Figure 8, but the exact grid of values is not reported in text.
assumptions (6)
  • domain assumption UAMI is a valid similarity measure for partitions on different node sets and behaves appropriately under splits, merges, and node churn.
    The whole GA fitness function relies on UAMI from the authors' prior paper [39]; its suitability is not independently tested here.
  • domain assumption The genetic algorithm can find partitions satisfying prescribed UAMI similarity and node count.
    Only empirically demonstrated in two small convergence experiments (Figure 9), with no guarantee for larger or more complex settings.
  • domain assumption Communities are adequately represented as hard partitions at each snapshot.
    The model assumes a single community label per node; no overlapping or mixed membership is considered.
  • domain assumption Edge generation follows a stochastic block model with only two probabilities, p_in and p_out.
    Section 3.3 uses this to construct edges; degree heterogeneity is only added in the benchmark via a degree-correction sequence.
  • domain assumption The community structure evolves as a first-order Markov process.
    Explicitly stated in the Discussion: the partition at time t depends only on t-1. This limits the model's ability to reproduce long-term memory.
  • domain assumption Inter-event times can be modeled by a Poisson process.
    Used for timestamp generation; the authors acknowledge in the Discussion that self-exciting point processes might be needed for bursty dynamics.

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Cite this review

Pith. "Pith review of A model for generating temporal networks with dynamic community structure guided by mutual information." pith.science (2026). https://pith.science/paper/W6GSK7ZV

@misc{pith2026260715855,
  author       = {Pith},
  title        = {Pith review of: A model for generating temporal networks with dynamic community structure guided by mutual information},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W6GSK7ZV}},
  note         = {Machine review of arXiv:2607.15855}
}
read the original abstract

This paper introduces a generative model for temporal networks that jointly controls community evolution and dynamic node sets. The model represents community structure as a sequence of partitions and uses a genetic search guided by a similarity measure based on mutual information to regulate changes between snapshots. This allows explicit control of community evolution including splits and merges while handling node additions and removals. Temporal edges are then generated using intra- and inter-community probabilities derived from data or theoretical bounds to ensure connectivity. Simulation experiments on real-world datasets demonstrate the ability of the generative model to model the evolution of real dynamic communities. The model is used as a benchmark to study the impact of the rate at which nodes join/leave the network on the performance of dynamic community detection algorithms.

Figures

Figures reproduced from arXiv: 2607.15855 by the authors.

Figure 1
Figure 1. The figure demonstrate the work flow of the generative model: the first step is applying a [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. We consider multiple prescribed similarity levels between consecutive time steps, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. This figure shows our workflow for synthesising a temporal network that simulates a real one. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The figure demonstrates the similarity of community structures between pairs of time steps [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: The figure demonstrates the similarity of community structures between pairs of time steps [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The figure demonstrates the similarity of community structures between pairs of time steps [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: The figure demonstrates the similarity of community structures between pairs of time steps [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: The figure illustrates the similarity between the detected community structures and the ground [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: The model converges to the given community structure similarity and the number of nodes [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

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Reference graph

Works this paper leans on

40 extracted references · 1 linked inside Pith

  1. [1]

    Community detection and stochastic block models: recent developments.Journal of Machine Learning Research, 18(177):1–86, 2018

    Emmanuel Abbe. Community detection and stochastic block models: recent developments.Journal of Machine Learning Research, 18(177):1–86, 2018

    2018

  2. [2]

    Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery

    Emmanuel Abbe and Colin Sandon. Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery. In2015 IEEE 56th Annual Symposium on Foundations of Computer Science, pages 670–688. IEEE, 2015

    2015

  3. [3]

    Mixed membership stochastic block- models.Advances in neural information processing systems, 21, 2008

    Edo M Airoldi, David Blei, Stephen Fienberg, and Eric Xing. Mixed membership stochastic block- models.Advances in neural information processing systems, 21, 2008. 15

    2008

  4. [4]

    Generating temporal contact graphs using random walkers.IEEE Transactions on Network Science and Engineering, 2025

    Anton-David Almasan, Sergey Shvydun, Ingo Scholtes, and Piet Van Mieghem. Generating temporal contact graphs using random walkers.IEEE Transactions on Network Science and Engineering, 2025

    2025

  5. [5]

    Constrained information flows in temporal networks reveal intermittent communities.Physical Review E, 97(6):062312, 2018

    Ulf Aslak, Martin Rosvall, and Sune Lehmann. Constrained information flows in temporal networks reveal intermittent communities.Physical Review E, 97(6):062312, 2018

    2018

  6. [6]

    A framework for the construction of generative models for mesoscale structure in multilayer networks.Physical Review Research, 2(2):023100, 2020

    Marya Bazzi, Lucas GS Jeub, Alex Arenas, Sam D Howison, and Mason A Porter. A framework for the construction of generative models for mesoscale structure in multilayer networks.Physical Review Research, 2(2):023100, 2020

    2020

  7. [7]

    Community detection in temporal multilayer networks, with an application to correlation networks

    Marya Bazzi, Mason A Porter, Stacy Williams, Mark McDonald, Daniel J Fenn, and Sam D Howison. Community detection in temporal multilayer networks, with an application to correlation networks. Multiscale Modeling & Simulation, 14(1):1–41, 2016

    2016

  8. [8]

    Clan: An efficient distributed temporal community detection protocol for manets

    Stephen Dabideen, Vikas Kawadia, and Samuel C Nelson. Clan: An efficient distributed temporal community detection protocol for manets. In2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, pages 91–99. IEEE, 2014

    2014

Show all 40 references
  1. [9]

    Community detection in sparse time- evolving graphs with a dynamical bethe-hessian.Advances in Neural Information Processing Sys- tems, 33:7486–7497, 2020

    Lorenzo Dall’Amico, Romain Couillet, and Nicolas Tremblay. Community detection in sparse time- evolving graphs with a dynamical bethe-hessian.Advances in Neural Information Processing Sys- tems, 33:7486–7497, 2020

    2020

  2. [10]

    Tigger: Scalable generative modelling for temporal interaction graphs

    Shubham Gupta, Sahil Manchanda, Srikanta Bedathur, and Sayan Ranu. Tigger: Scalable generative modelling for temporal interaction graphs. InProceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 6819–6828, 2022

    2022

  3. [11]

    Anomaly detection in blockchain networks: A comprehensive survey.IEEE Communications Surveys & Tutorials, 25(1):289–318, 2022

    Muneeb Ul Hassan, Mubashir Husain Rehmani, and Jinjun Chen. Anomaly detection in blockchain networks: A comprehensive survey.IEEE Communications Surveys & Tutorials, 25(1):289–318, 2022

    2022

  4. [12]

    Stochastic blockmodels: First steps.Social networks, 5(2):109–137, 1983

    Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. Stochastic blockmodels: First steps.Social networks, 5(2):109–137, 1983

    1983

  5. [13]

    Temporal graph benchmark for machine learning on temporal graphs.Advances in Neural Information Processing Systems, 36:2056–2073, 2023

    Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, and Reihaneh Rabbany. Temporal graph benchmark for machine learning on temporal graphs.Advances in Neural Information Processing...

  6. [14]

    A survey of community detection methods in multilayer networks.Data Mining and Knowledge Discovery, 35:1–45, 2021

    Xinyu Huang, Dongming Chen, Tao Ren, and Dongqi Wang. A survey of community detection methods in multilayer networks.Data Mining and Knowledge Discovery, 35:1–45, 2021

    2021

  7. [15]

    Dynamic infinite re- lational model for time-varying relational data analysis.Advances in Neural Information Processing Systems, 23, 2010

    Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, and Joshua Tenenbaum. Dynamic infinite re- lational model for time-varying relational data analysis.Advances in Neural Information Processing Systems, 23, 2010

    2010

  8. [16]

    Temporal community detec- tion based on symmetric nonnegative matrix factorization.International Journal of Modern Physics B, 31(13):1750102, 2017

    Pengfei Jiao, Haodong Lyu, Xiaoming Li, Wei Yu, and Wenjun Wang. Temporal community detec- tion based on symmetric nonnegative matrix factorization.International Journal of Modern Physics B, 31(13):1750102, 2017

    2017

  9. [17]

    Illegal community detection in bitcoin transaction networks.Entropy, 25(7):1069, 2023

    Dany Kamuhanda, Mengtian Cui, and Claudio J Tessone. Illegal community detection in bitcoin transaction networks.Entropy, 25(7):1069, 2023

    2023

  10. [18]

    Stochastic blockmodels and community structure in networks

    Brian Karrer and Mark EJ Newman. Stochastic blockmodels and community structure in networks. Physical Review E, 83(1):016107, 2011

    2011

  11. [19]

    Effects of missing data in social networks.Social networks, 28(3):247–268, 2006

    Gueorgi Kossinets. Effects of missing data in social networks.Social networks, 28(3):247–268, 2006

    2006

  12. [20]

    Adjusting for network size and com- position effects in exponential-family random graph models.Statistical methodology, 8(4):319–339, 2011

    Pavel N Krivitsky, Mark S Handcock, and Martina Morris. Adjusting for network size and com- position effects in exponential-family random graph models.Statistical methodology, 8(4):319–339, 2011

    2011

  13. [21]

    Restricted set classification: Who is there?Pattern Recognition, 63:158–170, 2017

    Ludmila I Kuncheva, Juan J Rodriguez, and Aaron S Jackson. Restricted set classification: Who is there?Pattern Recognition, 63:158–170, 2017. 16

    2017

  14. [22]

    Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.Physical Review E, 80(1):016118, 2009

    Andrea Lancichinetti and Santo Fortunato. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.Physical Review E, 80(1):016118, 2009

    2009

  15. [23]

    Benchmark graphs for testing com- munity detection algorithms.Physical Review E, 78(4):046110, 2008

    Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi. Benchmark graphs for testing com- munity detection algorithms.Physical Review E, 78(4):046110, 2008

    2008

  16. [24]

    Multilayer financial complex networks and their applications.IEEE Transactions on Circuits and Systems I: Regular Papers, 69(10):4103–4116, 2022

    Xuerong Li, Xiaoyue Xu, Jiaqi Liu, Jichang Dong, and Jinhu L¨ u. Multilayer financial complex networks and their applications.IEEE Transactions on Circuits and Systems I: Regular Papers, 69(10):4103–4116, 2022

    2022

  17. [25]

    Facetnet: a framework for analyzing communities and their evolutions in dynamic networks

    Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L Tseng. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. InProceedings of the 17th international conference on World Wide Web, pages 685–694, 2008

    2008

  18. [26]

    Evolution of communities in dynamic social networks: An efficient map-based approach.Expert Systems with Applications, 147:113221, 2020

    Kaveh Kadkhoda Mohammadmosaferi and Hassan Naderi. Evolution of communities in dynamic social networks: An efficient map-based approach.Expert Systems with Applications, 147:113221, 2020

    2020

  19. [27]

    Com- munity structure in time-dependent, multiscale, and multiplex networks.science, 328(5980):876–878, 2010

    Peter J Mucha, Thomas Richardson, Kevin Macon, Mason A Porter, and Jukka-Pekka Onnela. Com- munity structure in time-dependent, multiscale, and multiplex networks.science, 328(5980):876–878, 2010

    2010

  20. [28]

    Mapping the NFT revolution: market trends, trade networks, and visual features.Scientific reports, 11(1):20902, 2021

    Matthieu Nadini, Laura Alessandretti, Flavio Di Giacinto, Mauro Martino, Luca Maria Aiello, and Andrea Baronchelli. Mapping the NFT revolution: market trends, trade networks, and visual features.Scientific reports, 11(1):20902, 2021

    2021

  21. [29]

    Relating modularity maximization and stochastic block models in multilayer networks.SIAM Journal on Mathematics of Data Science, 1(4):667–698, 2019

    A Roxana Pamfil, Sam D Howison, Renaud Lambiotte, and Mason A Porter. Relating modularity maximization and stochastic block models in multilayer networks.SIAM Journal on Mathematics of Data Science, 1(4):667–698, 2019

    2019

  22. [30]

    Motifs in temporal networks

    Ashwin Paranjape, Austin R Benson, and Jure Leskovec. Motifs in temporal networks. InPro- ceedings of the tenth ACM international conference on web search and data mining, pages 601–610, 2017

    2017

  23. [31]

    Nonparametric weighted stochastic block models.Physical Review E, 97(1):012306, 2018

    Tiago P Peixoto. Nonparametric weighted stochastic block models.Physical Review E, 97(1):012306, 2018

    2018

  24. [32]

    inferential community detection in networks: Pitfalls, myths and half-truths

    Tiago P Peixoto.Descriptive vs. inferential community detection in networks: Pitfalls, myths and half-truths. Cambridge University Press, 2023

    2023

  25. [33]

    Rossi and Nesreen K

    Ryan A. Rossi and Nesreen K. Ahmed. The network data repository with interactive graph analytics and visualization. InProceedings of the AAAI conference on Artificial Intelligence, 2015

    2015

  26. [34]

    Raphtory: The temporal graph engine for rust and python.arXiv preprint arXiv:2306.16309, 2023

    Ben Steer, Naomi Arnold, Cheick Tidiane Ba, Renaud Lambiotte, Haaroon Yousaf, Lucas Jeub, Fabian Murariu, Shivam Kapoor, Pedro Rico, Rachel Chan, et al. Raphtory: The temporal graph engine for rust and python.arXiv preprint arXiv:2306.16309, 2023

    2023 arXiv

  27. [35]

    Tracking the evolution of com- munity structures in time-evolving social networks

    Etienne Gael Tajeuna, Mohamed Bouguessa, and Shengrui Wang. Tracking the evolution of com- munity structures in time-evolving social networks. In2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 1–10. IEEE, 2015

    2015

  28. [36]

    A tutorial on spectral clustering.Statistics and computing, 17(4):395–416, 2007

    Ulrike Von Luxburg. A tutorial on spectral clustering.Statistics and computing, 17(4):395–416, 2007

    2007

  29. [37]

    Modeling and detecting change in temporal networks via the degree corrected stochastic block model.Quality and Reliability Engi- neering International, 35(5):1363–1378, 2019

    James D Wilson, Nathaniel T Stevens, and William H Woodall. Modeling and detecting change in temporal networks via the degree corrected stochastic block model.Quality and Reliability Engi- neering International, 35(5):1363–1378, 2019

    2019

  30. [38]

    Dymond: Dynamic motif-nodes network generative model

    Giselle Zeno, Timothy La Fond, and Jennifer Neville. Dymond: Dynamic motif-nodes network generative model. InProceedings of the Web Conference 2021, pages 718–729, 2021

    2021

  31. [39]

    Quantifying community evolution in temporal networks.arXiv preprint arXiv:2411.10632, 2024

    Peijie Zhong, Cheick Ba, Ra´ ul Mondrag´ on, and Richard Clegg. Quantifying community evolution in temporal networks.arXiv preprint arXiv:2411.10632, 2024. 17

    2024

  32. [40]

    A data-driven graph generative model for temporal interaction networks

    Dawei Zhou, Lecheng Zheng, Jiawei Han, and Jingrui He. A data-driven graph generative model for temporal interaction networks. InProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 401–411, 2020. 18

    2020

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.