Pith. sign in

REVIEW 3 major objections 4 minor 54 references

Crowd: A Social Network Simulation Framework

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read Crowd is a Python framework that lets researchers build agent-based social network simulations from a YAML configuration file, and the paper claims this makes model development faster without slowing execution.

desk verdict Crowd is a genuinely usable framework with honest scoping; the speed-up claim is the weakest link and needs better evidence, but the tool itself deserves review. read the letter →

arxiv 2412.10781 v3 pith:CNPTODZW submitted 2024-12-14 cs.SI

Ann Nedime Nese Rende , Tolga Yilmaz , Özgür Ulusoy This is my paper · ORCID
classification cs.SI
keywords agent-basedmodelingsocialnetworksimulationYAMLconfigurationdiffusionmodelsgenerativeagentsinfluencemaximizationtrustgamesframework
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

The paper presents Crowd, a Python framework for agent-based simulations on social networks, and claims it lowers the effort needed to build, run, and analyze such simulations. Instead of writing model and agent classes from scratch, a user describes the network, node types, parameters, and diffusion rules in a YAML configuration file, optionally adding custom Python methods for study-specific logic. Crowd also supplies interactive network visualization, automatic data collection, chart drawing, and merging of results across runs. The authors demonstrate the framework on three diverse studies—a generative-agent epidemic model, influence maximization on a large Facebook network, and an evolutionary trust game—and report execution times comparable to a general-purpose baseline while requiring less setup code. The point of the paper is that a network-focused, config-driven design can make social network simulation accessible without sacrificing performance.

What carries the argument

The load-bearing mechanism is Crowd's class hierarchy of networks, anchored by a base Network class and a CustomSimNetwork subclass that stores node and edge parameters and executes user-supplied methods at designated points in each iteration. A DiffusionNetwork subclass integrates a compartment model so that state transitions such as Susceptible to Infected to Recovered can be declared as YAML rules instead of programmed. Configuration files, the GUI, and the Python library all drive the same simulation engine, with a standard graph data structure as the underlying representation and the frame's own data savers writing JSON snapshots at chosen intervals.

What would settle it

Run the same three studies in Crowd and in a general-purpose Python agent-based framework, using independently written, comparably optimized code, with many repetitions and reported variance; if the general-purpose framework matches or beats Crowd on most workloads and the total setup time is not clearly lower, the paper's central promise fails.

Watch

Extended reading notes

Core claim

The central claim is that Crowd makes agent-based modeling on networks simpler and faster to develop by moving simulation setup out of code and into declarative configuration. A researcher selects the network structure, node types, node and edge parameters, and—for diffusion tasks—compartment-based rules in YAML; the framework then handles iteration scheduling, snapshotting, data saving, and visualization. For cases the built-in diffusion engine cannot express, Crowd's custom simulation network lets the user attach Python methods that run before, during, or after each iteration, with return values automatically persisted. The paper's three case studies are offered as evidence that this design covers enough ground to reproduce published results from epidemic modeling, influence maximization, and trust games, and its timing tables are offered as evidence that the added conveniences do not cost runtime. In short, the authors claim to have separated the reusable mechanics of network simulation from the study-specific logic.

Load-bearing premise

The claimed time savings rest on the comparison with a general-purpose baseline being fair, meaning the baseline implementations must be reasonably optimized and the chosen case studies representative, and on the reported averages not being noise, since no variance is given.

Editorial extensions

If this is right

  • A social scientist who can write a YAML file can run a diffusion simulation and inspect interactive charts without writing any simulation-loop code.
  • Research teams that currently reuse boilerplate model and agent classes will cut setup effort, since configuration files and automatic data saving replace repetitive scaffolding.
  • Because user methods run inside Python, existing data-analysis and machine-learning libraries can be called directly from simulations, including LLM-based generative agents.
  • The framework's built-in centrality-based seed selection and result merging make influence-maximization experiments faster to iterate.
  • The three reproduced case studies suggest the abstractions are general enough to cover epidemics, information diffusion, and evolutionary games, so new studies in those areas can start from a configuration rather than from scratch.

Reading between the lines

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

  • If the configuration approach catches on, one testable prediction is that published network-diffusion results will become easier to replicate, because the simulation logic is declared in a machine-readable file rather than embedded in bespoke code.
  • The generative-agent epidemic case hints at a broader pattern: Crowd could serve as a testbed for comparing LLM-driven and rule-based agents under identical network topologies, since only the decision method changes.
  • The paper's timing claims ignore variance; a natural extension would be a benchmark suite with confidence intervals across more datasets to see whether Crowd's edge persists.
  • Whether the no-code diffusion layer can express arbitrary compartment models, or only a fixed subset, limits how far the 'no code' promise generalizes; a community-contributed library of YAML examples would test that boundary.
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 / 4 minor

Summary. The paper introduces Crowd, a Python-based agent-based modeling and simulation framework specialized for social networks. The framework provides YAML-based configuration for simulation setup, no-code diffusion modeling via NDLib-style compartments, interactive network visualization, automatic data collection, and a Tauri-based desktop GUI. The authors demonstrate Crowd through three case studies: an LLM-driven epidemic simulation with generative agents, an influence-maximization scenario on real social networks, and a networked N-player trust game. They also compare Crowd with the general-purpose framework Mesa in terms of modeling effort, execution time, and visualization facilities, reporting execution-time tables for the three case studies. The central claim is that Crowd simplifies and accelerates the development of agent-based network simulations.

Significance. If the claims are substantiated, Crowd is a useful open-source contribution: it targets a genuine gap—general-purpose ABMS tools require repetitive boilerplate for network simulations—and it demonstrates concrete integrations with external libraries (NetworkX, igraph, Hugging Face Transformers) and with real datasets (Facebook, GitHub, Twitch). The case studies are nontrivial and show that the framework can express models from the literature with relatively little code. The paper is honest about its scope (single-threaded, small-to-medium scale) and identifies limitations and future work. The main unproven part is the quantitative 'fasten' claim: the execution-time comparison against Mesa lacks reproducibility artifacts, variance reporting, and statistical testing, and it contains at least one internal contradiction. The framework's qualitative advantages (configuration-driven setup, automatic data saving, interactive GUI) are plausible even without the timing results.

major comments (3)
  1. [§VI-B-2, Tables II-IV] The execution-time comparison is the quantitative backbone of the 'fasten' claim, but it is reported only as averages of 5 runs with no variance, no per-run values, and no statistical test. Differences on the order of 10–20%, as in Table II (107.07 min vs 95.55 min) and Table IV (38.03 s vs 24.24 s), could plausibly fall within run-to-run noise, especially for the GPU-based LLM scenario. Moreover, Table III shows Crowd is slower than Mesa on Facebook (5.19 s vs 4.62 s), which contradicts the summary statement that 'Crowd takes less time in most simulations' only if that one case is treated as noise. Please report standard deviations or full distributions, state whether the differences are statistically significant, and reconcile the Facebook row with the claimed advantage.
  2. [§V-A vs §VI-B-2] There is an unexplained inconsistency in the reported LLM inference time: Section V-A states each query takes 'approximately 10–10.5 seconds' and a 50-day, 100-agent experiment with 5000 queries takes 857 minutes, while Section VI-B-2 states 'each query taking around 6 seconds on average' for the same scenario in Table II. These numbers cannot both describe the same setup unless the hardware or model settings changed, and the paper does not say so. This discrepancy undermines the reliability of the Table II timing comparison and needs to be resolved explicitly (e.g., different GPU, different quantization, different prompt length).
  3. [§VI-B (all), reproducibility] The Mesa baseline implementations are not archived or documented in enough detail for a reader to verify that they are faithful, fairly optimized, and equivalent in functionality to the Crowd implementations. Without the benchmark code (with a fixed commit or version) and a clear description of how the Mesa models were written, the comparison is unverifiable, and the workload may have been shaped around Crowd's abstractions. Please make the benchmark scripts publicly available and include a precise specification of the Mesa models and their configuration, so that the claim 'fasten ... development' can be independently checked.
minor comments (4)
  1. [§V-C, Figure 11 caption] The caption for Figure 11 says 'n = 1000' while the text in Section V-C states the Scale-Free network has 1024 nodes; please correct the caption or the text to be consistent.
  2. [§V-A] The text refers to 'William et al.' but the reference [34] is Williams et al.; the name is misspelled in the body text.
  3. [Throughout] YAML is rendered as 'Y AML' in multiple places (e.g., Sections I, III, and Table I). This appears to be a formatting artifact, but it should be fixed as a single token 'YAML'.
  4. [Table I] The tool name 'MASON' is written as 'Mason' in the first column; use the official capitalization consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: Crowd's claims are supported by external case-study implementations and measured comparisons, not by self-referential derivation.

full rationale

The paper makes no mathematical derivation whose conclusion is equivalent to its premises. It presents Crowd as a tool and supports its usability claims with three case studies that re-implement external models (Williams et al., Kempe et al., Chica et al.); the closest thing to a validation, the trust-game comparison in Section V-C, is checked against the original study's published results, an external anchor, and the generative-agents case explicitly disclaims direct comparison with the original grid-based study because the network settings differ. The only comparative performance evidence, Section VI-B-2 and Tables II-IV, is a measured runtime benchmark against Mesa; even if the Mesa baselines are not archived and variance is not reported, that is a reproducibility and evidence-quality concern, not a circular reduction, because the runtimes are independent measurements rather than quantities derived from Crowd's own definitions. I found no self-citation chain, no imported uniqueness theorem, and no parameter fitted to a subset of the data and then renamed as a prediction. The mild self-referentiality of using Crowd to demonstrate Crowd is inherent to tool presentation and does not make any claim equivalent to its input.

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

No new physical entities or fitted parameters are introduced. The framework relies on standard domain models and data structures from prior work; the central claim is about software usability, not about a new scientific law.

assumptions (3)
  • domain assumption Network representation of social interactions is sufficient for the studied phenomena
    Crowd models social systems as static or dynamic networks; this is invoked throughout the architecture in Section III-A and the case studies.
  • domain assumption NDLib compartment abstractions cover the diffusion dynamics of the case studies
    Used in Section IV-2 and the case studies to express SIR and IC models without custom code.
  • domain assumption Proportional imitation and trust-game payoff models from Chica et al. are valid for networked populations
    Adopted directly in Section V-C; the framework does not validate these domain models.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Crowd: A Social Network Simulation Framework." pith.science (2026). https://pith.science/paper/CNPTODZW

@misc{pith2026241210781,
  author       = {Pith},
  title        = {Pith review of: Crowd: A Social Network Simulation Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CNPTODZW}},
  note         = {Machine review of arXiv:2412.10781}
}
read the original abstract

To observe how individual behavior shapes a larger community's actions, agent-based modeling and simulation (ABMS) has been widely adopted by researchers in social sciences, economics, and epidemiology. While simulations can be run on general-purpose ABMS frameworks, these tools are not specifically designed for social networks and, therefore, provide limited features, increasing the effort required for complex simulations. In this paper, we introduce Crowd, a social network simulator that adopts the agent-based modeling methodology to model real-world phenomena within a network environment. Designed to facilitate easy and quick modeling, Crowd supports simulation setup through YAML configuration and enables further customization with user-defined methods. Other features include no-code simulations for diffusion tasks, interactive visualizations, data aggregation, and chart drawing facilities. Designed in Python, Crowd also supports generative agents and connects easily with Python's libraries for data analysis and machine learning. Finally, we include three case studies to illustrate the use of the framework, including generative agents in epidemics, influence maximization, and networked trust games.

Figures

Figures reproduced from arXiv: 2412.10781 by the authors.

Figure 1
Figure 1. Architecture of the Crowd framework. A. Network architecture In the other ABMS frameworks, such as Mesa and MASON, two classes are expected to be implemented by the user, Model and Agent. Crowd adopts a different approach. In the current version, the only environment that the simulation can reside on is a network. This network environment consists of nodes and edges, where nodes are the agents and edges define the r… view at source ↗
Figure 2
Figure 2. Simulation development and analysis steps of the Crowd framework. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Rule addition in the Crowd GUI for the SIR example. (a) Adding a [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Network and Results sections of Crowd’s GUI. (a) Network tab. (b) Results tab. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Charts generated with Crowd for the generative agents experiment. (a) Agents staying at home. (b) Agents on the fourth day of infection. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Execution order of a full iteration in the generative agents experiment. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Network visualization of the generative agents experiment: [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Network visualization of the IM experiment: [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: IM: (a) Node counts (seed node selection with PageRank) (b) Comparison of total activation over time (seed node selection with PageRank, Degree, [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: N-player trust game: (a) Averaged number of investors (I), trustworthy trustees (T), untrustworthy trustees (U). (b) Averaged global net wealth. Over [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Network visualization of the trust game experiment: [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 46 canonical work pages

  1. [1]

    Zhang, J

    Q. Zhang, J. Liu, B. Liu, and G. Wang, ”Market adoption simulation of electric vehicle based on social network model considering nudge policies,” Energy, vol. 259, 2022, pp. 124984

    work page 2022

  2. [2]

    M. A. Zaffar, R. L. Kumar, and K. Zhao, ”Using agent-based modelling to investigate diffusion of mobile-based branchless banking services in a developing country,” Decis. Support Syst. , vol. 117, 2019, pp. 62–74

    work page 2019

  3. [3]

    R. Hou, J. Wu, and H. S. Du, ”Customer social network affects marketing strategy: a simulation analysis based on competitive diffusion model,” Physica A, vol. 469, 2017, pp. 644–653

    work page 2017

  4. [4]

    Stroeymeyt, A

    N. Stroeymeyt, A. V . Grasse, A. Crespi, D. P. Mersch, S. Cremer and L. Keller, ”Social network plasticity decreases disease transmission in a eusocial insect,” Science, vol. 362, no. 6417, 2018, pp. 941–945

    work page 2018

  5. [5]

    Block et al., ”Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world,” Nature Human Behav., vol

    P. Block et al., ”Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world,” Nature Human Behav., vol. 4, no. 6, 2020, pp. 588–596

    work page 2020

  6. [6]

    Lv et al., ”Agent-based campus novel coronavirus infection and control simulation,” IEEE Trans

    P. Lv et al., ”Agent-based campus novel coronavirus infection and control simulation,” IEEE Trans. Comput. Social Syst. , vol. 9, no. 3, 2021, pp. 688–699

    work page 2021

  7. [7]

    B. Ross, L. Pilz, B. Cabrera, F. Brachten, G. Neubaum and S. Stieglitz, ”Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks,” Eur. J. Inf. Syst. , vol. 28, no. 4, 2019, pp. 394–412

    work page 2019

  8. [8]

    Gao and Z

    Y . Gao and Z. Zhang, ”Consensus reaching with non-cooperative be- havior management for personalized individual semantics-based social network group decision making,” J. Oper. Res. Soc. , vol. 73, no. 11, 2021, pp. 2518–2535

    work page 2021

Show all 54 references
  1. [9]

    Devari, A

    A. Devari, A. G. Nikolaev and Q. He, ”Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers,” Transp. Res. E , vol. 105, 2017, pp. 105–122

    2017

  2. [10]

    Trivedi, S

    A. Trivedi, S. Rao, ”Agent-based modeling of emergency evacuations considering human panic behavior,” IEEE Trans. Comput. Social Syst. , vol. 5, no. 1, 2018, pp. 277–288

    2018

  3. [11]

    C. M. Macal, ”Everything you need to know about agent-based mod- elling and simulation,” J. Simul., vol. 10, no. 2, 2016, pp. 144–156

    2016

  4. [12]

    Antelmi, G

    A. Antelmi, G. Cordasco, G. D’Ambrosio, D. De Vinco and C. Spag- nuolo, ”Experimenting with agent-based model simulation tools,” Appl. Sci., vol. 13, no. 1, 2022, pp. 13

    2022

  5. [13]

    Tisue and U

    S. Tisue and U. Wilensky, ”Netlogo: A simple environment for modeling complexity,” in Proc. Int. Conf. Complex Syst., 2004, vol. 21, pp. 16–21

    2004

  6. [14]

    S. Luke, C. Cioffi-Revilla, L. Panait, K. Sullivan, and G. Balan, ”Mason: A multiagent simulation environment,” Simul., vol. 81, no. 7, 2005, pp. 517–527

    2005

  7. [15]

    M. J. North et al., ”Complex adaptive systems modeling with Repast Simphony,” Complex Adaptive Syst. Model. , vol. 1, 2013, pp. 1–26

    2013

  8. [16]

    Masad and J

    D. Masad and J. L. Kazil, ”Mesa: an agent-based modeling framework,” in SciPy, 2015, pp. 51-58

    2015

  9. [17]

    Rossetti, L

    G. Rossetti, L. Milli and S. Rinzivillo, ”NDlib: a python library to model and analyze diffusion processes over complex networks,” in Companion Proc. Web Conf., 2018, pp. 183–186

    2018

  10. [18]

    S. Abar, G. K. Theodoropoulos, P. Lemarinier and G. M. O’Hare, ”Agent based modelling and simulation tools: a review of the state- of-art software,” Comput. Sci. Rev., vol. 24, 2017, pp. 13–33

    2017

  11. [19]

    Chen and U

    J. Chen and U. Wilensky, ”ChatLogo: a large language model-driven hy- brid natural-programming language interface for agent-based modeling and programming,” 2023, arXiv:2308.08102

    2023 arXiv

  12. [20]

    Foramitti, ”AgentPy: a package for agent-based modeling in Python,” J

    J. Foramitti, ”AgentPy: a package for agent-based modeling in Python,” J. Open Source Softw. , vol. 6, no. 62, 2021, pp. 3065

    2021

  13. [21]

    Collier and M

    N. Collier and M. North, ”Repast HPC: A platform for large-scale agent- based modeling”, Large-Scale Comput., 2011, pp. 81–109

    2011

  14. [22]

    Collier and J

    N. Collier and J. Ozik, ”Distributed agent-based simulation with Repast4Py,” in Proc. Winter Simul. Conf. (WSC) , 2022, pp. 192–206

    2022

  15. [23]

    Chopra et al., ”DeepABM: scalable and efficient agent-based simu- lations via geometric learning frameworks-a case study for COVID-19 spread and interventions,” in Winter Simul

    A. Chopra et al., ”DeepABM: scalable and efficient agent-based simu- lations via geometric learning frameworks-a case study for COVID-19 spread and interventions,” in Winter Simul. Conf. (WSC), 2021, pp. 1–12

    2021

  16. [24]

    Datseris, A

    G. Datseris, A. R. Vahdati and T. C. DuBois, ”Agents.jl: a performant and feature-full agent-based modeling software of minimal code com- plexity,” in Simul., 2022

    2022

  17. [25]

    Antelmi et al., ”Reliable and efficient agent-based modeling and simulation,” J

    A. Antelmi et al., ”Reliable and efficient agent-based modeling and simulation,” J. Artif. Soc. Social Simul. , vol. 27, no. 2, 2024

    2024

  18. [26]

    Ryczko, A

    K. Ryczko, A. Domurad, N. Buhagiar and I. Tamblyn, ”Hashkat: large- scale simulations of online social networks,” Social Netw. Anal. Mining, vol. 7, 2017, pp. 1–13

    2017

  19. [27]

    X. Ye, L. Dang, J. Lee, M. H. Tsou and Z. Chen, ”Open source social network simulator focusing on spatial meme diffusion,” in Human Dyn. Res. Smart and Connected Communities , 2018, pp. 203-222

    2018

  20. [28]

    J. M. S ´anchez, C. A. Iglesias and J. F. S ´anchez-Rada, ”Soil: An agent- based social simulator in Python for modelling and simulation of social networks,” in Proc. PAAMS, 2017, pp. 234–245

    2017

  21. [29]

    C. C. Kerr et al., ”Covasim: an agent-based model of COVID-19 dynamics and interventions,” PLOS Comput. Biol., vol. 17, no. 7, 2021, pp. e1009149

    2021

  22. [30]

    Mahmood et al., ”FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local re- gions,” J

    I. Mahmood et al., ”FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local re- gions,” J. Simul., vol. 16, no. 4, 2022, pp. 355-373

    2022

  23. [31]

    Exploring network structure, dynamics, and function using NetworkX,

    A. Hagberg, P. J. Swart and D. A. Schult, “Exploring network structure, dynamics, and function using NetworkX, ” Los Alamos Nat. Lab. (LANL), Los Alamos, NM, USA, Rep. LA-UR-08-5495, 2008

    2008

  24. [32]

    Csardi and T

    G. Csardi and T. Nepusz, ”The igraph software,” Complex syst , vol. 1695, 2006, pp. 1-9

    2006

  25. [33]

    W. O. Kermack, and A. G. McKendrick, ”A contribution to the math- ematical theory of epidemics,” in Proc. of the roy. soc. of london. Ser. A, Containing papers of a math. and physical character , vol. 115, no. 772, 1927, pp. 700-721

    1927

  26. [34]

    Williams, N

    R. Williams, N. Hosseinichimeh, A. Majumdar and N. Ghaffarzadegan, ”Epidemic modeling with generative agents,” 2023, arXiv:2307.04986

    2023 arXiv

  27. [35]

    Learning to discover social circles in ego networks,

    J. McAuley and J. Leskovec, “Learning to discover social circles in ego networks,” in Proc. Neural Inf. Process. Syst. (NIPS) , 2012

    2012

  28. [36]

    Chica, R

    M. Chica, R. Chiong, M. Kirley and H. Ishibuchi, ”A networked N- player trust game and its evolutionary dynamics,” in IEEE Trans. Evol. Comput., vol. 22, no. 6, 2017, pp. 866-878

    2017

  29. [37]

    Wang, et al., ”A survey on large language model based autonomous agents,” Frontiers of Comput

    L. Wang, et al., ”A survey on large language model based autonomous agents,” Frontiers of Comput. Sci. , vol. 18, no. 6, 2024, pp. 186345

    2024

  30. [38]

    J. Lin, H. Zhao, A. Zhang, Y . Wu, H. Ping and Q. Chen, ”Agentsims: An open-source sandbox for large language model evaluation,” 2023, arXiv:2308.04026

    2023 arXiv

  31. [39]

    Gao, et al., ”S 3: social-network simulation system with large language model-empowered agents,” 2023, arXiv:2307.14984

    C. Gao, et al., ”S 3: social-network simulation system with large language model-empowered agents,” 2023, arXiv:2307.14984

    2023 arXiv

  32. [40]

    11, 2024

    OpenAI, ”ChatGPT 3.5 Turbo,” Accessed: Dec. 11, 2024. [Online]. Available: https://platform.openai.com

    2024

  33. [41]

    A. Q. Jiang et al., ”Mistral 7B,” 2023, arXiv:2310.06825

    2023 arXiv

  34. [42]

    A. S. Vezhnevets, et al., ”Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia,” 2023, arXiv:2312.03664

    2023 arXiv

  35. [43]

    Y . Li, J. Fan, Y . Wang and K. L. Tan, ”Influence maximization on social graphs: a survey,” in IEEE Trans. Knowl. Data Eng. , vol. 30, no. 10, 2018, pp. 1852-1872

    2018

  36. [44]

    Kempe, J

    D. Kempe, J. Kleinberg, and E. Tardos, ”Maximizing the spread of influence through a social network,” in Proc. 9th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining , 2003, pp. 137-146

    2003

  37. [45]

    Granovetter, ”Threshold models of collective behavior,” Amer

    M. Granovetter, ”Threshold models of collective behavior,” Amer. J. Sociology, vol. 83, no.6, 1978, pp. 1420-1443

    1978

  38. [46]

    T. C. Schelling, Micromotives and Macrobehavior, New York, NY , USA: Norton, 1978

    1978

  39. [47]

    Leskovec and A

    J. Leskovec and A. Krevl, ”SNAP datasets: Stanford large network dataset collection,” 2014. Accessed: Dec. 11, 2024. [Online]. Available: https://snap.stanford.edu/data

    2014

  40. [48]

    Evolutionary games on graphs,

    G. Szab ´o and G. F ´ath, “Evolutionary games on graphs,” Phys. Rep., vol. 446, nos. 4–6, pp. 97–216, Jul. 2007

    2007

  41. [49]

    A simple rule for the evolution of cooperation on graphs and social networks,

    H. Ohtsuki, C. Hauert, E. Lieberman, and M. A. Nowak, “A simple rule for the evolution of cooperation on graphs and social networks,” Nature, vol. 441, pp. 502–505, May 2006

    2006

  42. [50]

    Y . Du, J. Z. Leibo, U. Islam, R. Willis, and P. Sunehag, ”A review of cooperation in multi-agent learning,” 2023, arXiv:2312.05162

    2023 arXiv

  43. [51]

    R. R. Mallipeddi, S. Kumar, C. Sriskandarajah, and Y . Zhu, ”A frame- work for analyzing influencer marketing in social networks: selection and scheduling of influencers,” Manage. Sci., vol. 68, no.1, pp.75-104, 2022

    2022

  44. [52]

    Adesokan, M

    A. Adesokan, M. S. Siraj, A. B. Rahman, E. E. Tsiropoulou, and S. Papavassiliou, ”How to become an influencer in social networks,” in ICC 2023-IEEE Int. Conf. on Commun. , pp. 5570-5575, 2023

    2023

  45. [53]

    Rozemberczki, C

    B. Rozemberczki, C. Allen, and R. Sarkar, ”Multi-scale attributed node embedding,” 2019, arXiv:1909.13021

    2019 arXiv

  46. [54]

    Rozemberczki, and R

    B. Rozemberczki, and R. Sarkar, ”Twitch gamers: a dataset for evalu- ating proximity preserving and structural role-based node embeddings,” 2021, arXiv:2101.03091

    2021 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.