This is a reading list. While many of the articles in it are of high quality, being listed here does not imply endorsement of content.
Malvestio, I, Cardillo, A, Masuda, N. (2020) Interplay between -core and community structure in complex networks. Scientific Reports 10.1038/s41598-020-71426-8
Garfield (1963) Citation Indexes in Sociological and Historical Research in Essays of an Information Scientist Vol 1 43-46 Current Contents #9 (1969), Reprinted from American Documentation 144(4): 289-291 (1969)
Editorial (2017) Neutral citation is poor scholarship Nature Genetics 10.1038/ng.3989
Rand and Rust (2011) Agent-based modeling in marketing: Guidelines for rigor 10.1016/j.ijresmar.2011.04.002
Bao and Teplitsky (2024) A simulation-based analysis of the impact of rhetorical citations in science 10.1038/s41467-023-44249-0
Nester (1996) An Applied Statistician's Creed Appl. Statist. 45(4) 401-410
Sales-Pardo (2007) Extracting the hierarchical organizationof complex systems 10.1073pnas.0703740104
Moreira et al. (2015) The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or from an Academic Department Are Consistent with a Discrete Lognormal Model 10.1371/journal.pone.0143108
Poncela-Casanovas et al. (2018) Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria 10.1038/s41562-019-0585-7
Richardson et al. (2025) The entities enabling scientific fraud at scale are large, resilient, and growing rapidly. 10.1073/pnas.2420092122
Petersen, Arroyave, and Pammoli (2025) The disruption index suffers from citation inflation: Re-analysis of temporal CD trend and relationship with team size reveal discrepancies. 10.1016/j.joi.2024.101605
Hopkin K, Robert K, and Alberts B. (2025) Teaching trust in science: a critical new focus for science education 10.3389/fcomm.2025.1589116
Fletcher & Stevenson (2025) Predicting retracted research: a dataset and machine learning approaches 10.1186/s41073-025-00168-w
McCain (2009) Using Tricitation to Dissect the Citation Image: Conrad Hal Waddington and the Rise of Evolutionary Developmental Biology 10.1002/asi.21064
Hill et al. (2025) The pivot penalty in research. 10.1038/s41586-025-09048-1
Kalhor et al. (2025) Measuring biases in AI-generated co-authorshipnetworks EPJ epjds/s13688-025-00555-9
Wren and Georgescu (2022) Detecting anomalous referencing patterns in PubMed papers suggestive of author-centric reference list manipulation Scientometrics 10.1007/s11192-022-04503-6
Shaw, D. (2025) The digital erosion of intellectual integrity: why misuse of generative AI is worse than plagiarism. AI & Society 10.1007/s00146-025-02362-2
Gao et al. (2024) Large language models empowered agent-based modeling and simulation: a survey and perspectives 10.1057/s41599-024-03611-3
Jordon et al. (2022) Synthetic Data- what why and how. also on arXiv. https://arxiv.org/abs/2205.03257
Touwen et al. (2024) Learning the mechanisms of network growth. 10.1038/s41598-024-61940-4
Lawrence P. (2007) The mismeasurement of science. 10.1016/j.cub.2007.06.014
Lawrence, P., and Locke, M. (1997) A Man For Our Season 10.1038/386757a0
Gilbert and Woolgar (1974) The Quantitative Study of Science: An Examination of the Literature. Science Studies (4): 279-294
G.N. Gilbert (1977) Referencing as Persuasion Social Studies of Science 7(1): 113-122
Elsenbroich and Polhill (2023) Agent-based modelling as a method for prediction in complex social systems 10.1080/13645579.2023.2152007
Jeanson et al. (2024) Medical calculators derived synthetic cohorts: a novel method for generating synthetic patient data Scientific Reports 10.1038/s41598-024-61721-z
Eom and Fortunato (2011) Characterizing and Modeling Citation Dynamics PLOS One 10.1371/journal.pone.0024926
Leahey et al. (2023) What Types of Novelty Are Most Disruptive? 10.1177/00031224231168074
Macfarlane, B. (2024) The DECAY of Merton’s scientific norms and the new academic ethos 10.1080/03054985.2023.2243814
Bhaktavatsalam, S. & Cartwright, N. (2017) What’s so special about empirical adequacy? 10.1007/s13194-017-0171-7
Heard et al. (2015) Agent-Based Models and Microsimulation 10.1146/annurev-statistics-010814-020218
Hacking, I. (1994) "The Advancement of Science: Science Without Legend, Objectivity without Illusion. by Philip Kitcher" The Journal of Philosophy, Apr., 1994, Vol. 91, No. 4 (Apr., 1994), pp. 212-215
Silva et al. (2020) Recency predicts bursts in the evolution of author citations 10.1162/qss_a_00070
Martin BR and Irvine J (1983) Assessing basic research. Some partial indicators of scientific progress in radio astronomy. Research Policy 12: 61-90
Cozzens, S. (1989) What Do Citations Count? The Rhetoric-First Model Scientometrics 15:436-447
Huber, J. (2002) A New Model that Generates Lotka’s Law 10.1002/asi.10025
Gilbert, N. (1997) A Simulation of the Structure of Academic Science. Sociological Research Online 2(2)
Meghanathan, N,. (2024) Local clustering coefficient‐based iterative peeling strategy to extract the core and peripheral layers of a network. Applied Network Science 10.1007/s41109‐024‐00667‐7
Statistical Bibliography in Relation to the Growth of Modern Civilization: Two Lectures delivered in the University of Cambridge in May 1922 (1923) E.W. Hulme
Weisberg, M. (2007) Who Is A Modeler? 10.1093/bjps/axm011
Pike et al. (2022) Growing the simulation ecosystem: introducing Mesa Data to provide transparent, accessible, and extensible data pipelines for simulation development 10.1177/00375497221077425
Rao, R. Methodological and conceptual questions of bibliometric standards (1996) Scientometrics 10.1007/BF02018484
Gläser, J., Held, M., & Laudel, G. (2019). Linking individual-level to community-level thematic change: How do individual research trails match disjoint clusters of direct citation networks?. In ISSI (pp. 2750-2751).
Taking the Measure of Science: A Review of Citation Theories (1981) International Society for The Study of Knowledge 7:16-21
Cited Documents as Concept Symbols (1978) Small, H. Social Studies of Science , 8 (3): 327-340
Theories of citation Leydesdorff, L. (1998)
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance (2010) JMLR 11(10) 2837-2854
Unpacking the essential tension of knowledge recombination: Analyzing the impact of knowledge spanning on citation impact and disruptive innovation (2023) Wang et al. 10.1016/j.joi.2023.101451
Past as prologue: Approaches to the study of confirmation in science (2020) Small, H.G. 10.1162/qss_a_00063
Network analysis to evaluate the impact of research funding on research community consolidation (2019) Hicks et al. 10.1371/journal.pone.0218273
A mid-level approach to modeling scientific communities (2019) Harnagel, A. 10.1016/j.shpsa.2018.12.010
Formal models of the scientific community and the value-ladenness of science. (2021) Politi, V. 10.1007/s13194-021-00418-w
A review of stochastic block models and extensions for graph clustering (2019) 10.1007/s41109-019-0232-2 Lee and Wilkinson
Clustering spectrum of scale-free networks (2017) Stegehuis et al. 10.1103/PhysRevE.96.042309
Using published pathway figures in enrichment analysis and machine learning (2023) 0.1186/s12864-023-09816-1 Shin and Pico
Adjusting for Chance Clustering Comparison Measures JMLR 17(134):1−32, 2016.
Two-sample goodness-of-fit tests when ties are present (1993) Journal of Statistical Planning and Inference 39 (1994) 399-424
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (2016) 10.1007/s10654-016-0149-3
Extracellular vesicles - on the cusp of a new language in the biological sciences (2023) 10.20517/evcna.2023.18
Hyper-ambition and the Replication Crisis:...(2024) 10.1007/s10805-024-09528-5
Statistical Network Similarity (2023) Complex Networks and Their Applications 10.1007/978-3-031-21131-7_25
Comparing methods for comparing networks. (2019) Sci Rep 10.1038/s41598-019-53708-y
Science In The Age of Selfies (2024) PNAS. 10.1073/pnas.160979311
The Burden of Knowledge and the 'Death of the Renaissance Man': Is Innovation Getting Harder? (2009) The Review of Economic Studies
cuAlign: Scalable Network Alignment on GPU Accelerators (2023) 10.1145/3624062.3625129
Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks (2022) 10.1109/HST56032.2022.10024983
Scalable static and dynamic community detection using Grappolo (2017) 10.1109/HPEC.2017.8091047
Think locally, act locally: Detection of small, medium-sized, and large communities in large networks (2016) 10.1103/PhysRevE.91.012821
A new insight to the analysis of co-authorship in Google Scholar (2022) 10.1007/s41109-022-00460-4
How New Ideas Diffuse In Science (2023) 10.1177/00031224231166
A density-based statistical analysis of graph clustering algorithm performance 10.1093/comnet/cnaa012
Statistical power, accuracy, reproducibility and robustness of a graph clusterability test. 10.1007/s41060-023-00389-6
Efficient discovery of overlapping communities in massive networks 10.1073/pnas.1221839110
Disruption indices and their calculation using web-of-science data: Indicators of historical developments or evolutionary dynamics? 10.1016/j.joi.2021.101219
How citation distortions create unfounded authority: analysis of a citation network. 10.1136/bmj.b2680
Systematic assessment of the quality of fit of the stochastic block model for empirical networks. 10.1103/PhysRevE.105.054311
Understanding Trends, Patterns, and Dynamics in Global Acquisitions: A Network Perspective arXiv 2402.03910
Ambiguity in Ethical Standards: Global Versus Local Science in Explaining Academic Plagiarism. Sci Eng Ethics 30, 4 (2024). 10.1007/s11948-024-00464-6