refs.bib

@book{newman2018networks,
  title={Networks},
  author={Newman, Mark E. J.},
  year={2018},
  publisher={Oxford University Press}
}

@article{zachary1977information,
  title={An information flow model for conflict and fission in small groups},
  author={Zachary, Wayne W},
  journal={Journal of Anthropological Research},
  volume={33},
  number={4},
  pages={452--473},
  year={1977},
  publisher={University of New Mexico}
}

@article{mitzenmacherBriefHistoryGenerative2004,
  title = {A {{Brief History}} of {{Generative Models}} for {{Power Law}} and {{Lognormal Distributions}}},
  author = {Mitzenmacher, Michael},
  year = {2004},
  month = jan,
  journal = {Internet Mathematics},
  volume = {1},
  number = {2},
  pages = {226--251},
  issn = {1542-7951, 1944-9488},
  doi = {10.1080/15427951.2004.10129088},
  urldate = {2024-07-10},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/ZYD3YSRD/Mitzenmacher - 2004 - A Brief History of Generative Models for Power Law.pdf}
}


@article{clausetPowerLawDistributionsEmpirical2009,
  title = {Power-{{Law Distributions}} in {{Empirical Data}}},
  author = {Clauset, Aaron and Shalizi, Cosma Rohilla and Newman, Mark E. J.},
  year = {2009},
  month = nov,
  journal = {SIAM Review},
  volume = {51},
  number = {4},
  pages = {661--703},
  issn = {0036-1445, 1095-7200},
  doi = {10.1137/070710111},
  urldate = {2023-09-04},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/E4ZX6WAW/Clauset et al. - 2009 - Power-Law Distributions in Empirical Data.pdf}
}


@article{barabasiEmergenceScalingRandom1999,
  title = {Emergence of {{Scaling}} in {{Random Networks}}},
  author = {Barab{\'a}si, Albert-L{\'a}szl{\'o} and Albert, R{\'e}ka},
  year = {1999},
  month = oct,
  journal = {Science},
  volume = {286},
  number = {5439},
  pages = {509--512},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.286.5439.509},
  urldate = {2023-05-01},
  abstract = {Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/E227Q37M/Barabási and Albert - 1999 - Emergence of Scaling in Random Networks.pdf}
}


@inproceedings{overgoorChoosingGrowGraph2019,
  title = {Choosing to {{Grow}} a {{Graph}}: {{Modeling Network Formation}} as {{Discrete Choice}}},
  shorttitle = {Choosing to {{Grow}} a {{Graph}}},
  booktitle = {The {{World Wide Web Conference}}},
  author = {Overgoor, Jan and Benson, Austin and Ugander, Johan},
  year = {2019},
  month = may,
  pages = {1409--1420},
  publisher = {ACM},
  address = {San Francisco CA USA},
  doi = {10.1145/3308558.3313662},
  urldate = {2023-09-04},
  isbn = {978-1-4503-6674-8},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/8C4EDBE7/Overgoor et al. - 2019 - Choosing to Grow a Graph Modeling Network Formati.pdf}
}

@article{broidoScalefreeNetworksAre2019,
  title = {Scale-Free Networks Are Rare},
  author = {Broido, Anna D. and Clauset, Aaron},
  year = {2019},
  month = mar,
  journal = {Nature Communications},
  volume = {10},
  number = {1},
  pages = {1017},
  issn = {2041-1723},
  doi = {10.1038/s41467-019-08746-5},
  urldate = {2024-08-30},
  abstract = {Abstract                            Real-world networks are often claimed to be scale free,~meaning that the fraction of nodes with degree               k               follows a power law               k                                -                 {$\alpha$}                              , a pattern with broad implications for the structure and dynamics of complex systems. However, the universality of scale-free networks remains controversial. Here, we organize different definitions of scale-free networks and construct a severe test of their empirical prevalence using state-of-the-art statistical tools applied to nearly 1000 social, biological, technological, transportation, and information networks. Across these networks, we find robust evidence that strongly scale-free structure is empirically rare, while for most networks, log-normal distributions fit the data as well or better than power laws. Furthermore, social networks are at best weakly scale free, while a handful of technological and biological networks appear strongly scale free. These findings highlight the structural diversity of real-world networks and the need for new theoretical explanations of these non-scale-free patterns.},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/VYKVBI5U/Broido and Clauset - 2019 - Scale-free networks are rare.pdf}
}

@article{holmeRareEverywherePerspectives2019,
  title = {Rare and Everywhere: {{Perspectives}} on Scale-Free Networks},
  shorttitle = {Rare and Everywhere},
  author = {Holme, Petter},
  year = {2019},
  month = mar,
  journal = {Nature Communications},
  volume = {10},
  number = {1},
  pages = {1016},
  issn = {2041-1723},
  doi = {10.1038/s41467-019-09038-8},
  urldate = {2024-08-30},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/NNINIQ3E/Holme - 2019 - Rare and everywhere Perspectives on scale-free ne.pdf}
}

@article{yuleMathematicalTheoryEvolution1925,
  title = {A Mathematical Theory of Evolution, Based on the Conclusions of {{Dr}}. {{J}}. {{C}}. {{Willis}}, {{F}}. {{R}}. {{S}}},
  author = {Yule},
  year = {1925},
  month = jan,
  journal = {Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character},
  volume = {213},
  number = {402-410},
  pages = {21--87},
  issn = {0264-3960, 2053-9266},
  doi = {10.1098/rstb.1925.0002},
  urldate = {2024-08-30},
  abstract = {The following work is founded on that conception of evolution, the most recent and precise formulation of which is due to Dr. J. C. Willis, and represents an attempt to develop the quantitative consequences of the conception. By his statistical studies of distribution Dr. Willis was led to two conclusions:--- (1) Species occupying large areas are,               on the whole               , older than those occupying small areas, provided that allied forms are compared.},
  copyright = {https://royalsociety.org/journals/ethics-policies/data-sharing-mining/},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/LV77BWFN/1925 - II.—A mathematical theory of evolution, based on t.pdf}
}

@article{simonClassSkewDistribution1955,
  title = {On a {{Class}} of {{Skew Distribution Functions}}},
  author = {Simon, Herbert A.},
  year = {1955},
  journal = {Biometrika},
  volume = {42},
  number = {3-4},
  pages = {425--440},
  issn = {0006-3444, 1464-3510},
  doi = {10.1093/biomet/42.3-4.425},
  urldate = {2024-08-30},
  langid = {english}
}

@article{price1976general,
  title = {A General Theory of Bibliometric and Other Cumulative Advantage Processes},
  author = {Price, Derek de Solla},
  year = {1976},
  journal = {Journal of the American society for Information science},
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  number = {5},
  pages = {292--306},
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}

@article{bollobasDegreeSequenceScale2001,
  title = {The Degree Sequence of a Scale-free Random Graph Process},
  author = {Bollob{\'a}s, B{\textasciiacute}ela and Riordan, Oliver and Spencer, Joel and Tusn{\'a}dy, G{\'a}bor},
  year = {2001},
  month = may,
  journal = {Random Structures \& Algorithms},
  volume = {18},
  number = {3},
  pages = {279--290},
  issn = {1042-9832, 1098-2418},
  doi = {10.1002/rsa.1009},
  urldate = {2024-08-30},
  abstract = {Abstract                            Recently, Barab{\'a}si and Albert [2] suggested modeling complex real-world networks such as the worldwide web as follows: consider a random graph process in which vertices are added to the graph one at a time and joined to a fixed number of earlier vertices, selected with probabilities proportional to their degrees. In [2] and, with Jeong, in [3], Barab{\'a}si and Albert suggested that after many steps the proportion               P               (               d               ) of vertices with degree               d               should obey a power law               P               (               d               ){$\alpha$}               d               -{$\gamma$}               . They obtained {$\gamma$}=2.9{\textpm}0.1 by experiment and gave a simple heuristic argument suggesting that {$\gamma$}=3. Here we obtain               P               (               d               ) asymptotically for all               d               {$\leq$}               n               1/15               , where               n               is the number of vertices, proving as a consequence that {$\gamma$}=3.{$\quad\copyright$} 2001 John Wiley \& Sons, Inc.{$\quad$}Random Struct. Alg., 18, 279--290, 2001},
  copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/HUUMRPT3/Bollobás et al. - 2001 - The degree sequence of a scale‐free random graph p.pdf}
}



@article{musae,
          author = {Rozemberczki, Benedek and Allen, Carl and Sarkar, Rik},
          title = {{Multi-Scale Attributed Node Embedding}},
          journal = {Journal of Complex Networks},
          volume = {9},
          number = {2},
          year = {2021},
}

@article{chung2002connected,
  title={Connected components in random graphs with given expected degree sequences},
  author={Chung, Fan and Lu, Linyuan},
  journal={Annals of Combinatorics},
  volume={6},
  number={2},
  pages={125--145},
  year={2002},
  publisher={Springer}
}

@article{milgram1967small,
  title={The small world problem},
  author={Milgram, Stanley},
  journal={Psychology Today},
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  year={1967},
  publisher={New York}
}

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  title={Behavioral study of obedience.},
  author={Milgram, Stanley},
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  publisher={American Psychological Association}
}

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  title={Cumulated social roles: The duality of persons and their algebras},
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  journal={Social networks},
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}

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  title={An algorithm for drawing general undirected graphs},
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}

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  title={Matrix analysis},
  author={Horn, Roger A and Johnson, Charles R},
  year={2012},
  publisher={Cambridge university press}
}

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  title={The Perron--Frobenius theorem and the ranking of football teams},
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  title={A new status index derived from sociometric analysis},
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  pages={39--43},
  year={1953},
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@article{langville2005survey,
  title={A survey of eigenvector methods for web information retrieval},
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  volume={47},
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  pages={135--161},
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}

@article{boldi2014axioms,
  title={Axioms for centrality},
  author={Boldi, Paolo and Vigna, Sebastiano},
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  pages={222--262},
  year={2014},
  publisher={Taylor \& Francis}
}

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  title={Communication patterns in task-oriented groups},
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  pages={161--163},
  year={1965},
  publisher={Wiley Online Library}
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  title={A set of measures of centrality based on betweenness},
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  year={1977}
}

@article{erdHos1960evolution,
  title={On the evolution of random graphs},
  author={Erd{\H{o}}s, Paul and R{\'e}nyi, Alfr{\'e}d},
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@article{watson1875probability,
  title={On the probability of the extinction of families},
  author={Watson, Henry William and Galton, Francis},
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@article{watts1998collective,
  title={Collective dynamics of ‘small-world’networks},
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@article{riordan2010diameter,
  title={The diameter of sparse random graphs},
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  volume={19},
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  pages={835--926},
  year={2010},
  publisher={Cambridge University Press}
}

@article{bollobas1980probabilistic,
  title={A probabilistic proof of an asymptotic formula for the number of labelled regular graphs},
  author={Bollob{\'a}s, B{\'e}la},
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  publisher={Elsevier}
}

@article{fosdick2018configuring,
  title={Configuring random graph models with fixed degree sequences},
  author={Fosdick, Bailey K and Larremore, Daniel B and Nishimura, Joel and Ugander, Johan},
  journal={Siam Review},
  volume={60},
  number={2},
  pages={315--355},
  year={2018},
  publisher={SIAM}
}

@misc{luxburgTutorialSpectralClustering2007,
  title = {A {{Tutorial}} on {{Spectral Clustering}}},
  author = {von Luxburg, Ulrike},
  year = {2007},
  month = nov,
  number = {arXiv:0711.0189},
  eprint = {0711.0189},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2024-10-09},
  abstract = {In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Data Structures and Algorithms,Computer Science - Machine Learning},
  file = {/Users/philchodrow/Zotero/storage/B9NU254F/Luxburg - 2007 - A Tutorial on Spectral Clustering.pdf;/Users/philchodrow/Zotero/storage/BZ95M9P5/0711.html}
}


@inproceedings{gleich2016mining,
  title={Mining Large Graphs.},
  author={Gleich, David F and Mahoney, Michael W},
  booktitle={Handbook of Big Data},
  editors = {Peter B\:uhlmann, Petros Drineas, Michael Kane, Mark van der Laan},
  year={2016}
}

@inproceedings{wagner1993between,
  title={Between min cut and graph bisection},
  author={Wagner, Dorothea and Wagner, Frank},
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  pages={744--750},
  year={1993},
  organization={Springer}
}

@article{shi2000normalized,
  title = {Normalized Cuts and Image Segmentation},
  author = {Shi, Jianbo and Malik, Jitendra},
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  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {22},
  number = {8},
  pages = {888--905},
  publisher = {Ieee}
}


@article{fournet2014contact,
  title={Contact patterns among high school students},
  author={Fournet, Julie and Barrat, Alain},
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  volume={9},
  number={9},
  pages={e107878},
  year={2014},
  publisher={Public Library of Science San Francisco, USA}
}

@article{newman2004finding,
  title={Finding and evaluating community structure in networks},
  author={Newman, Mark E.J. and Girvan, Michelle},
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  volume={69},
  number={2},
  pages={026113},
  year={2004},
  publisher={APS}
}

@article{zhang2014scalable,
  title={Scalable detection of statistically significant communities and hierarchies, using message passing for modularity},
  author={Zhang, Pan and Moore, Cristopher},
  journal={Proceedings of the National Academy of Sciences},
  volume={111},
  number={51},
  pages={18144--18149},
  year={2014},
  publisher={National Acad Sciences}
}

@article{girvan2002community,
  title={Community structure in social and biological networks},
  author={Girvan, Michelle and Newman, Mark EJ},
  journal={Proceedings of the National Academy of Sciences},
  volume={99},
  number={12},
  pages={7821--7826},
  year={2002},
  publisher={National Acad Sciences}
}

@article{chodrow2020moments,
  title={Moments of uniform random multigraphs with fixed degree sequences},
  author={Chodrow, Philip S},
  journal={SIAM Journal on Mathematics of Data Science},
  volume={2},
  number={4},
  pages={1034--1065},
  year={2020},
  publisher={SIAM}
}

@article{delvenne2010stability,
  title={Stability of graph communities across time scales},
  author={Delvenne, J-C and Yaliraki, Sophia N and Barahona, Mauricio},
  journal={Proceedings Of The National Academy Of Sciences},
  volume={107},
  number={29},
  pages={12755--12760},
  year={2010},
  publisher={National Acad Sciences}
}

@article{newman2016equivalence,
  title={Equivalence between modularity optimization and maximum likelihood methods for community detection},
  author={Newman, Mark EJ},
  journal={Physical Review E},
  volume={94},
  number={5},
  pages={052315},
  year={2016},
  publisher={APS}
}

@article{peel2017ground,
  title={The ground truth about metadata and community detection in networks},
  author={Peel, Leto and Larremore, Daniel B and Clauset, Aaron},
  journal={Science Advances},
  volume={3},
  number={5},
  pages={e1602548},
  year={2017},
  publisher={American Association for the Advancement of Science}
}

@article{brandes2007modularity,
  title={On modularity clustering},
  author={Brandes, Ulrik and Delling, Daniel and Gaertler, Marco and Gorke, Robert and Hoefer, Martin and Nikoloski, Zoran and Wagner, Dorothea},
  journal={IEEE transactions on knowledge and data engineering},
  volume={20},
  number={2},
  pages={172--188},
  year={2007},
  publisher={IEEE}
}

@article{shannon1948mathematical,
  title={A mathematical theory of communication},
  author={Shannon, Claude Elwood},
  journal={The Bell system technical journal},
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  number={3},
  pages={379--423},
  year={1948},
  publisher={Nokia Bell Labs}
}

@book{cover2006elements,
  title={Elements of information theory},
  author={Cover, Thomas M. and Thomas, Joy A},
  year={2006},
  publisher={Wiley-Interscience}
}

@article{peixoto2019bayesian,
  title={Bayesian stochastic blockmodeling},
  author={Peixoto, Tiago P},
  journal={Advances in network clustering and blockmodeling},
  pages={289--332},
  year={2019},
  publisher={Wiley Online Library}
}

@article{brin1998anatomy,
  title = {The Anatomy of a Large-Scale Hypertextual Web Search Engine},
  author = {Brin, Sergey and Page, Lawrence},
  year = {1998},
  journal = {Computer Networks and ISDN Systems},
  volume = {30},
  number = {1-7},
  pages = {107--117},
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}

@article{bak2021stewardship,
  title={Stewardship of global collective behavior},
  author={Bak-Coleman, Joseph B and Alfano, Mark and Barfuss, Wolfram and Bergstrom, Carl T and Centeno, Miguel A and Couzin, Iain D and Donges, Jonathan F and Galesic, Mirta and Gersick, Andrew S and Jacquet, Jennifer and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={118},
  number={27},
  pages={e2025764118},
  year={2021},
  publisher={National Acad Sciences}
}

@inproceedings{liben2003link,
  title={The link prediction problem for social networks},
  author={Liben-Nowell, David and Kleinberg, Jon},
  booktitle={Proceedings of the twelfth international conference on Information and knowledge management},
  pages={556--559},
  year={2003}
}

@book{sahimi2023applications,
  title={Applications of Percolation Theory},
  author={Sahimi, Muhammad},
  year={2023},
  publisher={Springer}
}

@article{porter2016dynamical,
  title={Dynamical systems on networks},
  author={Porter, Mason A and Gleeson, James P},
  journal={Frontiers in Applied Dynamical Systems: Reviews and Tutorials},
  volume={4},
  pages={29},
  year={2016},
  publisher={Springer}
}

@article{Landry_XGI_2023,
author = {Landry, Nicholas W. and Lucas, Maxime and Iacopini, Iacopo and Petri, Giovanni and Schwarze, Alice and Patania, Alice and Torres, Leo},
title = {{XGI: A Python package for higher-order interaction networks}},
doi = {10.21105/joss.05162},
journal = {Journal of Open Source Software},
publisher = {The Open Journal},
year = {2023},
month = may,
volume = {8},
number = {85},
pages = {5162},
url = {https://doi.org/10.21105/joss.05162},
}

@incollection{dijkstra2022note,
  title={A note on two problems in connexion with graphs},
  author={Dijkstra, Edsger W},
  booktitle={Edsger Wybe Dijkstra: his life, work, and legacy},
  pages={287--290},
  year={1956}
}

@book{rosen2011discrete,
  title={Discrete Mathematics and Its Applications (7th Edition)},
  author={Rosen, Kenneth H},
  publisher={William C Brown Publishing},
  year={2011}
}



@article{newmanModularityCommunityStructure2006,
  title = {Modularity and Community Structure in Networks},
  author = {Newman, Mark E. J.},
  year = {2006},
  month = jun,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {103},
  number = {23},
  pages = {8577--8582},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0601602103},
  urldate = {2025-02-21},
  abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as ``modularity'' over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.},
  langid = {english},
  file = {/Users/philchodrow/Zotero/storage/PRY494E9/Newman - 2006 - Modularity and community structure in networks.pdf}
}


@article{fire2012organization,
  title={Organization mining using online social networks},
  author={Fire, Michael and Puzis, Rami},
  journal={Networks and Spatial Economics},
  pages={1--34},
  year={2012},
  publisher={Springer}
}


@article{ramani2019coin,
  title={Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities},
  author={Ramani, Arjun S and Eikmeier, Nicole and Gleich, David F},
  journal={SIAM Review},
  volume={61},
  number={3},
  pages={549--595},
  year={2019},
  publisher={SIAM}
}