Configuring Random Graph Models with Fixed Degree Sequences

Emergence of Scaling in Random Networks

Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works

Finding and evaluating community structure in networks

Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works

Community detection in graphs

Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · see citing works

Power-Law Distributions in Empirical Data

Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019 · see citing works

Network motifs: simple building blocks of complex networks.

Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368 · see citing works

Networks

Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595 · see citing works

Assortative Mixing in Networks

Newman, M. E. J. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.208701 · Cited by 5,034 · see citing works

Design of Experiments

Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980 · see citing works

Random graphs with arbitrary degree distributions and their applications

Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.026118 · Cited by 3,675 · see citing works

Snowball Sampling

Goodman, L. A. (1961) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177705148 · Cited by 3,606 · see citing works

Mixing patterns in networks.

Newman, M. (2002) · Physical review. E, Statistical, nonlinear, and soft matter physics · doi:10.1103/physreve.67.026126 · Cited by 3,130 · see citing works

Networks of Scientific Papers

Price, D. J. D. S. (1965) · Science · doi:10.1126/science.149.3683.510 · Cited by 3,121 · see citing works

Specificity and Stability in Topology of Protein Networks

Maslov, S., & Sneppen, K. (2002) · Science · doi:10.1126/science.1065103 · Cited by 3,051 · see citing works

Stochastic blockmodels: First steps

Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983) · Social Networks · doi:10.1016/0378-8733(83)90021-7 · Cited by 2,877 · see citing works

Network Robustness and Fragility: Percolation on Random Graphs

Callaway, D. S., Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.5468 · Cited by 2,495 · see citing works

A critical point for random graphs with a given degree sequence

Molloy, M. S. O., & Reed, B. A. (1995) · Random Structures and Algorithms · doi:10.1002/rsa.3240060204 · Cited by 2,419 · see citing works

Markov Chains and Mixing Times

Levin, D., Peres, Y., & Wilmer, E. (2008) · doi:10.1090/mbk/058 · Cited by 2,241 · see citing works

Stochastic blockmodels and community structure in networks

Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.83.016107 · Cited by 2,025 · see citing works

A general theory of bibliometric and other cumulative advantage processes

Price, D. J. D. S. (1976) · Journal of the American Society for Information Science · doi:10.1002/asi.4630270505 · Cited by 2,013 · see citing works

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Leskovec, J., Lang, K. J., Dasgupta, A., & Mahoney, M. W. (2009) · Internet Mathematics · doi:10.1080/15427951.2009.10129177 · Cited by 1,867 · see citing works

Influentials, Networks, and Public Opinion Formation

Watts, D. J., & Dodds, P. S. (2007) · Journal of Consumer Research · doi:10.1086/518527 · Cited by 1,864 · see citing works

A Brief History of Generative Models for Power Law and Lognormal Distributions

Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129088 · Cited by 1,809 · see citing works

Mixed Membership Stochastic Blockmodels.

Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2008) · PubMed · Cited by 1,702 · see citing works

Assembly of species communities

Diamond, J. M. (1975) · Medical Entomology and Zoology · Cited by 1,617 · see citing works

Superfamilies of Evolved and Designed Networks

Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S. S., Ayzenshtat, I., Sheffer, M., & Alon, U. S. (2004) · Science · doi:10.1126/science.1089167 · Cited by 1,427 · see citing works

NULL MODELS IN ECOLOGY

Gotelli, N. J., & Graves, G. R. (1996) · Cited by 1,316 · see citing works

The Assembly of Species Communities: Chance or Competition?

Connor, E. F., & Simberloff, D. (1979) · Ecology · doi:10.2307/1936961 · Cited by 1,221 · see citing works

A Probabilistic Proof of an Asymptotic Formula for the Number of Labelled Regular Graphs

Bollobás, B. (1980) · European Journal of Combinatorics · doi:10.1016/s0195-6698(80)80030-8 · Cited by 1,204 · see citing works

A Survey of Exact Inference for Contingency Tables

Agresti, A. (1992) · Statistical Science · doi:10.1214/ss/1177011454 · Cited by 1,110 · see citing works

The checkerboard score and species distributions

Stone, L., & Roberts, A. P. (1990) · Oecologia · doi:10.1007/bf00317345 · Cited by 1,061 · see citing works

The asymptotic number of labeled graphs with given degree sequences

Bender, E. A., & Canfield, E. R. (1978) · Journal of Combinatorial Theory Series A · doi:10.1016/0097-3165(78)90059-6 · Cited by 995 · see citing works

Who Shall Survive

Moreno, J. L., & Jennings, H. H. (2007) · The Journal of Psychodrama Sociometry and Group Psychotherapy · doi:10.12926/abygax75 · Cited by 918 · see citing works

The Theory of Graphs and Its Applications

Burger, E., Berge, C., & Doig, A. (1965) · Econometrica · doi:10.2307/1911908 · Cited by 905 · see citing works

The average distances in random graphs with given expected degrees

Chung, F., & Lü, L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252631999 · Cited by 891 · see citing works

SNAP

Leskovec, J., & Sosič, R. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2898361 · Cited by 885 · see citing works

Die Theorie der regulären graphs

Petersen, J. (1891) · Acta Mathematica · doi:10.1007/bf02392606 · Cited by 812 · see citing works

Motifs in Brain Networks

Sporns, O., & Kötter, R. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020369 · Cited by 805 · see citing works

Markov chain Monte Carlo estimation of exponential random graph models

Snijders, T. A. B. (2002) · Cited by 742 · see citing works

On Realizability of a Set of Integers as Degrees of the Vertices of a Linear Graph. I

Hakimi, S. L. (1962) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0110037 · Cited by 642 · see citing works

Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow

Sinclair, A. (1992) · Combinatorics Probability Computing · doi:10.1017/s0963548300000390 · Cited by 514 · see citing works

Combinatorial Properties of Matrices of Zeros and Ones

Ryser, H. J. (1957) · Canadian Journal of Mathematics · doi:10.4153/cjm-1957-044-3 · Cited by 510 · see citing works

Bayesian data analysis, third edition

Gelman, A., Carlin, J. B., Stern, H., Dunson, D., Vehtari, A., & Rubin, D. (2013) · Cited by 488 · see citing works

Combinatorial Properties of Matrices of Zeros and Ones

Ryser, H. J. (2009) · Birkhäuser Boston eBooks · doi:10.1007/978-0-8176-4842-8_18 · Cited by 480 · see citing works

Are randomly grown graphs really random?

Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., & Strogatz, S. H. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.041902 · Cited by 451 · see citing works

Hierarchical Block Structures and High-Resolution Model Selection in Large Networks

Peixoto, T. P. (2014) · Physical Review X · doi:10.1103/physrevx.4.011047 · Cited by 383 · see citing works

Detection of topological patterns in complex networks: correlation profile of the internet

Maslov, S., Sneppen, K., & Zaliznyak, A. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2003.06.002 · Cited by 368 · see citing works

Cascade dynamics of complex propagation

Centola, D. M., Eguı́luz, V. M., & Macy, M. W. (2006) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.06.018 · Cited by 361 · see citing works

Cut-offs and finite size effects in scale-free networks

Boguñá, M., Pastor‐Satorras, R., & Vespignani, A. (2004) · The European Physical Journal B · doi:10.1140/epjb/e2004-00038-8 · Cited by 332 · see citing works

Percolation critical exponents in scale-free networks

Reuven Cohen, C., ben‐Avraham, D., & Havlin, S. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.66.036113 · Cited by 313 · see citing works

Onset of synchronization in large networks of coupled oscillators

Restrepo, J. G., Ott, E., & Hunt, B. R. (2005) · Physical Review E · doi:10.1103/physreve.71.036151 · Cited by 312 · see citing works

The Structure of Positive Interpersonal Relations in Small Groups.

Davis, J. A., & Leinhardt, S. (1967) · Cited by 311 · see citing works

Generalized Monte Carlo significance tests

Besag, J. E., & Clifford, P. (1989) · Biometrika · doi:10.1093/biomet/76.4.633 · Cited by 303 · see citing works

Exponential-family random graph models for valued networks

Krivitsky, P. N. (2012) · Electronic Journal of Statistics · doi:10.1214/12-ejs696 · Cited by 298 · see citing works

A Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees

Blitzstein, J. K., & Diaconis, P. W. (2011) · Internet Mathematics · doi:10.1080/15427951.2010.557277 · Cited by 298 · see citing works

RANDOMIZATION OF PRESENCE–ABSENCE MATRICES: COMMENTS AND NEW ALGORITHMS

Miklós, I., & Podani, J. (2004) · Ecology · doi:10.1890/03-0101 · Cited by 286 · see citing works

Sparsification—a technique for speeding up dynamic graph algorithms

Eppstein, D., Galil, Z., Italiano, G. F., & Nissenzweig, A. (1997) · Journal of the ACM · doi:10.1145/265910.265914 · Cited by 282 · see citing works

Graph Isomorphism in Quasipolynomial TimePreprint

Babai, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.03547 · Cited by 277 · see citing works

Comment on "Network Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of Evolved and Designed Networks"

Artzy‐Randrup, Y. A., Fleishman, S. J., Ben‐Tal, N., & Stone, L. (2004) · Science · doi:10.1126/science.1099334 · Cited by 253 · see citing works

On a General Class of Models for Interaction

Strauss, D. (1986) · SIAM Review · doi:10.1137/1028156 · Cited by 252 · see citing works

Estimating and understanding exponential random graph models

Chatterjee, S., & Diaconis, P. (2013) · The Annals of Statistics · doi:10.1214/13-aos1155 · Cited by 238 · see citing works

Sequential Monte Carlo Methods for Statistical Analysis of Tables

Chen, Y., Diaconis, P. W., Holmes, S., & Liu, J. S. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001303 · Cited by 237 · see citing works

Efficient and Simple Generation of Random Simple Connected Graphs with Prescribed Degree Sequence

Viger, F., & Matthieu Latapy, B. (2005) · Lecture notes in computer science · doi:10.1007/11533719_45 · Cited by 233 · see citing works

Subgraphs in random networks

Itzkovitz, S., Milo, R., Kashtan, N., Ziv, G., & Alon, U. S. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.026127 · Cited by 218 · see citing works

A note on the graph isomorphism counting problem

Mathon, R. A. (1979) · Information Processing Letters · doi:10.1016/0020-0190(79)90004-8 · Cited by 202 · see citing works

Predicting Criticality and Dynamic Range in Complex Networks: Effects of Topology

Larremore, D. B., Shew, W. L., & Restrepo, J. G. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.058101 · Cited by 201 · see citing works

Algorithms for constructing graphs and digraphs with given valences and factors

Kleitman, D. J., & Wang, D. (1973) · Discrete Mathematics · doi:10.1016/0012-365x(73)90037-x · Cited by 190 · see citing works

Quantifying randomness in real networks

Orsini, C., Dankulov, M. M., Colomer-de-Simón, P., Jamakovic, A., Mahadevan, P., Vahdat, A. M., Bassler, K. E., Toroczkai, Z., Boguñá, M., Caldarelli, G., et al. (2015) · Nature Communications · doi:10.1038/ncomms9627 · Cited by 185 · see citing works

Evidence for the existence of a robust pattern of prey selection in food webs

Stouffer, D., Camacho, J., Jiang, W.-X., & Amaral, L. A. N. (2007) · Proceedings of the Royal Society of London. Biological Sciences · doi:10.1098/rspb.2007.0571 · Cited by 185 · see citing works

Efficiently inferring community structure in bipartite networks

Larremore, D. B., Clauset, A. J., & Jacobs, A. Z. (2014) · Physical Review E · doi:10.1103/physreve.90.012805 · Cited by 182 · see citing works

The role of mentorship in protégé performance

Malmgren, R. D., Ottino, J. M., & Amaral, L. A. N. (2010) · Nature · doi:10.1038/nature09040 · Cited by 170 · see citing works

Efficient and Exact Sampling of Simple Graphs with Given Arbitrary Degree Sequence

del Genio, C. I., Kim, H., Toroczkai, Z., & Bassler, K. E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010012 · Cited by 170 · see citing works

A Scalable Generative Graph Model with Community Structure

Kolda, T. G., Pınar, A., Plantenga, T. D., & Seshadhri, C. (2014) · SIAM Journal on Scientific Computing · doi:10.1137/130914218 · Cited by 158 · see citing works

Uniform generation of random regular graphs of moderate degree

McKay, B. D., & Wormald, N. (1990) · Journal of Algorithms · doi:10.1016/0196-6774(90)90029-e · Cited by 156 · see citing works

Identifying communities within energy landscapes

Massen, C. P., & Doye, J. P. K. (2005) · Physical Review E · doi:10.1103/physreve.71.046101 · Cited by 153 · see citing works

Dynamic Paternity Allocation as a Function of Male Plumage Color in Barn Swallows

Safran, R. J., Neuman, C. R., McGraw, K. J., & Lovette, I. J. (2005) · Science · doi:10.1126/science.1115090 · Cited by 148 · see citing works

A Sequential Algorithm for Generating Random Graphs

Bayati, M., Kim, J. H., & Saberi, A. (2009) · Algorithmica · doi:10.1007/s00453-009-9340-1 · Cited by 136 · see citing works

Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model

Desmarais, B. A., & Cranmer, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030136 · Cited by 135 · see citing works

Sampling Regular Graphs and a Peer-to-Peer Network

Cooper, C., Dyer, M., & Greenhill, C. (2006) · Combinatorics Probability Computing · doi:10.1017/s0963548306007978 · Cited by 135 · see citing works

SNAP: A General Purpose Network Analysis and Graph Mining LibraryPreprint

Leskovec, J., & Sosič, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07550 · Cited by 134 · see citing works

The Markov Chain Simulation Method for Generating Connected Power Law Random Graphs.

Gkantsidis, C., Mihail, M., & Zegura, E. W. (2003) · Cited by 132 · see citing works

Fast uniform generation of regular graphs

Jerrum, M., & Sinclair, A. (1990) · Theoretical Computer Science · doi:10.1016/0304-3975(90)90164-d · Cited by 118 · see citing works

Mixing time of exponential random graphs

Bhamidi, S., Bresler, G., & Sly, A. (2011) · The Annals of Applied Probability · doi:10.1214/10-aap740 · Cited by 105 · see citing works

A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

Larremore, D. B., Clauset, A. J., & Buckee, C. O. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003268 · Cited by 103 · see citing works

A Markov chain Monte carol method for generating random (0, 1)-matrices with given marginals

Rao, A. R., Jana, R., & Bandyopadhyay, S. (1996) · Cited by 103 · see citing works

"Go with the winners" algorithms

Aldous, D., & Vazirani, U. V. (2002) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365742 · Cited by 101 · see citing works

Generating uniformly distributed random networks

Artzy‐Randrup, Y. A., & Stone, L. (2005) · Physical Review E · doi:10.1103/physreve.72.056708 · Cited by 96 · see citing works

Uncorrelated random networks

Burda, Z., & Krzywicki, A. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.67.046118 · Cited by 90 · see citing works

Cluster Inference by Using Transitivity Indices in Empirical Graphs

Frank, O., & Harary, F. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477895 · Cited by 88 · see citing works

Methods for detecting non-randomness in species co-occurrences: a contribution

Wilson, J. B. (1987) · Oecologia · doi:10.1007/bf00379419 · Cited by 75 · see citing works

On Realizability of a Set of Integers as Degrees of the Vertices of a Linear Graph II. Uniqueness

Hakimi, S. L. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111010 · Cited by 74 · see citing works

An Efficient MCMC Algorithm to Sample Binary Matrices with Fixed Marginals

Verhelst, N. D. (2008) · Psychometrika · doi:10.1007/s11336-008-9062-3 · Cited by 73 · see citing works

Constructing and sampling graphs with a prescribed joint degree distribution

Stanton, I., & Pınar, A. (2012) · ACM Journal of Experimental Algorithmics · doi:10.1145/2133803.2330086 · Cited by 63 · see citing works

Stress response, gut microbial diversity and sexual signals correlate with social interactions

Levin, I. I., Zonana, D. M., Fosdick, B. K., Song, S. J., Knight, R. D., & Safran, R. J. (2016) · Biology Letters · doi:10.1098/rsbl.2016.0352 · Cited by 60 · see citing works

Some issues in monitoring convergence of iterative simulations

Brooks, S. P., & Gelman, S. A. (1998) · Cited by 59 · see citing works

Response to Comment on "Network Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of Evolved and Designed Networks"

Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., & Alon, U. S. (2004) · Science · doi:10.1126/science.1100519 · Cited by 58 · see citing works

Peer-to-peer networks based on random transformations of connected regular undirected graphs

Mahlmann, P., & Schindelhauer, C. (2005) · ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) · doi:10.1145/1073970.1073992 · Cited by 55 · see citing works

A survey of algorithms for exact distributions of test statistics in r × c contingency tables with fixed margins

Verbeek, A., & Kroonenberg, P. M. (1985) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(85)90080-5 · Cited by 53 · see citing works

A Local Switch Markov Chain on Given Degree Graphs with Application in Connectivity of Peer-to-Peer Networks

Feder, T., Guetz, A., Mihail, M., & Saberi, A. (2006) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2006.5 · Cited by 53 · see citing works

Towards Random Uniform Sampling of Bipartite Graphs with given Degree Sequence

Miklós, I., Erdős, P. L., & Soukup, L. (2013) · The Electronic Journal of Combinatorics · doi:10.37236/3028 · Cited by 50 · see citing works

Loops and multiple edges in modularity maximization of networks

Cafieri, S., Hansen, P., & Liberti, L. (2010) · Physical Review E · doi:10.1103/physreve.81.046102 · Cited by 43 · see citing works

Graph cluster randomization: network exposure to multiple universesPreprint

Ugander, J. H., Karrer, B., Bäckström, L., & Kleinberg, J. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.6979 · Cited by 41 · see citing works

Proof of uniform sampling of binary matrices with fixed row sums and column sums for the fast Curveball algorithm

Carstens, C. J. (2015) · Physical Review E · doi:10.1103/physreve.91.042812 · Cited by 41 · see citing works

Performance of Encounternet Tags: Field Tests of Miniaturized Proximity Loggers for Use on Small Birds

Levin, I. I., Zonana, D. M., Burt, J. M., & Safran, R. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137242 · Cited by 38 · see citing works

Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph CollectionsPreprint

Ugander, J. H., Bäckström, L., & Kleinberg, J. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.1548 · Cited by 38 · see citing works

Exact sampling of graphs with prescribed degree correlations

Bassler, K. E., Genio, C. I. D., Erdős, P. L., Miklós, I., & Toroczkai, Z. (2015) · New Journal of Physics · doi:10.1088/1367-2630/17/8/083052 · Cited by 36 · see citing works

A Polynomial Bound on the Mixing Time of a Markov Chain for Sampling Regular Directed Graphs

Greenhill, C. (2011) · The Electronic Journal of Combinatorics · doi:10.37236/721 · Cited by 36 · see citing works

On realizations of a joint degree matrix

Czabarka, É., Dutle, A., Erdős, P. L., & Miklós, I. (2014) · Discrete Applied Mathematics · doi:10.1016/j.dam.2014.10.012 · Cited by 33 · see citing works

Generating constrained random graphs using multiple edge switches

Tabourier, L., Roth, C., & Cointet, J. (2011) · ACM Journal of Experimental Algorithmics · doi:10.1145/1963190.2063515 · Cited by 33 · see citing works

Contrained switchings in graphs

Taylor, R. (1981) · Lecture notes in mathematics · doi:10.1007/bfb0091828 · Cited by 32 · see citing works

Comment on “Subgraphs in random networks”

King, O. D. (2004) · Physical Review E · doi:10.1103/physreve.70.058101 · Cited by 32 · see citing works

A degree sequence problem related to network design

Bienstock, D., & Günlük, O. (1994) · Networks · doi:10.1002/net.3230240402 · Cited by 29 · see citing works

On the Swap-Distances of Different Realizations of a Graphical Degree Sequence

Erdős, P. L., Király, Z., & Miklós, I. (2013) · Combinatorics Probability Computing · doi:10.1017/s0963548313000096 · Cited by 27 · see citing works

The switch Markov chain for sampling irregular graphsPreprint

Greenhill, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.5249 · Cited by 26 · see citing works

Approximate Counting of Graphical Realizations

Erdős, P. L., Kiss, S. Z., Miklós, I., & Soukup, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131300 · Cited by 24 · see citing works

Reply to “Comment on ‘Subgraphs in random networks’ ”

Itzkovitz, S., Milo, R., Kashtan, N., Newman, M. E. J., & Alon, U. S. (2004) · Physical Review E · doi:10.1103/physreve.70.058102 · Cited by 24 · see citing works

Structural sparsity of complex networks: Bounded expansion in random models and real-world graphs

Demaine, E. D., Reidl, F., Rossmanith, P., Villaamil, F. S., Sikdar, S., & Sullivan, B. D. (2019) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2019.05.004 · Cited by 16 · see citing works

Graphic Realizations of Joint-Degree MatricesPreprint

Amanatidis, G., Green, B., & Mihail, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.07076 · Cited by 15 · see citing works

Simple and multigraphic realizations of degree sequences

Eggleton, R. B., & Holton, D. (1981) · Lecture notes in mathematics · doi:10.1007/bfb0091817 · Cited by 15 · see citing works

Switching edges to randomize networks: what goes wrong and how to fix it

Carstens, C. J., & Horadam, K. J. (2016) · Journal of Complex Networks · doi:10.1093/comnet/cnw027 · Cited by 12 · see citing works

The connectivity of graphs of graphs with self-loops and a given degree sequence

Nishimura, J. (2018) · Journal of Complex Networks · doi:10.1093/comnet/cny008 · Cited by 9 · see citing works

Detecting network communities beyond assortativity-related attributes

Liu, X., Murata, T., & Wakita, K. (2014) · Physical Review E · doi:10.1103/physreve.90.012806 · Cited by 8 · see citing works

Traversability of graph space with given degree sequence under edge rewiring

Zhang, G. (2010) · Electronics Letters · doi:10.1049/el.2010.3411 · Cited by 6 · see citing works

Susceptible-infected-susceptible dynamics on the rewired configuration modelPreprint

St-Onge, G., Young, J.-G., Laurence, E., Murphy, C. J. V., & Dubé, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.01740 · Cited by 2 · see citing works

Swap connectivity for two graph spaces between simple and pseudo graphs and disconnectivity for triangle constraintsPreprint

Nishimura, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01951 · Cited by 2 · see citing works

Networks beyond pairwise interactions: Structure and dynamics

Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., & Petri, G. (2020) · Physics Reports · doi:10.1016/j.physrep.2020.05.004 · Cited by 1,646 · see citing works

Networks beyond pairwise interactions: Structure and dynamics

Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J., & Petri, G. (2020) · UCL Discovery (University College London) · Cited by 1,179 · see citing works

Social physics

Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 662 · see citing works

What are higher-order networks?

Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023) · SIAM Review · doi:10.1137/21m1414024 · Cited by 420 · see citing works

Quantifying hierarchy and dynamics in US faculty hiring and retention

Wapman, K. H., Zhang, S., Clauset, A. J., & Larremore, D. B. (2022) · Nature · doi:10.1038/s41586-022-05222-x · Cited by 287 · see citing works

Nestedness in complex networks: Observation, emergence, and implications

Mariani, M. S., Ren, Z.-M., Bascompte, J., & Tessone, C. J. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.04.001 · Cited by 228 · see citing works

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206 · see citing works

Nonparametric Bayesian inference of the microcanonical stochastic block model

Peixoto, T. P. (2017) · Physical review. E · doi:10.1103/physreve.95.012317 · Cited by 196 · see citing works

The use of multilayer network analysis in animal behaviour

Finn, K. R., Silk, M. J., Porter, M. A., & Pinter‐Wollman, N. (2019) · Animal Behaviour · doi:10.1016/j.anbehav.2018.12.016 · Cited by 185 · see citing works

The effectiveness of backward contact tracing in networks

Kojaku, S., Hébert‐Dufresne, L., Mones, E., Lehmann, S., & Ahn, Y. (2021) · Nature Physics · doi:10.1038/s41567-021-01187-2 · Cited by 136 · see citing works

Configuration models of random hypergraphs

Chodrow, P. S. (2020) · Journal of Complex Networks · doi:10.1093/comnet/cnaa018 · Cited by 134 · see citing works

Network statistics of the whole-brain connectome of Drosophila

Lin, A., Yang, R., Dorkenwald, S., Matsliah, A., Sterling, A. R., Schlegel, P., Yu, S.-C., McKellar, C. E., Costa, M., Eichler, K., et al. (2024) · Nature · doi:10.1038/s41586-024-07968-y · Cited by 147 · see citing works

Network science approach to modelling the topology and robustness of supply chain networks: a review and perspective

Perera, S., Bell, M. G. H., & Bliemer, M. C. J. (2017) · Applied Network Science · doi:10.1007/s41109-017-0053-0 · Cited by 114 · see citing works

Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks

St-Onge, G., Sun, H., Allard, A., Hébert‐Dufresne, L., & Ginestra Bianconi, A. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.158301 · Cited by 110 · see citing works

Network motifs and their origins

Stone, L., Simberloff, D., & Artzy‐Randrup, Y. A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006749 · Cited by 100 · see citing works

Multiscale mixing patterns in networks

Peel, L., Delvenne, J., & Lambiotte, R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1713019115 · Cited by 104 · see citing works

Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs

St-Onge, G., Iacopini, I., Latora, V., Barrat, A., Petri, G., Allard, A., & Hébert‐Dufresne, L. (2022) · Communications Physics · doi:10.1038/s42005-021-00788-w · Cited by 95 · see citing works

Construction of and efficient sampling from the simplicial configuration model

Young, J.-G., Petri, G., Vaccarino, F., & Patania, A. (2017) · Physical review. E · doi:10.1103/physreve.96.032312 · Cited by 84 · see citing works

Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks

St-Onge, G., Sun, H., Allard, A., Hébert-Dufresne, L., & Bianconi, G. (2021) · Queen Mary Research Online (Queen Mary University of London) · Cited by 80 · see citing works

Interactive Viral Marketing Through Big Data Analytics, Influencer Networks, AI Integration, and Ethical Dimensions

Theodorakopoulos, L., Theodoropoulou, A., & Klavdianos, C. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20020115 · Cited by 97 · see citing works

Random Graph Modeling

Drobyshevskiy, M., & Турдаков, Д. (2019) · ACM Computing Surveys · doi:10.1145/3369782 · Cited by 67 · see citing works

Randomized Reference Models for Temporal Networks

Gauvin, L., Génois, M., Karsai, M., Kivelä, M., Takaguchi, T., Valdano, E., & Vestergaard, C. L. (2022) · SIAM Review · doi:10.1137/19m1242252 · Cited by 65 · see citing works

Descriptive vs. Inferential Community Detection in Networks

Peixoto, T. P. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009118897 · Cited by 63 · see citing works

A multilayer network model of the coevolution of the spread of a disease and competing opinions

Peng, K., Zheng, L., Lin, V., Lindstrom, M. R., Parkinson, C., Wang, C., Bertozzi, A. L., & Porter, M. A. (2021) · Mathematical Models and Methods in Applied Sciences · doi:10.1142/s0218202521500536 · Cited by 58 · see citing works

Annotated hypergraphs: models and applications

Chodrow, P. S., & Mellor, A. (2020) · Applied Network Science · doi:10.1007/s41109-020-0252-y · Cited by 55 · see citing works

The Local Closure Coefficient

Yin, H., Benson, A. R., & Leskovec, J. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290991 · Cited by 46 · see citing works

A framework for the construction of generative models for mesoscale structure in multilayer networks

Bazzi, M., Jeub, L. G. S., Arenas, À., Howison, S., & Porter, M. A. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023100 · Cited by 45 · see citing works

Reconstructing and Stress Testing Credit Networks

Ramadiah, A., Caccioli, F., & Fricke, D. (2017) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2019.103817 · Cited by 43 · see citing works

Modeling Memory Effects in Activity-Driven Networks

Zino, L., Rizzo, A., & Porfiri, M. (2018) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/18m1171485 · Cited by 41 · see citing works

Nonlinearity + Networks: A 2020 Vision

Porter, M. A. (2020) · Nonlinear systems and complexity · doi:10.1007/978-3-030-44992-6_6 · Cited by 41 · see citing works

Community detection in bipartite networks with stochastic block models

Yen, T.-C., & Larremore, D. B. (2020) · Physical review. E · doi:10.1103/physreve.102.032309 · Cited by 39 · see citing works

Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority

Juul, J. L., & Porter, M. A. (2019) · Physical review. E · doi:10.1103/physreve.99.022313 · Cited by 38 · see citing works

Relating Modularity Maximization and Stochastic Block Models in Multilayer Networks

Pamfil, A. R., Howison, S., Lambiotte, R., & Porter, M. A. (2019) · SIAM Journal on Mathematics of Data Science · doi:10.1137/18m1231304 · Cited by 37 · see citing works

Multiscale core-periphery structure in a global liner shipping network

Kojaku, S., Xu, M., Xia, H., & Masuda, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-35922-2 · Cited by 36 · see citing works

Modularity and Dynamics on Complex Networks

Lambiotte, R., & Schaub, M. T. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108774116 · Cited by 38 · see citing works

Configuration model for correlation matrices preserving the node strength

Masuda, N., Kojaku, S., & Sano, Y. (2018) · Physical review. E · doi:10.1103/physreve.98.012312 · Cited by 31 · see citing works

Space-independent community and hub structure of functional brain networks

Esfahlani, F. Z., Bertolero, M. A., Bassett, D. S., & Betzel, R. F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116612 · Cited by 30 · see citing works

Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership

Tien, J. H., Eisenberg, M. C., Cherng, S. T., & Porter, M. A. (2020) · Applied Network Science · doi:10.1007/s41109-019-0223-3 · Cited by 29 · see citing works

Geography, Host Genetics, and Cross‐Domain Microbial Networks Structure the Skin Microbiota of Fragmented Brazilian Atlantic Forest Frog Populations

Belasen, A. M., Riolo, M. A., Bletz, M. C., Lyra, M. L., Toledo, L. F., & James, T. Y. (2021) · Ecology and Evolution · doi:10.1002/ece3.7594 · Cited by 29 · see citing works

The Role of Directionality, Heterogeneity, and Correlations in Epidemic Risk and Spread

Allard, A., Moore, C., Scarpino, S. V., Althouse, B. M., & Hébert‐Dufresne, L. (2023) · SIAM Review · doi:10.1137/20m1383811 · Cited by 34 · see citing works

Randomizing Hypergraphs Preserving Degree Correlation and Local Clustering

Nakajima, K., Shudo, K., & Masuda, N. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3133380 · Cited by 27 · see citing works

Introduction to correlation networks: Interdisciplinary approaches beyond thresholding

Masuda, N., Boyd, Z. M., Garlaschelli, D., & Mucha, P. J. (2025) · Physics Reports · doi:10.1016/j.physrep.2025.06.002 · Cited by 30 · see citing works

Network constraints on the mixing patterns of binary node metadata

Cinelli, M., Peel, L., Iovanella, A., & Delvenne, J. (2020) · Physical review. E · doi:10.1103/physreve.102.062310 · Cited by 26 · see citing works

Generating Simple Directed Social Network Graphs for Information Spreading

Schweimer, C., Gfrerer, C., Lugstein, F., Pape, D., Velimsky, J. A., Elsässer⋆, R., & Geiger, B. C. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512194 · Cited by 25 · see citing works

Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks

St-Onge, G., Young, J.-G., Laurence, E., Murphy, C. J. V., & Dubé, L. J. (2018) · Physical review. E · doi:10.1103/physreve.97.022305 · Cited by 25 · see citing works

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Humphries, M. D. (2017) · Network Neuroscience · doi:10.1162/netn_a_00020 · Cited by 24 · see citing works

Demarcating geographic regions using community detection in commuting networks with significant self-loops

He, M., Glasser, J., Pritchard, N., Bhamidi, S., & Kaza, N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230941 · Cited by 25 · see citing works

Localization and Universality of Eigenvectors in Directed Random Graphs

Metz, F. L., & Neri, I. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.040604 · Cited by 25 · see citing works

A Laplacian approach to stubborn agents and their role in opinion formation on influence networks

Baumann, F., Sokolov, I. M., & Tyloo, M. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124869 · Cited by 24 · see citing works

Percolation and the Effective Structure of Complex Networks

Allard, A., & Hébert-Dufresne, L. (2019) · Physical Review X · doi:10.1103/physrevx.9.011023 · Cited by 25 · see citing works

The Atlas for the Aspiring Network ScientistPreprint

Coscia, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00863 · Cited by 48 · see citing works

Toward Principles of Brain Network Organization and Function

Kulkarni, S., & Bassett, D. S. (2025) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-030722-110624 · Cited by 26 · see citing works

Multilayer network analyses as a toolkit for measuring social structure

Finn, K. R. (2021) · Current Zoology · doi:10.1093/cz/zoaa079 · Cited by 22 · see citing works

Interplay between $$k$$-core and community structure in complex networks

Malvestio, I., Cardillo, A., & Masuda, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-71426-8 · Cited by 20 · see citing works

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

Nordick, B., & Hong, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04405-z · Cited by 20 · see citing works

Birds of a Feather Flock Together Online: Digital Inequality in Social Media Repertoires

Horvát, E.-Á., & Hargittai, E. (2021) · Social Media + Society · doi:10.1177/20563051211052897 · Cited by 21 · see citing works

Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512962 · Cited by 20 · see citing works

Synergistic effects in threshold models on networks

Juul, J. L., & Porter, M. A. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5017962 · Cited by 18 · see citing works

Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks

Cai, Q., Pratama, M., & Alam, S. (2019) · Complexity · doi:10.1155/2019/2680972 · Cited by 19 · see citing works

Random graphs with arbitrary clustering and their applications

Mann, P., Smith, V. A., Mitchell, J. B. O., & Dobson, S. (2021) · Physical review. E · doi:10.1103/physreve.103.012309 · Cited by 18 · see citing works

Gillespie Algorithms for Stochastic Multiagent Dynamics in Populations and Networks

Masuda, N., & Vestergaard, C. L. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009239158 · Cited by 18 · see citing works

Message-Passing Methods for Complex Contagions

Gleeson, J. P., & Porter, M. A. (2018) · Computational social sciences · doi:10.1007/978-3-319-77332-2_5 · Cited by 18 · see citing works

Configuration models as an urn problem

Casiraghi, G., & Nanumyan, V. (2021) · Scientific Reports · doi:10.1038/s41598-021-92519-y · Cited by 18 · see citing works

Analytic solution of the resolvent equations for heterogeneous random graphs: spectral and localization properties

Silva, J. D., & Metz, F. L. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/aca9b1 · Cited by 17 · see citing works

Complex contagions with timers

Oh, S.-W., & Porter, M. A. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4990038 · Cited by 17 · see citing works

Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks

Metz, F. L. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.037401 · Cited by 16 · see citing works

The HyperKron Graph Model for Higher-Order Features

Eikmeier, N., Ramani, A. S., & Gleich, D. F. (2018) · Proceedings · doi:10.1109/icdm.2018.00115 · Cited by 16 · see citing works

A data-driven network approach for characterization of political parties’ ideology dynamics

Faustino, J., Barbosa, H., Ribeiro, E., & de Menezes, R. P. (2019) · Applied Network Science · doi:10.1007/s41109-019-0161-0 · Cited by 16 · see citing works

A wrap-around movement path randomization method to distinguish social and spatial drivers of animal interactions

Gahm, K., Nguyen, R., Acácio, M., Anglister, N., Vaadia, G., Spiegel, O., & Pinter-Wollman, N. (2024) · Philosophical Transactions B · doi:10.1098/rstb.2022.0531 · Cited by 5 · see citing works

Measuring directed triadic closure with closure coefficients

Yin, H., Benson, A. R., & Ugander, J. H. (2020) · Network Science · doi:10.1017/nws.2020.20 · Cited by 15 · see citing works

Measuring directed triadic closure with closure coefficients

Yin, H., Benson, A. R., & Ugander, J. H. (2020) · RePEc: Research Papers in Economics · Cited by 15 · see citing works

Graph Theory Approach to Detect Examinees Involved in Test Collusion

Belov, D. I., & Wollack, J. A. (2021) · Applied Psychological Measurement · doi:10.1177/01466216211013902 · Cited by 14 · see citing works

Mean-field approach to evolving spatial networks, with an application to osteocyte network formation

Taylor‐King, J. P., Basanta, D., Chapman, S. J., & Porter, M. A. (2017) · Physical review. E · doi:10.1103/physreve.96.012301 · Cited by 14 · see citing works

Latent Space Modeling of Hypergraph Data

Turnbull, K., Lunagómez, S., Nemeth, C., & Airoldi, E. M. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2270750 · Cited by 12 · see citing works

Measuring Quadrangle Formation in Complex Networks

Jia, M., Gabryś, B., & Musiał, K. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3123735 · Cited by 12 · see citing works

Multiplex Network Regression: How do relations drive interactions?Preprint

Casiraghi, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.02048 · Cited by 12 · see citing works

Bursts of communication increase opinion diversity in the temporal Deffuant model

Zarei, F., Gandica, Y., & Rocha, L. E. C. (2024) · Scientific Reports · doi:10.1038/s41598-024-52458-w · Cited by 11 · see citing works

Distributed network flows generate localized category selectivity in human visual cortex

Cocuzza, C. V., Sánchez-Romero, R., Ito, T., Mill, R. D., Keane, B. P., & Cole, M. W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012507 · Cited by 12 · see citing works

Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis

Koevering, K. V., Benson, A. R., & Kleinberg, J. M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450001 · Cited by 11 · see citing works

A guide to choosing and implementing reference models for social network analysis

Hobson, E. A., Silk, M. J., Fefferman, N. H., Larremore, D. B., Rombach, P., Shai, S., & Pinter‐Wollman, N. (2021) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12775 · Cited by 11 · see citing works

A generalized hypothesis test for community structure in networks

Yanchenko, E., & Sengupta, S. (2024) · Network Science · doi:10.1017/nws.2024.1 · Cited by 10 · see citing works

Systematic assessment of the quality of fit of the stochastic block model for empirical networks

Vaca-Ramírez, F., & Peixoto, T. P. (2022) · Physical review. E · doi:10.1103/physreve.105.054311 · Cited by 11 · see citing works

Breakup of Directed Multipartite Networks

Cai, Q., Pratama, M., Alam, S., Ma, C., & Liu, J. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2894142 · Cited by 10 · see citing works

Predicting the diversity of early epidemic spread on networks

Allen, A. J., Boudreau, M. C., Roberts, N. J., Allard, A., & Hébert‐Dufresne, L. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013123 · Cited by 10 · see citing works

Construction and Random Generation of Hypergraphs with Prescribed Degree and Dimension Sequences

Arafat, N. A., Basu, D., Decreusefond, L., & Bressan, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59051-2_9 · Cited by 10 · see citing works

Towards quantifying the communication aspect of resilience in disaster-prone communities

Okeukwu-Ogbonnaya, A., Amariucai, G. T., Natarajan, B., & Kim, H. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-59192-3 · Cited by 10 · see citing works

Duality between predictability and reconstructability in complex systems

Murphy, C., Thibeault, V., Allard, A., & Desrosiers, P. (2024) · Nature Communications · doi:10.1038/s41467-024-48020-x · Cited by 9 · see citing works

Generating graphs randomly

Greenhill, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009036214.005 · Cited by 9 · see citing works

MCMC sampling of directed flag complexes with fixed undirected graphs

Unger, F., & Krebs, J. (2024) · Journal of Applied and Computational Topology · doi:10.1007/s41468-024-00178-5 · Cited by 9 · see citing works

The connectivity of graphs of graphs with self-loops and a given degree sequence

Nishimura, J. (2018) · Journal of Complex Networks · doi:10.1093/comnet/cny008 · Cited by 9 · see citing works

Multistage onset of epidemics in heterogeneous networks

Cai, C.-R., Wu, Z.-X., & Holme, P. (2021) · Physical review. E · doi:10.1103/physreve.103.032313 · Cited by 9 · see citing works

Latent Poisson models for networks with heterogeneous density

Peixoto, T. P. (2020) · Physical review. E · doi:10.1103/physreve.102.012309 · Cited by 9 · see citing works

Residual2Vec: Debiasing graph embedding with random graphsPreprint

Kojaku, S., Yoon, J., Constantino, I., & Ahn, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07654 · Cited by 9 · see citing works

A bipartite graph-based expected networks approach identifies DDR genes not associated with TMB yet predictive of immune checkpoint blockade response

Weir, W. H., Mucha, P. J., & Kim, W. Y. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100602 · Cited by 8 · see citing works

Generating connected random graphs

Gray, C., Mitchell, L., & Roughan, M. (2019) · Journal of Complex Networks · doi:10.1093/comnet/cnz011 · Cited by 8 · see citing works

Degree correlations in graphs with clique clustering

Mann, P., Smith, V. A., Mitchell, J. B. O., & Dobson, S. (2022) · Physical review. E · doi:10.1103/physreve.105.044314 · Cited by 8 · see citing works

Assessment of model fit via network comparison methods based on subgraph counts

Ospina-Forero, L., Deane, C. M., & Reinert, G. (2018) · Journal of Complex Networks · doi:10.1093/comnet/cny017 · Cited by 8 · see citing works

Concurrency and reachability in treelike temporal networks

Lee, E., Emmons, S., Gibson, R., Moody, J. W., & Mucha, P. J. (2019) · Physical review. E · doi:10.1103/physreve.100.062305 · Cited by 8 · see citing works

Inference of edge correlations in multilayer networks

Pamfil, A. R., Howison, S., & Porter, M. A. (2020) · Physical review. E · doi:10.1103/physreve.102.062307 · Cited by 13 · see citing works

Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance

Fountoulakis, K., Liu, M., Gleich, D. F., & Mahoney, M. W. (2023) · SIAM Review · doi:10.1137/20m1333055 · Cited by 11 · see citing works

Counting graphs and null models of complex networks:Configuration model and extensions

van der Hofstad, R. (2017) · TU/e Research Portal

Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis

Koevering, K. V., Benson, A. R., & Kleinberg, J. M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450001 · Cited by 11 · see citing works

Sampling Random Graphs from the Colored Configuration Model

Pellegrina, L. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817950

Configuration Model

van der Hofstad, R. (2016) · Cambridge University Press eBooks · doi:10.1017/9781316779422.010 · Cited by 1 · see citing works

Configuration Models as an Urn Problem: The Generalized Hypergeometric Ensemble of Random GraphsPreprint

Casiraghi, G., & Nanumyan, V. (2021) · Research Square · doi:10.21203/rs.3.rs-254843/v1