January  2014, 1(1): 191-212. doi: 10.3934/jcd.2014.1.191

Modularity revisited: A novel dynamics-based concept for decomposing complex networks

1. 

Freie Universität Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany, Germany, Germany, Germany, Germany

Received  December 2011 Revised  July 2012 Published  April 2014

Finding modules (or clusters) in large, complex networks is a challenging task, in particular if one is not interested in a full decomposition of the whole network into modules. We consider modular networks that also contain nodes that do not belong to one of modules but to several or to none at all. A new method for analyzing such networks is presented. It is based on spectral analysis of random walks on modular networks. In contrast to other spectral clustering approaches, we use different transition rules of the random walk. This leads to much more prominent gaps in the spectrum of the adapted random walk and allows for easy identification of the network's modular structure, and also identifying the nodes belonging to these modules. We also give a characterization of that set of nodes that do not belong to any module, which we call transition region. Finally, by analyzing the transition region, we describe an algorithm that identifies so called hub-nodes inside the transition region that are important connections between modules or between a module and the rest of the network. The resulting algorithms scale linearly with network size (if the network connectivity is sparse) and thus can also be applied to very large networks.
Citation: Marco Sarich, Natasa Djurdjevac Conrad, Sharon Bruckner, Tim O. F. Conrad, Christof Schütte. Modularity revisited: A novel dynamics-based concept for decomposing complex networks. Journal of Computational Dynamics, 2014, 1 (1) : 191-212. doi: 10.3934/jcd.2014.1.191
References:
[1]

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[2]

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M. Dellnitz and R. Preis, Congestion and almost invariant sets in dynamical systems,, in Proceedings of the 2nd international conference on Symbolic and numerical scientific computation, (2003), 183.  doi: 10.1007/3-540-45084-X_8.  Google Scholar

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[8]

P. Deuflhard and M. Weber, Robust Perron cluster analysis in conformation dynamics,, Linear Algebra and its Applications, 398 (2005), 161.  doi: 10.1016/j.laa.2004.10.026.  Google Scholar

[9]

N. Djurdjevac, S. Bruckner, T. O. F. Conrad and C. Schuette, Random walks on complex modular networks,, Journal of Numerical Analysis, ().   Google Scholar

[10]

N. Djurdjevac, M. Sarich and C. Schuette, On Markov state models for metastable processes,, Proceeding of the ICM 2010, (2010), 3105.  doi: 10.1142/9789814324359_0182.  Google Scholar

[11]

N. Djurdjevac, M. Sarich and C. Schütte, Estimating the eigenvalue error of markov state models,, Multiscale Modeling & Simulation, 10 (2012), 61.  doi: 10.1137/100798910.  Google Scholar

[12]

P. Doyle and J. Snell, Random Walks and Electric Networks,, Carus Mathematical Monographs, (1984).   Google Scholar

[13]

W. E and E. Vanden-Eijnden, Transition-path theory and path-finding algorithms for the study of rare events,, Annual Review of Physical Chemistry, 61 (2010), 391.  doi: 10.1146/annurev.physchem.040808.090412.  Google Scholar

[14]

S. Fortunato, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

[15]

P. Garrido and J. Marro (eds.), Exploring Complex Graphs by Random Walks, vol. 661,, American Institute of Physics, (2002).   Google Scholar

[16]

M. Girvan and M. E. J. Newman, Community structure in social and biological networks,, Proceedings of the National Academy of Sciences, 99 (2002), 7821.  doi: 10.1073/pnas.122653799.  Google Scholar

[17]

W. Huisinga and B. Schmidt, Metastability and dominant eigenvalues of transfer operators,, in New Algorithms for Macromolecular Simulation (eds. B. Leimkuhler, (2006), 167.  doi: 10.1007/3-540-31618-3_11.  Google Scholar

[18]

H. Jeong, B. Tombor, R. Albert, Z. Oltvai and A. Barabasi, The large-scale organization of metabolic networks,, Nature, 407 (2000), 651.   Google Scholar

[19]

S. Lafon and A. Lee, Diffusion maps and coarse-graining: A unified framework for dimensionality reduction,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), 1393.  doi: 10.1109/TPAMI.2006.184.  Google Scholar

[20]

T. Li, W. E and E. V. Eijnden, Optimal partition and effective dynamics of complex networks,, Proc. Nat. Acad. Sci., 105 ().   Google Scholar

[21]

T. Li, J. Liu and W. E, A probabilistic framework for network partition,, Phys. Rev. E, 80 (2009).  doi: 10.1103/PhysRevE.80.026106.  Google Scholar

[22]

L. Lovasz, Random walks on graphs: A survey.,, Bolyayi Society Mathematical Studies, 2 (1996), 353.   Google Scholar

[23]

U. Luxburg, A tutorial on spectral clustering,, Statistics and Computing, 17 (2007), 395.  doi: 10.1007/s11222-007-9033-z.  Google Scholar

[24]

I. Marek and P. Mayer, Aggregation/disaggregation iterative methods applied to Leontev and Markov chain models,, Appl. Math., 47 (2002), 139.  doi: 10.1023/A:1021785102298.  Google Scholar

[25]

R. Mattingly, A revised stochastic complementation algorithm for nearly completely decomposable Markov chains,, ORSA Journal on Computing, 7 (1995), 117.  doi: 10.1287/ijoc.7.2.117.  Google Scholar

[26]

E. Meerbach, C. Schuette and A. Fischer, Eigenvalue bounds on restrictions of reversible nearly uncoupled Markov chains,, Lin. Alg. Appl., 398 (2005), 141.  doi: 10.1016/j.laa.2004.10.018.  Google Scholar

[27]

M. Meila and J. Shi, A random walks view of spectral segmentation,, AI and Statistics (AISTATS)., ().   Google Scholar

[28]

P. Metzner, C. Schuette and E. Vanden-Eijnden, Transition path theory for Markov jump processes,, Multiscale Modeling and Simulation, 7 (2009), 1192.  doi: 10.1137/070699500.  Google Scholar

[29]

C. D. Meyer, Stochastic complementation, uncoupling Markov chains, and the theory of nearly reducible systems,, SIAM Rev, 31 (1989), 240.  doi: 10.1137/1031050.  Google Scholar

[30]

M. C. V. Nascimento and A. C. P. L. F. De Carvalho, Spectral methods for graph clustering: A survey,, European Journal Of Operational Research, 211 (2011), 221.  doi: 10.1016/j.ejor.2010.08.012.  Google Scholar

[31]

M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices,, PHYS.REV.E, 74 (2006).  doi: 10.1103/PhysRevE.74.036104.  Google Scholar

[32]

M. Newman, The structure and function of complex networks,, SIAM Review, 45 (2003), 167.  doi: 10.1137/S003614450342480.  Google Scholar

[33]

M. Newman, A. Barabasi and D. Watts, The Structure and Dynamics of Networks,, Princeton Univ Press, (2006).   Google Scholar

[34]

M. Newman and M. Girvan, Finding and evaluating community structure in networks,, Phys. Rev. E, 69 (2004).  doi: 10.1103/PhysRevE.69.026113.  Google Scholar

[35]

V. Nicosia, G. Mangioni, V. Carchiolo and M. Malgeri, Extending the definition of modularity to directed graphs with overlapping communities,, Journal of Statistical Mechanics: Theory and Experiment, 2009 (2009).  doi: 10.1088/1742-5468/2009/03/P03024.  Google Scholar

[36]

J. Noh and H. Rieger, Random walks on complex networks,, Phys. Rev. Lett., 92 (2004).  doi: 10.1103/PhysRevLett.92.118701.  Google Scholar

[37]

G. Palla, I. Derenyi, I. Farkas and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society,, Nature, 435 (2005), 814.  doi: 10.1038/nature03607.  Google Scholar

[38]

M. A. Porter, J.-P. Onnela and P. J. Mucha, Communities in networks,, World Wide Web Internet And Web Information Systems, 56 (2009), 1082.   Google Scholar

[39]

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection,, PHYS.REV.E, 74 (2006).  doi: 10.1103/PhysRevE.74.016110.  Google Scholar

[40]

M. Rosvall and C. Bergstrom, Maps of random walks on complex networks reveal community structure,, Proc Natl Acad Sci, 105 (2008), 1118.  doi: 10.1073/pnas.0706851105.  Google Scholar

[41]

F. Santo, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

[42]

M. Sarich and C. Schuette, Approximating selected non-dominant timescales by markov state models,, Comm Math Sci., 10 (2012), 1001.  doi: 10.4310/CMS.2012.v10.n3.a14.  Google Scholar

[43]

M. Sarich, C. Schuette and E. Vanden-Eijnden, Optimal fuzzy aggregation of networks,, Multiscale Modeling and Simulation, 8 (2010), 1535.  doi: 10.1137/090758519.  Google Scholar

[44]

M. Sarich, Projected Transfer Operators,, PhD thesis, (2011).   Google Scholar

[45]

C. Schuette and W. Huisinga, Biomolecular conformations can be identified as metastable sets of molecular dynamics,, in Handbook of Numerical Analysis, X (2003), 699.   Google Scholar

[46]

C. Schuette, F. Noe, J. Lu, M. Sarich and E. Vanden-Eijnden, State models based on milestoning,, J. Chem. Phys, 134 (2011).  doi: 10.1063/1.3590108.  Google Scholar

[47]

S. Van Dongen, Graph Clustering by Flow Simulation,, PhD thesis, (2000).   Google Scholar

show all references

References:
[1]

A. H. Al-Mohy and N. J. Higham, Computing the action of the matrix exponential, with an application to exponential integrators,, SIAM J. Sci. Comput., 33 (2011), 488.  doi: 10.1137/100788860.  Google Scholar

[2]

R. Albert and A. Barabasi, Statistical mechanics of complex networks,, Rev. Mod. Phys., 74 (2002), 47.  doi: 10.1103/RevModPhys.74.47.  Google Scholar

[3]

D. Aldous and J. Fill, Reversible Markov Chains and Random Walks on Graphs,, University of California, (2002).   Google Scholar

[4]

M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation,, Neural Computation, 6 (2003), 1373.  doi: 10.1162/089976603321780317.  Google Scholar

[5]

G. Cho and C. Meyer, Aggregation/Disaggregation Methods for Nearly Uncoupled MArkov Chains,, Technical Report NCSU no. 041600-0400, (0416), 041600.   Google Scholar

[6]

M. Dellnitz and R. Preis, Congestion and almost invariant sets in dynamical systems,, in Proceedings of the 2nd international conference on Symbolic and numerical scientific computation, (2003), 183.  doi: 10.1007/3-540-45084-X_8.  Google Scholar

[7]

P. Deuflhard, W. Huisinga, A. Fischer and C. Schuette, Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains,, Linear Algebra and its Applications, 315 (2000), 39.  doi: 10.1016/S0024-3795(00)00095-1.  Google Scholar

[8]

P. Deuflhard and M. Weber, Robust Perron cluster analysis in conformation dynamics,, Linear Algebra and its Applications, 398 (2005), 161.  doi: 10.1016/j.laa.2004.10.026.  Google Scholar

[9]

N. Djurdjevac, S. Bruckner, T. O. F. Conrad and C. Schuette, Random walks on complex modular networks,, Journal of Numerical Analysis, ().   Google Scholar

[10]

N. Djurdjevac, M. Sarich and C. Schuette, On Markov state models for metastable processes,, Proceeding of the ICM 2010, (2010), 3105.  doi: 10.1142/9789814324359_0182.  Google Scholar

[11]

N. Djurdjevac, M. Sarich and C. Schütte, Estimating the eigenvalue error of markov state models,, Multiscale Modeling & Simulation, 10 (2012), 61.  doi: 10.1137/100798910.  Google Scholar

[12]

P. Doyle and J. Snell, Random Walks and Electric Networks,, Carus Mathematical Monographs, (1984).   Google Scholar

[13]

W. E and E. Vanden-Eijnden, Transition-path theory and path-finding algorithms for the study of rare events,, Annual Review of Physical Chemistry, 61 (2010), 391.  doi: 10.1146/annurev.physchem.040808.090412.  Google Scholar

[14]

S. Fortunato, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

[15]

P. Garrido and J. Marro (eds.), Exploring Complex Graphs by Random Walks, vol. 661,, American Institute of Physics, (2002).   Google Scholar

[16]

M. Girvan and M. E. J. Newman, Community structure in social and biological networks,, Proceedings of the National Academy of Sciences, 99 (2002), 7821.  doi: 10.1073/pnas.122653799.  Google Scholar

[17]

W. Huisinga and B. Schmidt, Metastability and dominant eigenvalues of transfer operators,, in New Algorithms for Macromolecular Simulation (eds. B. Leimkuhler, (2006), 167.  doi: 10.1007/3-540-31618-3_11.  Google Scholar

[18]

H. Jeong, B. Tombor, R. Albert, Z. Oltvai and A. Barabasi, The large-scale organization of metabolic networks,, Nature, 407 (2000), 651.   Google Scholar

[19]

S. Lafon and A. Lee, Diffusion maps and coarse-graining: A unified framework for dimensionality reduction,, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2006), 1393.  doi: 10.1109/TPAMI.2006.184.  Google Scholar

[20]

T. Li, W. E and E. V. Eijnden, Optimal partition and effective dynamics of complex networks,, Proc. Nat. Acad. Sci., 105 ().   Google Scholar

[21]

T. Li, J. Liu and W. E, A probabilistic framework for network partition,, Phys. Rev. E, 80 (2009).  doi: 10.1103/PhysRevE.80.026106.  Google Scholar

[22]

L. Lovasz, Random walks on graphs: A survey.,, Bolyayi Society Mathematical Studies, 2 (1996), 353.   Google Scholar

[23]

U. Luxburg, A tutorial on spectral clustering,, Statistics and Computing, 17 (2007), 395.  doi: 10.1007/s11222-007-9033-z.  Google Scholar

[24]

I. Marek and P. Mayer, Aggregation/disaggregation iterative methods applied to Leontev and Markov chain models,, Appl. Math., 47 (2002), 139.  doi: 10.1023/A:1021785102298.  Google Scholar

[25]

R. Mattingly, A revised stochastic complementation algorithm for nearly completely decomposable Markov chains,, ORSA Journal on Computing, 7 (1995), 117.  doi: 10.1287/ijoc.7.2.117.  Google Scholar

[26]

E. Meerbach, C. Schuette and A. Fischer, Eigenvalue bounds on restrictions of reversible nearly uncoupled Markov chains,, Lin. Alg. Appl., 398 (2005), 141.  doi: 10.1016/j.laa.2004.10.018.  Google Scholar

[27]

M. Meila and J. Shi, A random walks view of spectral segmentation,, AI and Statistics (AISTATS)., ().   Google Scholar

[28]

P. Metzner, C. Schuette and E. Vanden-Eijnden, Transition path theory for Markov jump processes,, Multiscale Modeling and Simulation, 7 (2009), 1192.  doi: 10.1137/070699500.  Google Scholar

[29]

C. D. Meyer, Stochastic complementation, uncoupling Markov chains, and the theory of nearly reducible systems,, SIAM Rev, 31 (1989), 240.  doi: 10.1137/1031050.  Google Scholar

[30]

M. C. V. Nascimento and A. C. P. L. F. De Carvalho, Spectral methods for graph clustering: A survey,, European Journal Of Operational Research, 211 (2011), 221.  doi: 10.1016/j.ejor.2010.08.012.  Google Scholar

[31]

M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices,, PHYS.REV.E, 74 (2006).  doi: 10.1103/PhysRevE.74.036104.  Google Scholar

[32]

M. Newman, The structure and function of complex networks,, SIAM Review, 45 (2003), 167.  doi: 10.1137/S003614450342480.  Google Scholar

[33]

M. Newman, A. Barabasi and D. Watts, The Structure and Dynamics of Networks,, Princeton Univ Press, (2006).   Google Scholar

[34]

M. Newman and M. Girvan, Finding and evaluating community structure in networks,, Phys. Rev. E, 69 (2004).  doi: 10.1103/PhysRevE.69.026113.  Google Scholar

[35]

V. Nicosia, G. Mangioni, V. Carchiolo and M. Malgeri, Extending the definition of modularity to directed graphs with overlapping communities,, Journal of Statistical Mechanics: Theory and Experiment, 2009 (2009).  doi: 10.1088/1742-5468/2009/03/P03024.  Google Scholar

[36]

J. Noh and H. Rieger, Random walks on complex networks,, Phys. Rev. Lett., 92 (2004).  doi: 10.1103/PhysRevLett.92.118701.  Google Scholar

[37]

G. Palla, I. Derenyi, I. Farkas and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society,, Nature, 435 (2005), 814.  doi: 10.1038/nature03607.  Google Scholar

[38]

M. A. Porter, J.-P. Onnela and P. J. Mucha, Communities in networks,, World Wide Web Internet And Web Information Systems, 56 (2009), 1082.   Google Scholar

[39]

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection,, PHYS.REV.E, 74 (2006).  doi: 10.1103/PhysRevE.74.016110.  Google Scholar

[40]

M. Rosvall and C. Bergstrom, Maps of random walks on complex networks reveal community structure,, Proc Natl Acad Sci, 105 (2008), 1118.  doi: 10.1073/pnas.0706851105.  Google Scholar

[41]

F. Santo, Community detection in graphs,, Physics Reports, 486 (2010), 75.  doi: 10.1016/j.physrep.2009.11.002.  Google Scholar

[42]

M. Sarich and C. Schuette, Approximating selected non-dominant timescales by markov state models,, Comm Math Sci., 10 (2012), 1001.  doi: 10.4310/CMS.2012.v10.n3.a14.  Google Scholar

[43]

M. Sarich, C. Schuette and E. Vanden-Eijnden, Optimal fuzzy aggregation of networks,, Multiscale Modeling and Simulation, 8 (2010), 1535.  doi: 10.1137/090758519.  Google Scholar

[44]

M. Sarich, Projected Transfer Operators,, PhD thesis, (2011).   Google Scholar

[45]

C. Schuette and W. Huisinga, Biomolecular conformations can be identified as metastable sets of molecular dynamics,, in Handbook of Numerical Analysis, X (2003), 699.   Google Scholar

[46]

C. Schuette, F. Noe, J. Lu, M. Sarich and E. Vanden-Eijnden, State models based on milestoning,, J. Chem. Phys, 134 (2011).  doi: 10.1063/1.3590108.  Google Scholar

[47]

S. Van Dongen, Graph Clustering by Flow Simulation,, PhD thesis, (2000).   Google Scholar

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