Structural Controllability of Complex Networks Based on Preferential Matching

Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network.

[1]  David A. Smith,et al.  Structure and Dynamics of the Global Economy: Network Analysis of International Trade 1965–1980 , 1992 .

[2]  K. Norlen 1 EVA : Extraction , Visualization and Analysis of the Telecommunications and Media Ownership Network , 2002 .

[3]  D. Watts Networks, Dynamics, and the Small‐World Phenomenon1 , 1999, American Journal of Sociology.

[4]  Derek Ruths,et al.  Control Profiles of Complex Networks , 2014, Science.

[5]  Richard M. Karp,et al.  A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.

[6]  Noah J. Cowan,et al.  Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks , 2011, PloS one.

[7]  Robert E. Ulanowicz,et al.  Ascendency as an ecological indicator: a case study of estuarine pulse eutrophication , 2004 .

[8]  Jie Ren,et al.  Controlling complex networks: How much energy is needed? , 2012, Physical review letters.

[9]  Alessandro Vespignani Modelling dynamical processes in complex socio-technical systems , 2011, Nature Physics.

[10]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[11]  Christian Commault,et al.  Characterization of generic properties of linear structured systems for efficient computations , 2002, Kybernetika.

[12]  Marián Boguñá,et al.  Popularity versus similarity in growing networks , 2011, Nature.

[13]  Tamás Vicsek,et al.  Controlling edge dynamics in complex networks , 2011, Nature Physics.

[14]  Ching-tai Lin Structural controllability , 1974 .

[15]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[16]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[17]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[18]  Christian Commault,et al.  Generic properties and control of linear structured systems: a survey , 2003, Autom..

[19]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[20]  Jure Leskovec,et al.  Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters , 2008, Internet Math..

[21]  F. Müller,et al.  Few inputs can reprogram biological networks , 2011, Nature.

[22]  Andrew Parker,et al.  The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations , 2004 .

[23]  Tore Opsahl,et al.  Clustering in weighted networks , 2009, Soc. Networks.

[24]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[25]  Richard M. Karp,et al.  A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.

[26]  Y. Lai,et al.  Optimizing controllability of complex networks by minimum structural perturbations. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[28]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Lenka Zdeborová,et al.  The number of matchings in random graphs , 2006, ArXiv.

[30]  Leslie G. Valiant,et al.  The Complexity of Computing the Permanent , 1979, Theor. Comput. Sci..

[31]  L. Aravind,et al.  Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. , 2006, Journal of molecular biology.

[32]  Robert R. Christian,et al.  Organizing and understanding a winter's seagrass foodweb network through effective trophic levels , 1999 .

[33]  Neo D. Martinez,et al.  Food-web structure and network theory: The role of connectance and size , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[35]  Neo D. Martinez Artifacts or Attributes? Effects of Resolution on the Little Rock Lake Food Web , 1991 .

[36]  C Berge,et al.  TWO THEOREMS IN GRAPH THEORY. , 1957, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Jacob G Foster,et al.  Edge direction and the structure of networks , 2009, Proceedings of the National Academy of Sciences.

[38]  Gourab Ghoshal,et al.  Ranking stability and super-stable nodes in complex networks. , 2011, Nature communications.

[39]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[40]  Wouter de Nooy,et al.  A literary playground: Literary criticism and balance theory , 1999 .

[41]  Ljupco Kocarev,et al.  Identifying communities by influence dynamics in social networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.