Core-Periphery in Networks: An Axiomatic Approach

Recent evidence shows that in many societies worldwide the relative sizes of the economic and social elites are continuously shrinking. Is this a natural social phenomenon? What are the forces that shape this process? We try to address these questions by studying a Core-Periphery social structure composed of a social elite, namely, a relatively small but well-connected and highly influential group of powerful individuals, and the rest of society, the periphery. Herein, we present a novel axiom-based model for the forces governing the mutual influences between the elite and the periphery. Assuming a simple set of axioms, capturing the elite's dominance, robustness, compactness and density, we are able to draw strong conclusions about the elite-periphery structure. In particular, we show that a balance of powers between elite and periphery and an elite size that is sub-linear in the network size are universal properties of elites in social networks that satisfy our axioms. We note that the latter is in controversy to the common belief that the elite size converges to a linear fraction of society (most recently claimed to be 1%). We accompany these findings with a large scale empirical study on about 100 real-world networks, which supports our results.

[1]  K. Selçuk Candan,et al.  How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? , 2010, ICWSM.

[2]  Jure Leskovec,et al.  Overlapping Communities Explain Core–Periphery Organization of Networks , 2014, Proceedings of the IEEE.

[3]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[4]  S. Wasserman,et al.  Blockmodels: Interpretation and evaluation , 1992 .

[5]  Michael Ley,et al.  The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives , 2002, SPIRE.

[6]  Ling-Yun Wu,et al.  Structure and dynamics of core/periphery networks , 2013, J. Complex Networks.

[7]  P. Holme Core-periphery organization of complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Yiming Yang,et al.  Introducing the Enron Corpus , 2004, CEAS.

[9]  Paolo Avesani,et al.  Controversial Users Demand Local Trust Metrics: An Experimental Study on Epinions.com Community , 2005, AAAI.

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

[11]  Marko Bajec,et al.  Model of complex networks based on citation dynamics , 2013, WWW.

[12]  J. H. Rogers,et al.  The mind and society : Trattato di sociologia generale , 1935 .

[13]  Daniel A. Hojman,et al.  Core and periphery in networks , 2008, J. Econ. Theory.

[14]  J. Miro-Julia,et al.  Marvel Universe looks almost like a real social network , 2002 .

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

[16]  Rami Puzis,et al.  Link Prediction in Highly Fractional Data Sets , 2013 .

[17]  Luciano da Fontoura Costa,et al.  Rich-club phenomenon across complex network hierarchies , 2007 .

[18]  M. De Domenico,et al.  The Anatomy of a Scientific Rumor , 2013, Scientific Reports.

[19]  Guilin Qi,et al.  Zhishi.me - Weaving Chinese Linking Open Data , 2011, SEMWEB.

[20]  Ian T. Foster,et al.  Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design , 2002, ArXiv.

[21]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[22]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[23]  Martin G. Everett,et al.  Analyzing social networks , 2013 .

[24]  Jure Leskovec,et al.  Image Labeling on a Network: Using Social-Network Metadata for Image Classification , 2012, ECCV.

[25]  Jon M. Kleinberg,et al.  Overview of the 2003 KDD Cup , 2003, SKDD.

[26]  A. Arenas,et al.  Models of social networks based on social distance attachment. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Xiao Zhang,et al.  Identification of core-periphery structure in networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Vaclav Petricek,et al.  Recommender System for Online Dating Service , 2007, ArXiv.

[29]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[30]  Azadeh Iranmehr,et al.  Trust Management for Semantic Web , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[31]  Peter Bailey,et al.  Engineering a multi-purpose test collection for Web retrieval experiments , 2003, Inf. Process. Manag..

[32]  Martin G. Everett,et al.  Models of core/periphery structures , 2000, Soc. Networks.

[33]  L. Takac DATA ANALYSIS IN PUBLIC SOCIAL NETWORKS , 2012 .

[34]  Munmun De Choudhury,et al.  Social Synchrony: Predicting Mimicry of User Actions in Online Social Media , 2009, 2009 International Conference on Computational Science and Engineering.

[35]  Deok-Sun Lee,et al.  Scaling of nestedness in complex networks , 2011, ArXiv.

[36]  Thomas Gottron,et al.  Online dating recommender systems: the split-complex number approach , 2012, RSWeb@RecSys.

[37]  V. Galasso,et al.  Working For the Few: Political Capture and Economic Inequality , 2014 .

[38]  An-Ping Zeng,et al.  Centrality, Network Capacity, and Modularity as Parameters to Analyze the Core-Periphery Structure in Metabolic Networks , 2008, Proceedings of the IEEE.

[39]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[40]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[41]  Rami Puzis,et al.  Computationally efficient link prediction in a variety of social networks , 2013, ACM Trans. Intell. Syst. Technol..

[42]  Vicenç Gómez,et al.  Statistical analysis of the social network and discussion threads in slashdot , 2008, WWW.

[43]  Krishna P. Gummadi,et al.  Growth of the flickr social network , 2008, WOSN '08.

[44]  Jérôme Kunegis,et al.  KONECT: the Koblenz network collection , 2013, WWW.

[45]  Christian Bauckhage,et al.  The slashdot zoo: mining a social network with negative edges , 2009, WWW.

[46]  Sang Hoon Lee,et al.  Detection of core–periphery structure in networks using spectral methods and geodesic paths , 2014, European Journal of Applied Mathematics.

[47]  G. Caldarelli,et al.  On the rich-club effect in dense and weighted networks , 2008, 0807.0793.

[48]  David Peleg,et al.  Distributed computing on core-periphery networks: Axiom-based design , 2014, J. Parallel Distributed Comput..

[49]  Fabio Della Rossa,et al.  Profiling core-periphery network structure by random walkers , 2013, Scientific Reports.

[50]  Michael Small,et al.  Rich-club connectivity dominates assortativity and transitivity of complex networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  T. Piketty Capital in the twenty-first century: a multidimensional approach to the history of capital and social classes. , 2013, The British journal of sociology.

[52]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[53]  Anthony B. Atkinson,et al.  The world top incomes database , 2012 .

[54]  Shi Zhou,et al.  The rich-club phenomenon in the Internet topology , 2003, IEEE Communications Letters.

[55]  C. Lee Giles,et al.  CiteSeer: an autonomous Web agent for automatic retrieval and identification of interesting publications , 1998, AGENTS '98.

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

[57]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[58]  LeskovecJure,et al.  Defining and evaluating network communities based on ground-truth , 2015 .

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

[60]  Rami Puzis,et al.  Link Prediction in Social Networks Using Computationally Efficient Topological Features , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[61]  Tore Opsahl,et al.  Prominence and control: the weighted rich-club effect. , 2008, Physical review letters.

[62]  Alessandro Vespignani,et al.  Detecting rich-club ordering in complex networks , 2006, physics/0602134.

[63]  M Angeles Serrano,et al.  Rich-club vs rich-multipolarization phenomena in weighted networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[65]  Peter Druschel,et al.  Online social networks: measurement, analysis, and applications to distributed information systems , 2009 .