Homophyly/Kinship Model: Naturally Evolving Networks

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

[1]  Eli Upfal,et al.  Stochastic models for the Web graph , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[2]  A. Vázquez Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Pan Peng,et al.  The small-community phenomenon in networks† , 2011, Mathematical Structures in Computer Science.

[4]  Albert,et al.  Topology of evolving networks: local events and universality , 2000, Physical review letters.

[5]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[7]  Sidney Redner,et al.  Community structure of the physical review citation network , 2009, J. Informetrics.

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

[9]  A. Orth,et al.  Large-scale analysis of the human and mouse transcriptomes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[11]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  A. Clauset Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Angsheng Li,et al.  Homophyly/kinship hypothesis: Natural communities, and predicting in networks , 2015 .

[14]  A. Gray,et al.  I. THE ORIGIN OF SPECIES BY MEANS OF NATURAL SELECTION , 1963 .

[15]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[16]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[17]  Pan Peng,et al.  Community Structures in Classical Network Models , 2011, Internet Math..

[18]  M. Nowak,et al.  Evolutionary games and spatial chaos , 1992, Nature.

[19]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[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]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

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

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

[24]  Razvan Diaconescu Interpolation for predefined types , 2012, Math. Struct. Comput. Sci..

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

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

[27]  Angsheng Li,et al.  Discovering natural communities in networks , 2015 .

[28]  T. Poggio,et al.  Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[30]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[31]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[32]  Avrim Blum,et al.  A Random-Surfer Web-Graph Model , 2006, ANALCO.

[33]  Kevin Zhou Navigation in a small world , 2017 .

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