Resource and Remembering Influences on Acquaintance Networks

To better reflect actual human interactions in social network models, the authors take a bottom-up, agent-based modeling and network-oriented simulation approach to analyzing acquaintance network evolution based on local interaction rules. Resources and remembering are considered in addition to common friends, meeting by chance, and leaving and arriving. Based on these factors, friendships that have been established and built up can be strengthened, weakened, or broken up. Using computer simulations, Results from a series of experiments indicate that (a) network topology statistics, especially average degree of nodes, are irrelevant to parametric distributions because they rely on average values for initial parameters; and (b) resource, remembering, and initial friendship all raise the average number of friends and lower both degree of clustering and separation. These findings indicate a strong need for a bottom-up, agent-based modeling and network-oriented simulation approach to social network research, one that stresses interactive rules and experimental simulations.

[1]  Xiang Li,et al.  A local-world evolving network model , 2003 .

[2]  B. Bollobás.  The evolution of random graphs , 1984 .

[3]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[4]  Downloaded from , 1997 .

[5]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[6]  R. Leenders,et al.  Evolution of friendship and best friendship choices , 1996 .

[7]  Harold J. Larson.  Introduction to Probability , 1994 .

[8]  Duncan J. Watts,et al.  Networks, Dynamics, and the Smafl-World , 1999 .

[9]  M. Newman,et al.  Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.

[10]  A. Barabasi,et al.  Bose-Einstein condensation in complex networks. , 2000, Physical review letters.

[11]  Manfred Kochen,et al.  Small World , 2002 .

[12]  Mark R. Muldoon,et al.  The Small World of Corporate Boards , 2006 .

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

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

[15]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Eugene N. Bruce,et al.  Biomedical Signal Processing and Signal Modeling , 2000 .

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

[18]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[19]  Stefan Bornholdt,et al.  Emergence of a small world from local interactions: modeling acquaintance networks. , 2002, Physical review letters.

[20]  M E J Newman,et al.  Identity and Search in Social Networks , 2002, Science.

[21]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[22]  D. Watts,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2001 .

[23]  D. Zanette.  Dynamics of rumor propagation on small-world networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.