Literature DB >> 19256908

Dynamic phenomena and human activity in an artificial society.

A Grabowski1, N Kruszewska, R A Kosiński.   

Abstract

We study dynamic phenomena in a large social network of nearly 3x10;{4} individuals who interact in the large virtual world of a massive multiplayer online role playing game. On the basis of a database received from the online game server, we examine the structure of the friendship network and human dynamics. To investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We show that, even though the virtual network did not develop as a growing graph of an underlying network of social acquaintances in the real world, it influences it. Furthermore we find very interesting scaling laws concerning human dynamics. Our research shows how long people are interested in a single task and how much time they devote to it. Surprisingly, exponent values in both cases are close to -1 . We calculate the activity of individuals, i.e., the relative time daily devoted to interactions with others in the artificial society. Our research shows that the distribution of activity is not uniform and is highly correlated with the degree of the node, and that such human activity has a significant influence on dynamic phenomena, e.g., epidemic spreading and rumor propagation, in complex networks. We find that spreading is accelerated (an epidemic) or decelerated (a rumor) as a result of superspreaders' various behavior.

Entities:  

Year:  2008        PMID: 19256908     DOI: 10.1103/PhysRevE.78.066110

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  2 in total

1.  Local structure can identify and quantify influential global spreaders in large scale social networks.

Authors:  Yanqing Hu; Shenggong Ji; Yuliang Jin; Ling Feng; H Eugene Stanley; Shlomo Havlin
Journal:  Proc Natl Acad Sci U S A       Date:  2018-07-03       Impact factor: 11.205

2.  CD-Based Indices for Link Prediction in Complex Network.

Authors:  Tao Wang; Hongjue Wang; Xiaoxia Wang
Journal:  PLoS One       Date:  2016-01-11       Impact factor: 3.240

  2 in total

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