92,413 research outputs found
Understanding Co-evolution in Large Multi-relational Social Networks
Understanding dynamics of evolution in large social networks is an important
problem. In this paper, we characterize evolution in large multi-relational
social networks. The proliferation of online media such as Twitter, Facebook,
Orkut and MMORPGs\footnote{Massively Multi-player Online Role Playing Games}
have created social networking data at an unprecedented scale. Sony's Everquest
2 is one such example. We used game multi-relational networks to reveal the
dynamics of evolution in a multi-relational setting by macroscopic study of the
game network. Macroscopic analysis involves fragmenting the network into
smaller portions for studying the dynamics within these sub-networks, referred
to as `communities'. From an evolutionary perspective of multi-relational
network analysis, we have made the following contributions. Specifically, we
formulated and analyzed various metrics to capture evolutionary properties of
networks. We find that co-evolution rates in trust based `communities' are
approximately higher than the trade based `communities'. We also find
that the trust and trade connections within the `communities' reduce as their
size increases. Finally, we study the interrelation between the dynamics of
trade and trust within `communities' and find interesting results about the
precursor relationship between the trade and the trust dynamics within the
`communities'
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing
abstract: Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.The final version of this article, as published in Physical Review X, can be viewed online at: https://journals.aps.org/prx/abstract/10.1103/PhysRevX.1.02102
Emergence of communities and diversity in social networks
Communities are common in complex networks and play a significant role in the functioning of social, biological, economic,
and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the
effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social
networks is still lacking. Addressing this fundamental problem
is of paramount importance in understanding, predicting, and
controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here,
we answer this question using the ultimatum game, which has
been a paradigm for characterizing altruism and fairness. We
experimentally show that stable local communities with different
internal agreements emerge spontaneously and induce social
diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social
norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community
leaders. This result indicates that networks are significant in the
emergence and stabilization of communities and social diversity.
Our experimental results also provide valuable information about
strategies for developing network models and theories of evolutionary games and social dynamics.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio
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