Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority

@article{Juul2019HipstersON,
  title={Hipsters on networks: How a minority group of individuals can lead to an antiestablishment majority},
  author={Jonas S{\o}gaard Juul and Mason A. Porter},
  journal={Physical Review. E},
  year={2019},
  volume={99}
}
  • J. S. Juul, M. Porter
  • Published 22 July 2017
  • Physics, Computer Science, Mathematics, Medicine
  • Physical Review. E
The spread of opinions, memes, diseases, and “alternative facts” in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. One feature that can change spreading dynamics substantially is heterogeneous behavior among different types of individuals in a social network. In this paper, we explore how antiestablishment nodes (e.g., hipsters) influence the spreading dynamics of two competing products. We… 
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References

SHOWING 1-10 OF 100 REFERENCES
Spreading in online social networks: the role of social reinforcement.
TLDR
A unknown-known-approved-exhausted four-status model is proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate), and can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space.
Synergistic effects in threshold models on networks
TLDR
This paper incorporates the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks, and illustrates that the model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate.
Maximizing the spread of influence through a social network
TLDR
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Structural diversity in social contagion
TLDR
This analysis of the growth of Facebook shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.
Complex Contagions with Timers
TLDR
It is illustrated that heterogeneous timers can either accelerate or decelerate the spread of adoptions compared to an analogous situation with homogeneous timers, and the relationship of such acceleration and deceleration with respect to the timer distribution and network structure is investigated.
The "Majority Illusion" in Social Networks
TLDR
A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
TLDR
A dynamic matched sample estimation framework is developed to distinguish influence and homophily effects in dynamic networks, and this framework is applied to a global instant messaging network of 27.4 million users, finding that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and thathomophily explains >50% of the perceived behavioral contagion.
Diffusion and contagion in networks with heterogeneous agents and homophily
TLDR
It is shown that homophily can facilitate diffusion from a small initial seed of adopters, and the conditions under which a behavior or disease diffuses and becomes persistent in the population are identified.
Dynamical influence processes on networks: general theory and applications to social contagion.
TLDR
A general mean-field theory for random networks is constructed and it is shown that the dynamics on the network is a smoothed version of the average response function dynamics, which can range from steady state to chaotic depending on the response functions, network connectivity, and update synchronicity.
The structure of online diffusion networks
TLDR
This work describes the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing, and finds strikingly similar patterns across all domains.
...
1
2
3
4
5
...