1,445,898 research outputs found
Influence networks
Some behaviors, ideas or technologies spread and become persistent in society, whereas others vanish. This paper analyzes the role of social influence in determining such distinct collective outcomes. Agents are assumed to acquire information from others through a certain sampling process that generates an influence network, and they use simple rules to decide whether to adopt or not depending on the observed sample. We characterize, as a function of the primitives of the model, the diffusion threshold (i.e., the spreading rate above which the adoption of the new behavior becomes persistent in the population) and the endemic state (i.e., the fraction of adopters in the stationary state of the dynamics). We find that the new behavior will easily spread in the population if there is a high correlation between how influential (visible) and how easily influenced an agent is, which is determined by the sampling process and the adoption rule. We also analyze how the density and variance of the out-degree distribution affect the diffusion threshold and the endemic state.social influence, networks, diffusion threshold, endemic state
Influence Networks
Some behaviors, ideas or technologies spread and become persistent in society, whereas others vanish. This paper analyzes the role of social influence in determining such distinct collective outcomes. Agents are assumed to acquire information from others through a certain sampling process that generates an influence network and use simple rules to decide whether to adopt or not depending on the observed sample. The diffusion threshold (i.e., the spreading rate above which the behavior becomes persistent in the population) and the endemic state (i.e., the fraction of adopters in the stationary state of the dynamics) are characterized as a function of the primitives of the model. The results highlight the importance of the correlation between visibility and connectivity (or degree) for diffusion purposes.social influence, networks, diffusion threshold, endemic state.
Analysis of relative influence of nodes in directed networks
Many complex networks are described by directed links; in such networks, a
link represents, for example, the control of one node over the other node or
unidirectional information flows. Some centrality measures are used to
determine the relative importance of nodes specifically in directed networks.
We analyze such a centrality measure called the influence. The influence
represents the importance of nodes in various dynamics such as synchronization,
evolutionary dynamics, random walk, and social dynamics. We analytically
calculate the influence in various networks, including directed multipartite
networks and a directed version of the Watts-Strogatz small-world network. The
global properties of networks such as hierarchy and position of shortcuts,
rather than local properties of the nodes, such as the degree, are shown to be
the chief determinants of the influence of nodes in many cases. The developed
method is also applicable to the calculation of the PageRank. We also
numerically show that in a coupled oscillator system, the threshold for
entrainment by a pacemaker is low when the pacemaker is placed on influential
nodes. For a type of random network, the analytically derived threshold is
approximately equal to the inverse of the influence. We numerically show that
this relationship also holds true in a random scale-free network and a neural
network.Comment: 9 figure
Message passing optimization of Harmonic Influence Centrality
This paper proposes a new measure of node centrality in social networks, the
Harmonic Influence Centrality, which emerges naturally in the study of social
influence over networks. Using an intuitive analogy between social and
electrical networks, we introduce a distributed message passing algorithm to
compute the Harmonic Influence Centrality of each node. Although its design is
based on theoretical results which assume the network to have no cycle, the
algorithm can also be successfully applied on general graphs.Comment: 11 pages; 10 figures; to appear as a journal publicatio
Networks for change: How networks influence organizational change
This paper contributes to the literature on organizational change by examining organizations as social entities embedded in inter-organizational networks. In contrast to extant research that focuses on macro environment and internal factors to explain organizational change we put forth the social network surrounding the firm as a major driver of any change process. In specific we examine organization change as driven by the organizations? positions and relations in an interorganizational network. Our conceptual framework demonstrates that inter-organizational networks are important mid-level environmental factors that complement the macro-environment and internal organizational factors for the study of organizational changes. We conclude with a discussion on normative implications for organizations and avenues for future research.organizational change, social networks
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