A dynamic model for social networks.

2018 ◽  
Author(s):  
Field, Richard V., ◽  
Hamilton E. Link ◽  
Jacek Skryzalin ◽  
Jeremy D Wendt
Keyword(s):  
Social Networks ◽  
Dynamic Model

A dynamic model for social networks†

1977 ◽  
Vol 5 (1) ◽  
pp. 5-20 ◽  
Author(s):  
Paul W. Holland ◽  
Samuel Leinhardt
Keyword(s):  
Social Networks ◽  
Dynamic Model

FROM ASSORTATIVE TO DISSORTATIVE NETWORKS: THE ROLE OF CAPACITY CONSTRAINTS

2010 ◽  
Vol 13 (04) ◽  
pp. 483-499 ◽  
Author(s):  
MICHAEL D. KÖNIG ◽  
CLAUDIO J. TESSONE ◽  
YVES ZENOU
Keyword(s):  
Social Networks ◽  
Dynamic Model ◽  
Network Formation ◽  
Capacity Constraints ◽  
Simple Mechanism ◽  
Shed Light

We consider a dynamic model of network formation where agents form and sever links based on the centrality of their potential partners. We show that the existence of capacity constrains in the amount of links an agent can maintain introduces a transition from dissortative to assortative networks. This effect can shed light on the distinction between technological and social networks as it gives a simple mechanism explaining how and why this transition occurs.

scholarly journals Lattices in Social Networks with Influence

2015 ◽  
Vol 17 (01) ◽  
pp. 1540004
Author(s):  
Michel Grabisch ◽  
Agnieszka Rusinowska
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Distributive Lattice ◽  
Dynamic Model ◽  
Influence Function ◽  
Lattice Theory ◽  
General Analysis ◽  
Aggregation Functions ◽  
Action Model ◽  
Boolean Lattices

We present an application of lattice theory to the framework of influence in social networks. The contribution of the paper is not to derive new results, but to synthesize our existing results on lattices and influence. We consider a two-action model of influence in a social network in which agents have to make their yes–no decision on a certain issue. Every agent is preliminarily inclined to say either "yes" or "no", but due to influence by others, the agent's decision may be different from his original inclination. We discuss the relation between two central concepts of this model: Influence function and follower function. The structure of the set of all influence functions that lead to a given follower function appears to be a distributive lattice. We also consider a dynamic model of influence based on aggregation functions and present a general analysis of convergence in the model. Possible terminal classes to which the process of influence may converge are terminal states (the consensus states and nontrivial states), cyclic terminal classes and unions of Boolean lattices.

Meeting Strangers and Friends of Friends: How Random Are Social Networks?

2007 ◽  
Vol 97 (3) ◽  
pp. 890-915 ◽  
Author(s):  
Matthew O Jackson ◽  
Brian W Rogers
Keyword(s):  
Social Networks ◽  
Dynamic Model ◽  
Stochastic Dominance ◽  
Formation Process ◽  
Network Formation ◽  
Random Network ◽  
Local Level ◽  
Low Degree ◽  
Average Utility ◽  
Link Formation

We present a dynamic model of network formation where nodes find other nodes with whom to form links in two ways: some are found uniformly at random, while others are found by searching locally through the current structure of the network (e.g., meeting friends of friends). This combination of meeting processes results in a spectrum of features exhibited by large social networks, including the presence of more high- and low-degree nodes than when links are formed independently at random, having low distances between nodes in the network, and having high clustering of links on a local level. We fit the model to data from six networks and impute the relative ratio of random to network-based meetings in link formation, which turns out to vary dramatically across applications. We show that as the random/network-based meeting ratio varies, the resulting degree distributions can be ordered in the sense of stochastic dominance, which allows us to infer how the formation process affects average utility in the network. (JEL D85, Z13)

A Dynamic Model for On-Line Social Networks

2009 ◽  
pp. 127-142 ◽  
Author(s):  
Anthony Bonato ◽  
Noor Hadi ◽  
Paul Horn ◽  
Paweł Prałat ◽  
Changping Wang
Keyword(s):  
Social Networks ◽  
Dynamic Model ◽  
On Line

scholarly journals Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ping Huang ◽  
Xiao-Long Chen ◽  
Ming Tang ◽  
Shi-Min Cai
Keyword(s):  
Social Networks ◽  
Dynamic Model ◽  
Coupled Model ◽  
Resource Depletion ◽  
Equilibrium Analysis ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Coupled Dynamics ◽  
Dynamic Social Networks ◽  
Multiplex Network

In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic (RNR-SIS) dynamic model (coupled model for short) on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading.

Short-term volume and turgor regulation in yeast

2008 ◽  
Vol 45 ◽  
pp. 147-160 ◽  
Author(s):  
Jörg Schaber ◽  
Edda Klipp
Keyword(s):  
Dynamic Model ◽  
Volume Change ◽  
Volume Regulation ◽  
Time Course ◽  
Osmotic Shock ◽  
Intracellular Concentration ◽  
Short Term ◽  
Hydrodynamic Property ◽  
Turgor Regulation ◽  
Thermodynamic Framework

Volume is a highly regulated property of cells, because it critically affects intracellular concentration. In the present chapter, we focus on the short-term volume regulation in yeast as a consequence of a shift in extracellular osmotic conditions. We review a basic thermodynamic framework to model volume and solute flows. In addition, we try to select a model for turgor, which is an important hydrodynamic property, especially in walled cells. Finally, we demonstrate the validity of the presented approach by fitting the dynamic model to a time course of volume change upon osmotic shock in yeast.

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