How community structure influences epidemic spread in social networks

2008 ◽  
Vol 387 (2-3) ◽  
pp. 623-630 ◽  
Author(s):  
Xiaoyan Wu ◽  
Zonghua Liu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Epidemic Spread

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

Direction recovery in undirected social networks based on community structure and popularity

2019 ◽  
Vol 473 ◽  
pp. 31-43 ◽  
Author(s):  
Yi-Ming Wen ◽  
Ling Huang ◽  
Chang-Dong Wang ◽  
Kun-Yu Lin
Keyword(s):  
Social Networks ◽  
Community Structure

scholarly journals Visualizations of personal social networks on Facebook and community structure: an exploratory study

2017 ◽  
Author(s):  
Christina Gkini ◽  
Alexios Brailas
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Single Point ◽  
Community Formation ◽  
Structure Pattern ◽  
Diffusion Of Information ◽  
Qualitative Interpretation ◽  
Network Topologies ◽  
Personal Social Networks ◽  
And Diffusion

We studied the community structure pattern in the visualizations of ten personal social networks on Facebook at a single point in time. It seems to be a strong tendency towards community formation in online personal, social networks: somebody’s friends are usually also friends between them, forming subgroups of more densely connected nodes. Research on community structure in social networks usually focuses on the networks’ statistical properties. There is a need for qualitative studies bridging the gap between network topologies and their sociological implications. To this direction, visual representations of personal networks in social media could be a valuable source of empirical data for qualitative interpretation. Most of the personal social networks’ visualizations in the present study are very highly clustered with densely-knit overlapping subgroups of friends and interconnected between them through wide bridges. This network topology pattern seems to be quite efficient, allowing for a fast spread and diffusion of information across the whole social network.

scholarly journals Community structure and the spread of infectious disease in primate social networks

2011 ◽  
Vol 26 (4) ◽  
pp. 779-800 ◽  
Author(s):  
Randi H. Griffin ◽  
Charles L. Nunn
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Community Structure

scholarly journals An Algorithm for Critical Nodes Problem in Social Networks Based on Owen Value

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xue-Guang Wang
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Marginal Contribution ◽  
Actual Situation ◽  
Owen Value ◽  
Critical Node ◽  
Critical Nodes ◽  
Synthetic Datasets ◽  
Critical Node Problem ◽  
Node Problem

Discovering critical nodes in social networks has many important applications. For finding out the critical nodes and considering the widespread community structure in social networks, we obtain each node’s marginal contribution by Owen value. And then we can give a method for the solution of the critical node problem. We validate the feasibility and effectiveness of our method on two synthetic datasets and six real datasets. At the same time, the result obtained by using our method to analyze the terrorist network is in line with the actual situation.

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