Community Structure Based Shortest Path Finding for Social Networks

2018 ◽  
pp. 303-319
Author(s):  
Yale Chai ◽  
Chunyao Song ◽  
Peng Nie ◽  
Xiaojie Yuan ◽  
Yao Ge
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Shortest Path ◽  
Path Finding

scholarly journals An efficient shortest path approach for social networks based on community structure

2016 ◽  
Vol 1 (1) ◽  
pp. 114-123 ◽  
Author(s):  
Maoguo Gong ◽  
Guanjun Li ◽  
Zhao Wang ◽  
Lijia Ma ◽  
Dayong Tian
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Shortest Path

scholarly journals Topological measures for identifying and predicting the spread of complex contagions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas Guilbeault ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Shortest Path ◽  
Empirical Data ◽  
Path Length ◽  
Centrality Measures ◽  
Social Phenomena ◽  
Node Centrality ◽  
Network Connectedness ◽  
Topological Measures ◽  
Definition Of

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.

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.

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