The Accuracy of Power Law Based Similarity Model in Phonebook-Centric Social Networks

2010 ◽  
Author(s):  
Peter Ekler ◽  
Tamas Lukovszki
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Similarity Model

scholarly journals Models And Mining Of On-Line Social Networks

2021 ◽  
Author(s):  
Yanhua Tian
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Degree Distribution ◽  
Small World ◽  
Spectral Expansion ◽  
On Line ◽  
Simulation Results ◽  
Small World Property ◽  
P Type ◽  
Law Degree

Power law degree distribution, the small world property, and bad spectral expansion are three of the most important properties of On-line Social Networks (OSNs). We sampled YouTube and Wikipedia to investigate OSNs. Our simulation and computational results support the conclusion that OSNs follow a power law degree distribution, have the small world property, and bad spectral expansion. We calculated the diameters and spectral gaps of OSNs samples, and compared these to graphs generated by the GEO-P model. Our simulation results support the Logarithmic Dimension Hypothesis, which conjectures that the dimension of OSNs is m = [log N]. We introduced six GEO-P type models. We ran simulations of these GEO-P-type models, and compared the simulated graphs with real OSN data. Our simulation results suggest that, except for the GEO-P (GnpDeg) model, all our models generate graphs with power law degree distributions, the small world property, and bad spectral expansion.

scholarly journals Character networks and book genre classification

2019 ◽  
Vol 30 (08) ◽  
pp. 1950058
Author(s):  
Adriano J. Holanda ◽  
Mariane Matias ◽  
Sueli M. S. P. Ferreira ◽  
Gisele M. L. Benevides ◽  
Osame Kinouchi
Keyword(s):  
Social Networks ◽  
Complex Network ◽  
Power Law ◽  
Degree Distribution ◽  
Previous Literature ◽  
Literary Genre ◽  
Social Character ◽  
Network Measures ◽  
Genre Classification ◽  
The Social

We compared the social character networks of biographical, legendary and fictional texts in search for marks of genre differentiation. We examined the degree distribution of character appearance and found a power-law-like distribution that does not depend on the literary genre. We also analyzed local and global complex network measures, in particular, correlation plots between the recently introduced Lobby index and degree, betweenness and closeness centralities. Assortativity plots, which previous literature claims to separate fictional from real social networks, were also studied. We found no relevant differences among genres for the books studied when applying these network measures and we provide an explanation why the previous assortativity result is not correct.

scholarly journals How Clustering Affects Epidemics in Random Networks

2014 ◽  
Vol 46 (04) ◽  
pp. 985-1008 ◽  
Author(s):  
Emilie Coupechoux ◽  
Marc Lelarge
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Degree Distribution ◽  
Growth Process ◽  
Threshold Model ◽  
Clustering Coefficient ◽  
Random Networks ◽  
New Ideas ◽  
The Impact ◽  
Analysis Of Social Networks

Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth process on these graphs that model the spread of new ideas, technologies, viruses, or worms: the diffusion model and the symmetric threshold model. For both models, we characterize conditions under which global cascades are possible and compute their size explicitly, as a function of the degree distribution and the clustering coefficient. Our results are applied to regular or power-law graphs with exponential cutoff and shed new light on the impact of clustering.

scholarly journals How Clustering Affects Epidemics in Random Networks

2014 ◽  
Vol 46 (4) ◽  
pp. 985-1008 ◽  
Author(s):  
Emilie Coupechoux ◽  
Marc Lelarge
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Degree Distribution ◽  
Growth Process ◽  
Threshold Model ◽  
Clustering Coefficient ◽  
Random Networks ◽  
New Ideas ◽  
The Impact ◽  
Analysis Of Social Networks

Motivated by the analysis of social networks, we study a model of random networks that has both a given degree distribution and a tunable clustering coefficient. We consider two types of growth process on these graphs that model the spread of new ideas, technologies, viruses, or worms: the diffusion model and the symmetric threshold model. For both models, we characterize conditions under which global cascades are possible and compute their size explicitly, as a function of the degree distribution and the clustering coefficient. Our results are applied to regular or power-law graphs with exponential cutoff and shed new light on the impact of clustering.

scholarly journals Erratum: Corrigendum: Origins of power-law degree distribution in the heterogeneity of human activity in social networks

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Lev Muchnik ◽  
Sen Pei ◽  
Lucas C. Parra ◽  
Saulo D. S. Reis ◽  
José S. Andrade Jr ◽  
...  
Keyword(s):  
Social Networks ◽  
Human Activity ◽  
Power Law ◽  
Degree Distribution ◽  
Law Degree

scholarly journals Origins of power-law degree distribution in the heterogeneity of human activity in social networks

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Lev Muchnik ◽  
Sen Pei ◽  
Lucas C. Parra ◽  
Saulo D. S. Reis ◽  
José S. Andrade Jr ◽  
...  
Keyword(s):  
Social Networks ◽  
Human Activity ◽  
Power Law ◽  
Degree Distribution ◽  
Law Degree

scholarly journals The scaling of social interactions across animal species

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis E. C. Rocha ◽  
Jan Ryckebusch ◽  
Koen Schoors ◽  
Matthew Smith
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Social Relations ◽  
Building Blocks ◽  
Social Function ◽  
Physical Contact ◽  
Social Structures ◽  
Spatial Proximity ◽  
Social Contacts ◽  
The Social

AbstractSocial animals self-organise to create groups to increase protection against predators and productivity. One-to-one interactions are the building blocks of these emergent social structures and may correspond to friendship, grooming, communication, among other social relations. These structures should be robust to failures and provide efficient communication to compensate the costs of forming and maintaining the social contacts but the specific purpose of each social interaction regulates the evolution of the respective social networks. We collate 611 animal social networks and show that the number of social contacts E scales with group size N as a super-linear power-law $$E=CN^\beta$$ E = C N β for various species of animals, including humans, other mammals and non-mammals. We identify that the power-law exponent $$\beta$$ β varies according to the social function of the interactions as $$\beta = 1+a/4$$ β = 1 + a / 4 , with $$a \approx {1,2,3,4}$$ a ≈ 1 , 2 , 3 , 4 . By fitting a multi-layer model to our data, we observe that the cost to cross social groups also varies according to social function. Relatively low costs are observed for physical contact, grooming and group membership which lead to small groups with high and constant social clustering. Offline friendship has similar patterns while online friendship shows weak social structures. The intermediate case of spatial proximity (with $$\beta =1.5$$ β = 1.5 and clustering dependency on network size quantitatively similar to friendship) suggests that proximity interactions may be as relevant for the spread of infectious diseases as for social processes like friendship.

scholarly journals Dimensionality of Social Networks Using Motifs and Eigenvalues

2021 ◽  
Author(s):  
Anthony Bonato ◽  
David F. Gleich ◽  
Myunghwan Kim ◽  
Dieter Mitsche ◽  
Paweł Prałat ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model ◽  
Power Law ◽  
Metric Space ◽  
Real World ◽  
Eigenvalue Distribution

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

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