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 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 Specialization Models of Network Growth

2018 ◽  
Vol 7 (3) ◽  
pp. 375-392 ◽  
Author(s):  
L A Bunimovich ◽  
D C Smith ◽  
B Z Webb
Keyword(s):  
Power Law ◽  
Network Topology ◽  
Real World ◽  
Degree Distribution ◽  
Small World ◽  
Hierarchical Network ◽  
Network Growth ◽  
Complex Tasks ◽  
Small World Property ◽  
Law Degree

AbstractOne of the most important features observed in real networks is that, as a network’s topology evolves so does the network’s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network’s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.

scholarly journals Multistage Random Growing Small-World Networks with Power-Law Degree Distribution

2006 ◽  
Vol 23 (3) ◽  
pp. 746-749 ◽  
Author(s):  
Liu Jian-Guo ◽  
Dang Yan-Zhong ◽  
Wang Zhong-Tuo
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Small World ◽  
Small World Networks ◽  
Law Degree

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

Investigating the Properties of a Social Bookmarking and Tagging Network

2011 ◽  
pp. 1-16
Author(s):  
Ralitsa Angelova ◽  
Marek Lipczak ◽  
Evangelos Milios ◽  
Pawel Pralat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Random Graphs ◽  
Power Law ◽  
Degree Distribution ◽  
Information Dissemination ◽  
Social Bookmarking ◽  
Primary Means ◽  
Common Interests ◽  
Law Degree

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us3 is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make del.icio.us a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly “similar” users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. The authors investigate the question whether friends have common interests, they gain additional insights on the strategies that users use to assign tags to their bookmarks, and they demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

scholarly journals Interaction network effects on position- and velocity-based models of collective motion

2020 ◽  
Vol 17 (169) ◽  
pp. 20200165
Author(s):  
Ali Emre Turgut ◽  
İhsan Caner Boz ◽  
İlkin Ege Okay ◽  
Eliseo Ferrante ◽  
Cristián Huepe
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Network Effects ◽  
Collective Motion ◽  
Interaction Network ◽  
Small World ◽  
Minimal Models ◽  
Vicsek Model ◽  
Law Degree ◽  
Interaction Topology

We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to analyse the relationship between the interaction topology and the resilience to noise of the ordered state. For the Vicsek case, we find that a higher fraction of random connections with homogeneous or power-law degree distribution increases the critical noise, and thus the resilience to noise, as expected due to small-world effects. Surprisingly, for the AE model, a higher fraction of random links with power-law degree distribution can decrease this resilience, despite most links being long-range. We explain this effect through a simple mechanical analogy, arguing that the larger presence of agents with few connections contributes localized low-energy modes that are easily excited by noise, thus hindering the collective dynamics. These results demonstrate the strong effects of the interaction topology on self-organization. Our work suggests potential roles of the interaction network structure in biological collective behaviour and could also help improve decentralized swarm robotics control and other distributed consensus systems.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

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