Investigating the Properties of a Social Bookmarking and Tagging Network

2010 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Ralitsa Angelova ◽  
Marek Lipczak ◽  
Evangelos Milios ◽  
Pawel Pralat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Random Graphs ◽  
Power Law ◽  
Information Dissemination ◽  
Collaborative Tagging ◽  
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.

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 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 Contact processes on random graphs with power law degree distributions have critical value 0

2009 ◽  
Vol 37 (6) ◽  
pp. 2332-2356 ◽  
Author(s):  
Shirshendu Chatterjee ◽  
Rick Durrett
Keyword(s):  
Random Graphs ◽  
Power Law ◽  
Critical Value ◽  
Contact Processes ◽  
Law Degree

Interest-oriented versus relationship-oriented social network sites in China

First Monday ◽  
2010 ◽  
Author(s):  
Weiyu Zhang ◽  
Rong Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Structural Analysis ◽  
Collective Action ◽  
Social Ties ◽  
Social Network Sites ◽  
Design Features ◽  
The Social ◽  
Different Types ◽  
Common Interests

This paper examines interest-oriented vs. relationship-oriented social network sites in China and their different implications for collective action. By utilizing a structural analysis of the design features and a survey of members of the social networks, this paper shows that the way a social network site is designed strongly suggests the formation and maintenance of different types of social ties. The social networks formed among strangers who share common interests imply different types of collective action, compared to the social networks that aim at the replication and strengthening of off-line relationships.

scholarly journals Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution

2017 ◽  
Vol 173 (3-4) ◽  
pp. 806-844 ◽  
Author(s):  
Pim van der Hoorn ◽  
Gabor Lippner ◽  
Dmitri Krioukov
Keyword(s):  
Maximum Entropy ◽  
Random Graphs ◽  
Power Law ◽  
Degree Distribution ◽  
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

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.

scholarly journals Search for dynamical origin of social networks

2016 ◽  
Author(s):  
Michail Zak
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Power Law ◽  
Liouville Equation ◽  
Quantum Algorithms ◽  
Intelligent Agent ◽  
Second Law Of Thermodynamics ◽  
Complete Problems ◽  
Proposed Model ◽  
Physical Particle

The challenge of this work is to re-define the concept of intelligent agent as a building block of social networks by presenting it as a physical particle with additional non-Newtonian properties. The proposed model of an intelligent agent described by a system of ODE coupled with their Liouville equation has been introduced and discussed. Following the Madelung equation that belongs to this class, non-Newtonian properties such as superposition, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that the proposed model can be linked to mathematical models of livings as well as to models of AI. The model is presented in two modifications. The first one is illustrated by the discovery of a stochastic attractor approached by the social network; as an application, it was demonstrated that any statistics can be represented by an attractor of the solution to the corresponding system of ODE coupled with its Liouville equation. It was emphasized that evolution to the attractor reveals possible micro-mechanisms driving random events to the final distribution of the corresponding statistical law. Special attention is concentrated upon the power law and its dynamical interpretation: it is demonstrated that the underlying micro- dynamics supports a “violent reputation” of the power-law statistics. The second modification of the model of social network associated with a decision-making process and applied to solution of NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing the location of this item.

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