Statistical Properties of Social Networks

2011 ◽  
pp. 17-42 ◽  
Author(s):  
Mary McGlohon ◽  
Leman Akoglu ◽  
Christos Faloutsos
Keyword(s):  
Social Networks ◽  
Statistical Properties

Statistical Properties of Social Group Evolution

2011 ◽  
pp. 38-56
Author(s):  
Gergely Palla ◽  
Tamás Vicsek
Keyword(s):  
Social Networks ◽  
Time Evolution ◽  
Social Group ◽  
Social Groups ◽  
Statistical Properties ◽  
Statistical Features ◽  
New Members ◽  
Constant Change ◽  
Group Evolution ◽  
General Statistical

The authors’ focus is on the general statistical features of the time evolution of communities (also called as modules, clusters or cohesive groups) in large social networks. These structural sub-units can correspond to highly connected circles of friends, families, or professional cliques, which are subject to constant change due to the intense fluctuations in the activity and communication patterns of people. The communities can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. According to our results, the time evolution of social groups containing only a few members and larger communities, e.g., institutions show significant differences.

Statistical evaluation of spectral methods for anomaly detection in static networks

2019 ◽  
Vol 7 (3) ◽  
pp. 319-352 ◽  
Author(s):  
Tomilayo Komolafe ◽  
A. Valeria Quevedo ◽  
Srijan Sengupta ◽  
William H. Woodall
Keyword(s):  
Social Networks ◽  
Performance Evaluation ◽  
Anomaly Detection ◽  
Spectral Methods ◽  
Ribonucleic Acid ◽  
Statistical Evaluation ◽  
Statistical Properties ◽  
Counter Terrorism ◽  
Wide Range ◽  
Algorithmic Techniques

AbstractThe topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

COMMUNITY DYNAMICS IN SOCIAL NETWORKS

2007 ◽  
Vol 07 (03) ◽  
pp. L273-L287 ◽  
Author(s):  
GERGELY PALLA ◽  
ALBERT-LÁSZLÓ BARABÁSI ◽  
TAMÁS VICSEK
Keyword(s):  
Social Networks ◽  
Social Group ◽  
Community Dynamics ◽  
Social Groups ◽  
Statistical Properties ◽  
Modular Structure ◽  
New Members ◽  
Significant Difference

We study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure. Such cohesive groups of people can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We find significant difference between the behavior of smaller collaborative or friendship circles and larger communities, eg. institutions. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.

Statistical Properties of Community Dynamics in Large Social Networks

2009 ◽  
Vol 1 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Gergely Palla ◽  
Tamás Vicsek
Keyword(s):  
Social Networks ◽  
Time Evolution ◽  
Social Group ◽  
Community Dynamics ◽  
Social Groups ◽  
Statistical Properties ◽  
Statistical Features ◽  
New Members ◽  
Constant Change ◽  
General Statistical

The authors’ focus is on the general statistical features of the time evolution of communities (also called as modules, clusters or cohesive groups) in large social networks. These structural sub-units can correspond to highly connected circles of friends, families, or professional cliques, which are subject to constant change due to the intense fluctuations in the activity and communication patterns of people. The communities can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. According to our results, the time evolution of social groups containing only a few members and larger communities, e.g., institutions show significant differences.

scholarly journals Estimating uncertainty in respondent-driven sampling using a tree bootstrap method

2016 ◽  
Vol 113 (51) ◽  
pp. 14668-14673 ◽  
Author(s):  
Aaron J. Baraff ◽  
Tyler H. McCormick ◽  
Adrian E. Raftery
Keyword(s):  
Social Networks ◽  
Confidence Intervals ◽  
Drug Users ◽  
Bootstrap Method ◽  
High Variability ◽  
Statistical Properties ◽  
Respondent Driven Sampling ◽  
Sampling Variability ◽  
Design Effects ◽  
High Level

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.

From PageRank to Social Rank

2011 ◽  
pp. 134-154
Author(s):  
Sebastian Marius Kirsch ◽  
Melanie Gnasa ◽  
Markus Won ◽  
Armin Cremers
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Social Information ◽  
Social Rank ◽  
Statistical Properties ◽  
Retrieval Algorithm ◽  
Information Spaces ◽  
The Social

Social information spaces are characterized by the presence of a social network between participants. This chapter presents methods for utilizing social networks for information retrieval, by applying graph authority measures to the social network. We show how to integrate authority measures in an information retrieval algorithm. In order to determine the suitability of the described algorithms, we examine the structure and statistical properties of social networks, and present examples of social networks as well as evaluation results.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks
Sign in / Sign up

Export Citation Format

Share Document