“Follow the Leader”: A Centrality Guided Clustering and Its Application to Social Network Analysis

Within graph theory and network analysis, centrality of a vertex measures the relative importance of a vertex within a graph. The centrality plays key role in network analysis and has been widely studied using different methods. Inspired by the idea of vertex centrality, a novel centrality guided clustering (CGC) is proposed in this paper. Different from traditional clustering methods which usually choose the initial center of a cluster randomly, the CGC clustering algorithm starts from a “LEADER”—a vertex with the highest centrality score—and a new “member” is added into the same cluster as the “LEADER” when some criterion is satisfied. The CGC algorithm also supports overlapping membership. Experiments on three benchmark social network data sets are presented and the results indicate that the proposed CGC algorithm works well in social network clustering.

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Social Network Analysis in Marketing

E-Marketing ◽

10.4018/978-1-4666-1598-4.ch012 ◽

2012 ◽

pp. 185-197

Author(s):

Przemyslaw Kazienko ◽

Piotr Doskocz ◽

Tomasz Kajdanowicz

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Relationships ◽

Network Data ◽

Social Network Data ◽

The Social ◽

Targeted Marketing ◽

Potential Customers ◽

Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

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Social Network Analysis in Marketing

Customer Relationship Management and the Social and Semantic Web - Advances in Electronic Commerce ◽

10.4018/978-1-61350-044-6.ch016 ◽

2011 ◽

pp. 271-283

Author(s):

Przemyslaw Kazienko ◽

Piotr Doskocz ◽

Tomasz Kajdanowicz

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Relationships ◽

Network Data ◽

Social Network Data ◽

The Social ◽

Targeted Marketing ◽

Potential Customers ◽

Demographic Features

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On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0571 ◽

2016 ◽

Vol 20 (4) ◽

pp. 571-579 ◽

Cited By ~ 1

Author(s):

Yasunori Endo ◽

◽

Tomoyuki Suzuki ◽

Naohiko Kinoshita ◽

Yukihiro Hamasuna ◽

...

Keyword(s):

Data Clustering ◽

Incomplete Data ◽

Clustering Algorithm ◽

Uncertain Data ◽

Data Sets ◽

Membership Degree ◽

Clustering Methods ◽

Clustering Method ◽

Numerical Examples ◽

Metric Model

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzyc-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

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Application of Computer System in the Study of Sociology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.1680 ◽

2014 ◽

Vol 926-930 ◽

pp. 1680-1683

Author(s):

Ying Ming Xu ◽

Shu Juan Jin

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Computer Algorithms ◽

Useful Knowledge ◽

Social Network Data ◽

Analysis Task ◽

Original Analysis ◽

The Social ◽

The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

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Hospital-Nursing Home Transfer Patterns and Influence on Nursing Home Clostridium difficile Infection Rates

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx163.1006 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S402-S403

Author(s):

Lauren Campbell ◽

Kristen Bush ◽

Ghinwa Dumyati

Keyword(s):

Nursing Home ◽

Network Analysis ◽

Online Survey ◽

Negative Binomial ◽

Network Clustering ◽

Emerging Infections ◽

Clustering Methods ◽

Data Set ◽

Patient Transfers ◽

Network Analyses

Abstract Background Little is known as to how hospital C. difficile infection (CDI) may impact nursing home (NH) CDI, or how patient transfers may modify this relationship. This study aims to examine a possible association between hospital and NH CDI rates, and whether NH CDI rates are influenced by patient transfers from hospital to NH. Methods Patient transfers among the 5 hospitals and 34 NHs in Monroe County, NY were identified from the Minimum Data Set (MDS) 3.0 and Medicare Provider Analysis and Review files for 2011–13, and aggregated to the NH level. NH and hospital CDI rates were obtained from Emerging Infections Program CDI population surveillance and National Healthcare Safety Network data, respectively. Multivariate negative binomial regression modeled the association between hospital CDI rate (weighted by hospital-to-NH transfers/overall transfers among hospitals and NHs) and NH CDI rate, controlling for NH covariates from NH Compare and the Online Survey, Certification, and Reporting files. Patient transfer networks between hospitals and NHs were constructed, and basic network analysis of transfer patterns was conducted to confirm contributing factors to NH CDI rates from the multivariate model. Results When weighted hospital CDI rate increased by 1%, NH CDI rate increased by 18% (P = 0.016). Antibiotic and feeding tube prevalence were associated with a 4% and 8% increase in NH CDI rate, respectively (P≤0.014). Network analysis confirmed multivariate results and detected hospital-NH pairs with high edge weights (number of transfers) where NHs receiving patients from hospitals with high CDI rates had higher CDI rates. Network clustering methods were used to identify 2 sub-networks within overall annual networks and clusters of hospital-NH pairs for targeted intervention. Conclusion Hospital CDI rate, adjusting for patient transfers, is associated with higher NH CDI rates in multivariate and network analyses, suggesting that NHs with a large inflow of patients from hospitals may need to implement stricter infection prevention practices to reduce transmission among residents. By identifying regional sub-networks, network analysis can also be used to actively manage facility CDI and prevent spread to other healthcare facilities. Disclosures All authors: No reported disclosures.

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Social Network Big Data Hierarchical High-Quality Node Mining

Wireless Communications and Mobile Computing ◽

10.1155/2021/1444755 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Dongning Jia ◽

Bo Yin ◽

Xianqing Huang

Keyword(s):

Big Data ◽

Social Network ◽

Data Analysis ◽

Shopping Mall ◽

Network Data ◽

Clustering Methods ◽

Learning Mechanisms ◽

Social Network Data ◽

Analysis Methods ◽

Network Big Data

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self-organizing self-learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e-commerce platform, and cultivating their own customer base.

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Network Basics: Points, Lines, and Positions

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.2 ◽

2020 ◽

pp. 15-33

Author(s):

Ryan Light ◽

James Moody

Keyword(s):

Social Networks ◽

Social Network ◽

Network Analysis ◽

Ethical Issues ◽

Building Blocks ◽

Levels Of Analysis ◽

Network Data ◽

Social Network Data ◽

Basic Concepts ◽

Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

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A FAST IMPLEMENTATION OF THE ISODATA CLUSTERING ALGORITHM

International Journal of Computational Geometry & Applications ◽

10.1142/s0218195907002252 ◽

2007 ◽

Vol 17 (01) ◽

pp. 71-103 ◽

Cited By ~ 93

Author(s):

NARGESS MEMARSADEGHI ◽

DAVID M. MOUNT ◽

NATHAN S. NETANYAHU ◽

JACQUELINE LE MOIGNE

Keyword(s):

Clustering Algorithm ◽

Empirical Studies ◽

Synthetic Data ◽

Large Data ◽

Large Data Sets ◽

Cluster Center ◽

Data Sets ◽

Clustering Methods ◽

Sensing Applications ◽

Remote Sensing Applications

Clustering is central to many image processing and remote sensing applications. ISODATA is one of the most popular and widely used clustering methods in geoscience applications, but it can run slowly, particularly with large data sets. We present a more efficient approach to ISODATA clustering, which achieves better running times by storing the points in a kd-tree and through a modification of the way in which the algorithm estimates the dispersion of each cluster. We also present an approximate version of the algorithm which allows the user to further improve the running time, at the expense of lower fidelity in computing the nearest cluster center to each point. We provide both theoretical and empirical justification that our modified approach produces clusterings that are very similar to those produced by the standard ISODATA approach. We also provide empirical studies on both synthetic data and remotely sensed Landsat and MODIS images that show that our approach has significantly lower running times.

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Foreign Capital and Economic Growth: A Social Network Analysis, 2001–2017

Sociological Perspectives ◽

10.1177/07311214211035150 ◽

2021 ◽

pp. 073112142110351

Author(s):

Rob Clark ◽

Jeffrey Kentor

Keyword(s):

Economic Growth ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Direct Investment ◽

Fixed Effects ◽

Foreign Capital ◽

Global Network ◽

Data Sets ◽

The Impact

Foreign direct investment (FDI) holds a substantial and rapidly growing presence across every region of the world. However, our understanding of how foreign capital impacts economic growth in receiving and investing countries remains in question, despite nearly five decades of research. Our study contributes to this long-standing debate by (1) applying social network analysis to the FDI-growth literature, (2) utilizing recently available bilateral data for a global sample of countries during the post-2000 period, and (3) examining the impact of both inward and outward foreign capital on economic growth. While conventional measures of FDI typically focus on investment volume, we argue that the network structure of investment relations may be equally—or more—important. We construct a global network of FDI during the 2001–2017 period, bringing together two data sets: (1) the United Nations Conference on Trade and Development’s Bilateral FDI Statistics, and (2) the International Monetary Fund’s Coordinated Direct Investment Survey. We then calculate network centrality scores that reflect each country’s level of inward and outward embeddedness in the global FDI network. Drawing from a sample of 1,467 observations across 137 countries during the 2001–2017 period, we estimate two-way fixed effects models to examine the effect of FDI centrality on economic growth. Net of other predictors, we find that inward and outward centrality are positively—and independently—associated with growth, while more conventional measures of foreign capital display weaker and inconsistent effects.

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A Novel Complex Networks Clustering Algorithm Based on the Core Influence of Nodes

The Scientific World JOURNAL ◽

10.1155/2014/801854 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Chao Tong ◽

Jianwei Niu ◽

Bin Dai ◽

Zhongyu Xie

Keyword(s):

Complex Networks ◽

Clustering Algorithm ◽

Cluster Formation ◽

Clustering Algorithms ◽

Cluster Structure ◽

Network Clustering ◽

Clustering Methods ◽

Positive Role ◽

The Core ◽

Final Cluster

In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster’s core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.

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