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Proceedings ArticleDOI

Behavior of the resources in the growth of social network

TLDR
This paper is aimed to address the behavior of the resource in the growth of social networks by using the association rules and statistical calculations to explain the evolutionary mechanisms.
Abstract
Social network can be extracted from different sources of information, but the resources was growing dynamically require a flexible approach. Each social network has the resources, but the relationship between resources and information sources requires explanation. This paper is aimed to address the behavior of the resource in the growth of social networks by using the association rules and statistical calculations to explain the evolutionary mechanisms. There is a strong effect on the growth of the resources of social networks and totally behavior of resources has positive effect.

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Citations
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Journal ArticleDOI

Social Network Mining (SNM): A Definition of Relation between the Resources and SNA

TL;DR: This paper aimed to address the behavior of the resource as a part of social network analysis (SNA) in the growth of social networks by using the statistical calculations to explain the evolutionary mechanisms.
Posted Content

What WeChat can Learn from WhatsApp? Customer Value Proposition Development for Mobile Social Networking (MSN) Apps: A Case Study Approach

TL;DR: In this article, a new consumer value proposition (CVP) proposal for WeChat is proposed for consideration in matching with the globally evaluated consumers' value criteria, by considering WeChat as the company under study and comparing it with WhatsApp as the leading competitor in the market.

Research Opportunities for Argumentation in Social Networks.

TL;DR: In this article, the authors show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users.
Proceedings ArticleDOI

An extracted social network mining

TL;DR: In this paper, the authors proposed the mining of social network based on unit analysis in social network analysis to build a network: vertex and edge, and explored naturally formal relation of vertices and edges like leadership of an author, and then they explained in experiments.
Posted Content

Extracted Social Network Mining

TL;DR: In this paper, the authors study the relationship between the resources of social networks by exploring the Web as big data based on a simple search engine and provide them as representation of social actors and their relationship in clusters.
References
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Journal ArticleDOI

LOGIT MODELS AND LOGISTIC REGRESSIONS FOR SOCIAL NETWORKS: I. AN INTRODUCTION TO MARKOV GRAPHS AND p*

TL;DR: A large class of models, including several generalizations of stochastic block models, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies are described and extended.
Journal ArticleDOI

Discrete temporal models of social networks

TL;DR: This paper propose a family of statistical models for social network evolution over time, which represent an extension of Exponential Random Graph Models (ERGMs) and give examples of their use for hypothesis testing and classification.
Book

Social Networks and the Semantic Web

TL;DR: This paper presents an ontology for the representation of social networks and relationships, a hybrid system for online data acquisition that combines traditional web mining techniques with the collection of Semantic Web data, and a case study highlighting some of the possible analysis of this data using methods from Social Network Analysis.
Journal ArticleDOI

Logit models and logistic regressions for social networks: III. Valued relations

TL;DR: This paper generalizes thep* model for dichotomous social network data to the polytomous case by transforming valued social networks into three-way binary arrays and demonstrates that a suitable version of the Hammersley-Clifford theorem can be developed.
Book ChapterDOI

Discrete temporal models of social networks

TL;DR: A family of statistical models for social network evolution over time is proposed, which represents an extension of Exponential Random Graph Models (ERGMs), and examples of their use for hypothesis testing and classification are given.
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