Social Network Discovery from Multiple Log Data through a Behavior Model

2012 ◽  
Author(s):  
Tomonobu Ozaki ◽  
Minoru Etoh
Keyword(s):  
Social Network ◽  
Behavior Model ◽  
Log Data ◽  
Network Discovery

scholarly journals Analyzing the Intention of Consumer Purchasing Behaviors in Relation to Internet Memes Using VAB Model

2019 ◽  
Vol 11 (20) ◽  
pp. 5549
Author(s):  
Hsin-Hui Lee ◽  
Chia-Hsing Liang ◽  
Shu-Yi Liao ◽  
Han-Shen Chen
Keyword(s):  
Social Network ◽  
Purchase Intention ◽  
Structural Equation ◽  
Equation Modeling ◽  
Behavior Model ◽  
Hedonic Value ◽  
Internet Users ◽  
Marketing Activities ◽  
Convenience Sampling ◽  
The Impact

The proliferation of Internet has accelerated the dissemination of information, which has given birth to the term “Internet meme”. Social network is one of the pivotal media in spreading an Internet meme. Marketers utilize Internet memes to carry out marketing activities to significantly improve their Internet exposure. We thus verify whether consumers generate purchase intention after being attracted to an Internet meme, as no such research prevails. We employ the value–attitude–behavior model as its theoretical core and discuss how the values formed by consumers under the impact of an Internet meme influence their purchasing behaviors through their attitudes. The participants of the study are Internet users who are habitual to checking Facebook. We adopted convenience sampling and developed 380 valid questionnaires. Structural equation modeling is applied to verify the study’s hypotheses. The research results reveal that utilitarian and hedonic values influence the Purchase Intention through utilitarian and hedonic attitudes. In light of the aforementioned findings, it is suggested that marketers and relevant participants focus on the hedonic value brought by an Internet meme and design fun and witty Internet memes to attract consumers.

Exploring Virtual Communities with the Internet Community Text Analyzer (ICTA)

2011 ◽  
pp. 205-223 ◽  
Author(s):  
Anatoliy Gruzd
Keyword(s):  
Social Network ◽  
Virtual Communities ◽  
Virtual Community ◽  
Automated Analysis ◽  
Network Data ◽  
Web Based ◽  
Social Network Data ◽  
Network Discovery ◽  
The Social ◽  
Web Based System

The chapter presents a new web-based system called ICTA (http://netlytic.org) for automated analysis and visualization of online conversations in virtual communities. ICTA is designed to help researchers and other interested parties derive wisdom from large datasets. The system does this by offering a set of text mining techniques coupled with useful visualizations. The first part of the chapter describes ICTA’s infrastructure and user interface. The second part discusses two social network discovery procedures used by ICTA with a particular focus on a novel content-based method called name networks. The main advantage of this method is that it can be used to transform even unstructured Internet data into social network data. With the social network data available it is much easier to analyze, and make judgments about, social connections in a virtual community.

Give Attention to Overlapping Network Detection in Networks for Multimedia

2019 ◽  
Vol 16 (8) ◽  
pp. 3173-3177
Author(s):  
Mercy Paul Selvan ◽  
Akansha Gupta ◽  
Anisha Mukherjee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Recommender Systems ◽  
Small Groups ◽  
Network Nodes ◽  
Network Discovery ◽  
Multimedia Social Networks ◽  
Overlapping Community

Finding overlapping agencies from multimedia social networks is an thrilling and important trouble in records mining and recommender systems but, existing overlapping network discovery often generates overlapping community structures with superfluous small groups. Network detection in a multimedia and social network is a conducive difficulty in the network gadget and it helps to understand and learn the overall network shape in element. Those are essentially the dividing wall of network nodes into a few subgroups in which nodes within these subgroups are densely linked, but the connections are sparser in between the subgroups. Social network analysis is widely widespread domain which draws the attention of many information mining experts. Some wide variety of actual community common characteristics which it shares are facebook, Twitter show off the idea of network shape inside the community. Social network is represented as a community graph. Detecting the groups entails locating the densely linked nodes.

scholarly journals Social Network Discovery by Mining Spatio-Temporal Events

2005 ◽  
Vol 11 (2) ◽  
pp. 97-118 ◽  
Author(s):  
Hady W. Lauw ◽  
Ee-Peng Lim ◽  
HweeHwa Pang ◽  
Teck-Tim Tan
Keyword(s):  
Social Network ◽  
Network Discovery ◽  
Spatio Temporal ◽  
Temporal Events

A methodology for the semantic and structural restructuring of BPMN models

2017 ◽  
Vol 23 (1) ◽  
pp. 16-46 ◽  
Author(s):  
Wiem Khlif ◽  
Hanêne Ben-Abdallah ◽  
Nourchène Elleuch Ben Ayed
Keyword(s):  
Social Network ◽  
Business Process ◽  
Structural Information ◽  
Content Type ◽  
Business Context ◽  
Network Discovery ◽  
The Social ◽  
Business Process Modeling Notation ◽  
Bp Model ◽  
Structural Aspects

Purpose Restructuring a business process (BP) model may enhance the BP performance and improve its understandability. So-far proposed restructuring methods use either refactoring which focuses on structural aspects, social network discovery which uses semantic information to guide the affiliation process during its analysis, or social network rediscovery which uses structural information to identify clusters of actors according to their relationships. The purpose of this paper is to propose a hybrid method that exploits both the semantic and structural aspects of a BP model. Design/methodology/approach The proposed method first generates a social network from the BP model. Second, it applies hierarchical clustering to determine the performers’ partitions; this step uses the social context which specifies features related to performers, and two new distances that account for semantic and structural information. Finally, it applies a set of behavioral and organizational restructuring rules adapted from the graph optimization domain; each rule uses the identified performers’ partitions and the business context to reduce particular quality metrics. Findings The efficiency of the proposed method is illustrated through well-established complexity metrics. The illustration is made through the development of a tool that fully supports the proposed method and proposes a strategy for the application of the restructuring rules. Originality/value The proposed method has the merit of combining the semantic and structural aspects of a Business Process Modeling Notation model to identify restructuring operations whose ordered application reduces the complexity of the initial model.

Research on social network discovery algorithm in pervasive sensing environment

2016 ◽  
Vol 28 (15) ◽  
pp. 4093-4106 ◽  
Author(s):  
Kun Gao ◽  
Yiwei Zhu ◽  
Songjie Gong ◽  
Hengsong Tan ◽  
Guangyu Zhou
Keyword(s):  
Social Network ◽  
Network Discovery ◽  
Pervasive Sensing
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