A Framework: Workflow-Based Social Network Discovery and Analysis

2010 ◽  
Author(s):  
Jihye Song ◽  
Minjoon Kim ◽  
Haksung Kim ◽  
Kwanghoon Kim
Keyword(s):  
Social Network ◽  
Network Discovery

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

scholarly journals An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs

2019 ◽  
Vol 9 (11) ◽  
pp. 2368 ◽  
Author(s):  
Hyun Ahn ◽  
Dinh-Lam Pham ◽  
Kwanghoon Pio Kim
Keyword(s):  
Social Network ◽  
Human Resource ◽  
Real Life ◽  
Resource Planning ◽  
Life Event ◽  
Human Resource Planning ◽  
Network Discovery ◽  
Event Logs ◽  
Organizational Aspect ◽  
Enterprise Social Network

Work transference network is a type of enterprise social network centered on the interactions among performers participating in the workflow processes. It is thought that the work transference networks hidden in workflow enactment histories are able to denote not only the structure of the enterprise social network among performers but also imply the degrees of relevancy and intensity between them. The purpose of this paper is to devise a framework that can discover and analyze work transference networks from workflow enactment event logs. The framework includes a series of conceptual definitions to formally describe the overall procedure of the network discovery. To support this conceptual framework, we implement a system that provides functionalities for the discovery, analysis and visualization steps. As a sanity check for the framework, we carry out a mining experiment on a dataset of real-life event logs by using the implemented system. The experiment results show that the framework is valid in discovering transference networks correctly and providing primitive knowledge pertaining to the discovered networks. Finally, we expect that the analytics of the work transference network facilitates assessing the workflow fidelity in human resource planning and its observed performance, and eventually enhances the workflow process from the organizational aspect.

Sign in / Sign up

Export Citation Format

Share Document