K Mahyuddin

Bio: K Mahyuddin is an academic researcher from University of North Sumatra. The author has contributed to research in topics: Dynamic network analysis & Resource (project management). The author has an hindex of 1, co-authored 1 publications receiving 19 citations.

Behavior of the resources in the growth of social network

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.

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

Abstract: Social Network Mining (SNM) has become one of the main theme in big data agenda. As resultant network, we can extract social network from different sources of information, but the information sources was growing dynamically require a flexible approach. To determine the appropriate approach needs the data engineering in order to get the behavior associated with the data. Each social network has the resources and the information source, but the relationship between resources and information sources requires explanation. 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. To represent the analysis unit of the SNA, this paper only considers the degree of a vertex, where it be the core of all the analysis in the SNA and it be basic for defining relation between resources and SNA in SNM. There is a strong effect on the growth of the resources of social networks. In total, the behavior of resources has positive effect. Thus, different information sources behave similarly, and have relations with SNA.

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

Abstract: As a result of digital technology revolution and massive growth of smart phone usage, over 66% of world’s population (4.92 billion) is using mobile phones and over half of them are using social media. Mobile social networking (MSN) becomes one of the key communication tool, new trend, necessity and lifestyle. With information transparency, consumer value changes quickly, whilst rapid adaptation of similar offerings in the market place, it is essential for suppliers to keep pace with consumer value changes those directly affect the consumer satisfaction and loyalty. The research applies academic models and frameworks to the real life situation to develop a value proposition in the practical world by considering WeChat (1 billion active users) as the company under study and comparing it with WhatsApp (1.5 billion active users) as the leading competitor in the market. The objectives include identification of customers’ perception of a value for mobile social networking (MSN) services, followed by critical evaluation of existing value proposition of WeChat with Whatsapp to identify gap and finally to propose a new value proposition for WeChat. This paper first identifies customer perceived value (CPV) of mobile social networking (MSN) apps using Overby and Lee (2004) model followed by critical review of existing value proposition of WeChat and then comparing and contrasting with the leading player WhatsApp. Frameworks developed by Piercy (2009), Anderson J. et al. (2006) are employed for the same. Finally, the case develops new value proposition for WeChat by using Osterwalder et al 2014 and other frameworks. By the end of this research, a new consumer value proposition (CVP) proposal for WeChat is proposed for consideration in matching with the globally evaluated consumers’ value criteria.

Research Opportunities for Argumentation in Social Networks.

Abstract: Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we 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. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.

An extracted social network mining

Abstract: Future of data mining is the social network mining, especially in connection with the web pages, will incorporate the network timeline that are generated with social network extraction. This approach we use to deal with dynamic and process of evolution in networks. Although there is an increasing interest about the social network analysis, but a little of them has a significant impact to social substructures mining. Therefore this paper proposes the mining of social network based on unit analysis in social network analysis to build a network: vertex and edge. In study we used multiple regression to determine the relations of resources of networks, but we explore naturally formal relation of vertices and edges like leadership of an author, and then we explained in experiments. So there is the role of each actor in the social networks.

Extracted Social Network Mining

Abstract: In this paper we study the relationship between the resources of social networks by exploring the Web as big data based on a simple search engine. We have used set theory by utilizing the occurrence and co-occurrence for defining the singleton or doubleton spaces of event in a search engine model, and then provided them as representation of social actors and their relationship in clusters. Thus, there are behaviors of social actors and their relation based on Web.