Finding Pattern in Dynamic Network Analysis

Abstract

Internet and social media changes the way human act and make social interaction daily. The accumulated of human social interaction form large scale unstructured data that possibly store timely knowledge. Social Network Analysis (SNA) methodology can be used to perform knowledge extraction from those unstructured data. SNA also provide the way to model user interaction pattern in social media. The majority research regarding user interaction pattern is in the form of static model, but in real-world, the interaction dynamically evolves. Hence, we use Dynamic Network Analysis (DNA) to study network dynamic structure during the observation time. In this research, we present analysis of user interactions evolution on social media, specifically in Twitter. As case study, Indonesia e-commerce and the telecommunication businesses are used for the reason of both are having high dynamic interactions market. User interactions is modeled as networks that are annotated with the time markers. Our finding is there are difference network properties during weekday and weekend, thus provide promotion pattern opportunity. The result allows us to understand the network properties phenomenon over the time, that leads to actionable effort such as when the exact time to do product promotion for business organization.