SOCIAL NETWORK ANALYSIS OF HUAWEI COMMUNITY PLATFORM: IDENTIFYING KEY MEMBERS AND COMMUNITY STRUCTURES THROUGH CENTRALITY MEASURES, COMMUNITY DETECTION, AND CLUSTERING ALGORITHMS

Abstract

This research contains the application of the SNA (Social Network Analysis) algorithm to find out the most influential people or communities on the social media platforms that are Facebook, Twitter and Instagram Huawei pages. Social network data is taken from the Kaggle site entitled "Huawei Social Network Data", this data will be used for social media analysis. Currently, many of the most effective ways to do marketing are through 3 or more social media platforms, especially as large companies like Huawei definitely do this because their company coverage reaches all over the world. However, marketing online cannot be done haphazardly, because it will waste time and cost a lot of money. One of the factors that determines whether marketing is effective or not is the target market. Considering the importance of this, this research aims to find a good target market. One way to find a good target market is to use SNA. This research will use SNA algorithms such as , Centrality Measures, Community Detection, Clustering and other SNA algorithms. In the data there are 1000 columns and 1000 rows which are nodes and edges. The nodes labeled here are people's names and the number 1 is the number of edges. By analyzing this research, we can find out people or communities who have high potential to buy or even subscribe to Huawei products