@inproceedings{d77df0b265014ae5942545127235104e,
title = "Adopting centrality measure models in visualized financial datasets",
abstract = "The financial data is complex to analyse due to its complicated relationships and multiple attributes. Centrality measure models from the SNA (Social network analysis) can show the most critical variables in a network, and graph layouts can be produced to represent not only data networks but also the relations among data entries. To the best of our knowledge, there is no work that has been tried on the Australian stock market based on the combination of those two methods mentioned above so far. This study adopts centrality measure methods and a graph drawing algorithm (force-directed) to offer users big pictures and detailed views, comes with ranking factors based on weighted degree, pagerank and eigenvector metrics. The outcomes show that the methodology can produce clear graph layouts of the stock's social network, identify the central stocks (represent through features such as node colour and size) and the business sectors they belong to. This study may assist stakeholders with grasping deep insight from the complex financial datasets, and another angle of view to adjust future investments accordingly.",
keywords = "Centrality Measure, Social Network Analysis, Visual Analytics, Force-directed Algorithms",
author = "Jie Hua and Guohua Wang and Youquan Xu",
year = "2019",
doi = "10.1117/12.2537801",
language = "English",
isbn = "9781510634091",
series = "Proceedings of SPIE",
publisher = "SPIE",
pages = "1--6",
editor = "Ruidan Su",
booktitle = "2019 International Conference on Image and Video Processing, and Artificial Intelligence",
address = "United States",
note = "2019 2nd International Conference on Image and Video Processing, and Artificial Intelligence, IVPAI 2019 ; Conference date: 23-08-2019 Through 25-08-2019",
}