Interactive Visual Analytics on Representation and Dynamics of Online Game Community

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering

PhD Thesis Defence

Title: "Interactive Visual Analytics on Representation and Dynamics of 
Online Game Community"


Mr. Quan LI


Online games are the integration of culture, art, and high-technology, 
which provide us with a new way of recreation and entertainment. As games 
become more complex and are reaching a broader audience, there is a 
growing interest and urgent need to analyze player behaviors and the 
impact of game design alternatives. However, due to large volumes and 
dynamic correlations of the gameplay data, as well as the high complexity 
of analytical tasks in real-world scenarios, it is still challenging for 
game analysts to conduct in-depth analysis and extract valuable 
information. Although many automatic approaches that can scale to massive 
data sizes for effective and rapid analysis are leveraged, the 
interpretation of the results can still be difficult to some extent. This 
triggers a broad use of visualization and visual analytics. By including 
human perception in the data exploration process, the flexibility, 
creativity and domain knowledge of human beings and the computational 
power of computer machines can be combined. This can further inform the 
basic organizing principles and patterns of in-game activities, such as 
understanding of game dynamics and design of novel, or augmentation of 
online games so as to support better user engagement.

In this thesis, we focus on online game community representation and two 
types of online game community dynamics, i.e., individual-based ego 
network dynamics and team-based combat dynamics. First, identifying an 
appropriate network representation for an online community can greatly 
facilitate the downstream tasks such as online community dynamics 
analysis. Then, for dynamics analysis, in particular, for the 
individual-based ego network dynamics, we propose a visual analytics 
system to explore the evolution of the egocentric player social network. 
It not only provides a suite of novel visualization techniques to analyze 
the in-game ego network dynamics and impact propagation but also 
incorporates analytical metrics measuring structural changes during 
network evolution. For the team-based combat dynamics, we propose a visual 
analytics system to help game designers discover patterns behind different 
occurrences in MOBA games. It produces a full gameplay visualization 
demonstrating detailed information of team formation, team combat, and 
team tactics. Then, to better facilitate the game occurrence analysis in 
breadth and depth, we propose a stepwise co-design process and enhance 
this visual analytics system by incorporating Machine Learning (ML) models 
to automatically recommend match segments of interest and further 
streamline the cross-match analysis.

To the best of our knowledge, the above techniques are cutting-edge 
studies of visual analytics of online game dynamics. To validate the 
efficacy of our approaches, all the proposed techniques and systems are 
deployed in a game company to analyze real-world gameplay datasets and 
evaluated by domain experts.

Date:			Tuesday, 18 December 2018

Time:			10:00am - 12:00noon

Venue:			Room 5501
 			Lifts 25/26

Chairman:		Prof. Kai Liu (LIFS)

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Prof. Xiaojuan Ma (Supervisor)
 			Prof. Wilfred Ng
 			Prof. Ting-Chuen Pong
 			Prof. Wenbo Wang (MARK)
 			Prof. Enrico Bertini (New York University)
 			Prof. Hongbo Fu (City U)

**** ALL are Welcome ****