Visual Analytics of Dynamics in Online Game Communities

PhD Thesis Proposal Defence


Title: "Visual Analytics of Dynamics in Online Game Communities"

by

Mr. Quan LI


Abstract:

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 two types of game dynamics, i.e., team-based combat 
dynamics and individual-based ego network dynamics in online game communities. 
In particular, 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. 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.

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:			Monday, 30 July 2018

Time:                  	10:00am - 12:00noon

Venue:                  Room 3494
                         (lifts 25/26)

Committee Members:	Dr. Xiaojuan Ma (Supervisor)
 			Prof. Huamin Qu (Supervisor)
 			Dr. Pedro Sander (Chairperson)
 			Dr. Yangqiu Song


**** ALL are Welcome ****