Whales, Dolphins, or Minnows? Towards the Player Clustering in Free Online Games Based on Purchasing Behavior via Data Mining Technique

Yang, Wanshan; Yang, Gemeng; Huang, Ting; Chen, Lijun; Liu, Youjian

doi:10.1109/bigdata.2018.8622067

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…Bernardi [27] propose a bot detection algorithm by analyzing the behaviors of players. Yang et al [28] cluster game players by analyzing their purchase records. Thompson et al [29] analyze chat messages of players in StarCraft 2 using a lexicon-based approach.…”

Section: Related Workmentioning

confidence: 99%

Finding Key Structures in MMORPG Graph with Hierarchical Graph Summarization

Jang¹,

Park²,

Jang³

et al. 2020

Preprint

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What are the key structures existing in a large real-world MMORPG (Massively Multiplayer Online Role-Playing Game) graph? How can we compactly summarize an MMORPG graph with hierarchical node labels, considering consistent substructures at different levels of hierarchy? Recent MMORPGs generate complex interactions between entities inducing a heterogeneous graph where each entity has hierarchical labels. Succinctly summarizing a heterogeneous MMORPG graph is crucial to better understand its structure; however it is a challenging task since it needs to handle complex interactions and hierarchical labels efficiently. Although there exist few methods to summarize a large-scale graph, they do not deal with heterogeneous graphs with hierarchical node labels.We propose GSHL, a novel method that summarizes a heterogeneous graph with hierarchical labels. We formulate the encoding cost of hierarchical labels using MDL (Minimum Description Length). GSHL exploits the formulation to identify and segment subgraphs, and discovers compact and consistent structures in the graph. Experiments on a large real-world MMORPG graph with multi-million edges show that GSHL is a useful and scalable tool for summarizing the graph, finding important and interesting structures in the graph, and finding similar users.

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Section: Related Workmentioning

confidence: 99%