This article presents a framework for learning player embeddings in competitive games and events. Players and their win–loss relationships are modeled as a skill gap graph, which is an undirected weighted graph. The player embeddings are learned from the graph using a random walk-based graph embedding method and can reflect the relative skill levels among players. Embeddings are low-dimensional vector representations that can be conveniently applied to subsequent tasks while still preserving the topological relationships in a graph. In the latter part of this article, graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills. GElo is an extension of the classic Elo rating system. It constructs a skill gap graph based on player match histories and learns player embeddings from it. Afterward, the rating scores that were calculated by Elo are adjusted according to player activeness and cosine similarities among player embeddings. GElo can be executed offline and in parallel, and it is nonintrusive to existing rating systems. Experiments on public datasets show that GElo makes a more reliable evaluation of player skill levels than vanilla Elo. The experimental results suggest potential applications of player embeddings in competitive games and events.

中文翻译：

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图嵌入增强技能评级系统

本文提出了一个用于学习竞技游戏和活动中玩家嵌入的框架。玩家及其输赢关系被建模为技能差距图，这是一个无向加权图。玩家嵌入是使用基于随机游走的图嵌入方法从图中学习的，并且可以反映玩家之间的相对技能水平。嵌入是低维向量表示，可以方便地应用于后续任务，同时仍然保留图中的拓扑关系。在本文的后半部分，介绍了图形 Elo（GElo）作为玩家嵌入在评估玩家技能时的应用。GElo 是经典 Elo 评级系统的延伸。它根据玩家比赛历史构建技能差距图，并从中学习玩家嵌入。之后，Elo 计算的评分会根据玩家活跃度和玩家嵌入之间的余弦相似度进行调整。GElo可以离线并行执行，并且不干扰现有的评级系统。对公共数据集的实验表明，GElo 对玩家技能水平的评估比普通 Elo 更可靠。实验结果表明玩家嵌入在竞技游戏和赛事中的潜在应用。