Graph Embedding Augmented Skill Rating System
- URL: http://arxiv.org/abs/2304.08257v1
- Date: Mon, 17 Apr 2023 13:17:40 GMT
- Title: Graph Embedding Augmented Skill Rating System
- Authors: Jiasheng Wang
- Abstract summary: This paper presents a framework for learning player embeddings in competitive games and events.
The player embeddings are learned from a skill gap graph using a random walk-based graph embedding method.
In the latter part of this paper, Graphical Elo (GElo) is introduced as an application of player embeddings when rating player skills.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper 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 paper, 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 non-intrusive 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.
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