Knowledge enhanced graph contrastive learning for match outcome prediction

Abstract

With the booming popularity, Multiplayer Online Battle Arena (MOBA) Game has become one of the mainstream games, in which players are divided into two teams, and the goal is to destroy the base of the opponent. Accurate prediction of the match outcome enables the operators to improve the players’ game experience through balance matching. However, existing methods usually model the players’ combat effectiveness based on the profile data, neglecting the evolution of proficiency of different heroes and their performance against different opponents. To model the evolution of players’ abilities, we propose the Knowledge Enhanced Graph Contrastive Learning (KEGC) framework that reinforces the predictor with the information of the knowledge graph and match sequence graphs. Specifically, we construct a knowledge graph that reflects the static system information on the cooperation and confrontation of heroes by game developers. Meanwhile, the match sequence of each player is converted into a sequence graph for representing dynamic ability, which builds edges in similar matches to capture player skills evolution. Further, we propose a coupled contrastive training framework to adaptively fuse the information from the static and dynamic views. Considering the uncertainty of players’ performance in different games, the conditional variational mechanism is introduced to KEGC. Besides, we also adopt the auxiliary task, i.e., the match balance, and design the joint loss function to suppress the noise in the training data. Extensive experiments on two real-world datasets from well-known MOBA games demonstrate the superiority of KEGC compared to the state-of-the-art methods.