Abstract
Online competitive gaming has become one of the largest collective human activities globally and understanding motivations and social interaction is still not fully achieved. The aim of this study is to develop a basis for a systematic classification of player-centric networks in competitive online games based on structural network criteria. Using data extracted from League of Legends players, their matches and machine learning techniques, a classification of personal player networks in League of Legends is proposed. Results show the resulting egonets can be potentially grouped in four clusters related to their egos playing habits, ranging from solo to team players.
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