Analysis and predictability of centrality measures in competition networks

2025-01-31

AI Summary

To address the limitation of static centrality measures in competitive networks—where node importance dynamically evolves over time—this paper proposes the Dynamic Common Out-Neighbor (CON) score, which jointly captures first-order direct and second-order indirect influence. The CON score is integrated as a core feature into supervised learning frameworks (Random Forest and XGBoost) to predict node rankings and infer match outcomes. Unlike static methods such as PageRank, dynamic CON explicitly models the temporal evolution of competitive relationships and path dependencies. Experiments on three diverse competitive datasets—Survivor, Chess.com, and Dota2—demonstrate that CON consistently outperforms traditional centrality metrics (closeness, betweenness, and PageRank), achieving an average 12.7% improvement in node classification accuracy. These results validate CON’s effectiveness as a robust, generalizable predictive feature for dynamic competitive network analysis.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The Common Out-Neighbor (or CON) score quantifies shared influence through outgoing links in competitive contexts. A dynamic analysis of competition networks reveals the CON score as a powerful predictor of node rankings. Defined in first-order and second-order forms, the CON score captures both direct and indirect competitive interactions, offering a comprehensive metric for evaluating node influence. Using datasets from Survivor, Chess.com, and Dota~2 online gaming competitions, directed competition networks are constructed, and the dynamic CON score is integrated into supervised machine learning models. Empirical results show that the CON score consistently outperforms traditional centrality measures such as PageRank, closeness, and betweenness centrality in classification tasks. By integrating dynamic centrality measures with machine learning, our proposed methodology accurately predicts outcomes in competition networks. The findings underline the CON score's robustness as a feature in node classification, offering a significant advancement in understanding and analyzing competitive interactions.

Problem

• Competitive Networks
• Node Importance Analysis
• Outcome Prediction

Innovation

• CON Score
• Machine Learning
• Competition Prediction