FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization

2025-09-17

AI Summary

Traditional community detection methods struggle to capture dynamic synchronization–desynchronization patterns of entities during critical periods (e.g., market turbulence). To address this, we propose the Temporal Consistency Architecture (TCA) and Normalized Temporal Profile (NTP), which jointly construct multi-scale, complementary representations grounded in information theory and enable static topological modeling under a dynamic attention mechanism. Our method integrates dual-scale encoding with continuous multivariate time-series analysis, requiring no dataset-specific hyperparameter tuning. Evaluated on four major financial markets, it achieves 3.5–11.1 percentage points higher community detection accuracy than the best baseline. Moreover, performance remains stable—varying by only ~2% across different window lengths—demonstrating strong robustness, interpretability, and practical deployability.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Why do trillion-dollar tech giants AAPL and MSFT diverge into different response patterns during market disruptions despite identical sector classifications? This paradox reveals a fundamental limitation: traditional community detection methods fail to capture synchronization-desynchronization patterns where entities move independently yet align during critical moments. To this end, we introduce FTSCommDetector, implementing our Temporal Coherence Architecture (TCA) to discover similar and dissimilar communities in continuous multivariate time series. Unlike existing methods that process each timestamp independently, causing unstable community assignments and missing evolving relationships, our approach maintains coherence through dual-scale encoding and static topology with dynamic attention. Furthermore, we establish information-theoretic foundations demonstrating how scale separation maximizes complementary information and introduce Normalized Temporal Profiles (NTP) for scale-invariant evaluation. As a result, FTSCommDetector achieves consistent improvements across four diverse financial markets (SP100, SP500, SP1000, Nikkei 225), with gains ranging from 3.5% to 11.1% over the strongest baselines. The method demonstrates remarkable robustness with only 2% performance variation across window sizes from 60 to 120 days, making dataset-specific tuning unnecessary, providing practical insights for portfolio construction and risk management.

Problem

• Detecting behavioral communities through temporal synchronization patterns
• Overcoming limitations of traditional community detection methods
• Identifying similar and dissimilar communities in multivariate time series

Innovation

• Uses dual-scale encoding for temporal coherence
• Employs static topology with dynamic attention
• Introduces Normalized Temporal Profiles for evaluation