Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks
Several significant studies in the existing literature have relied on network models to gain insights into various collective behavior phenomena. Nevertheless, a facet that has been critically overlooked is the presence of numerous irrelevant edges that may obscure a more meaningful underlying topology, representing the targeted phenomenon. In fact, the literature provides ample evidence that overlooking these noisy edges may result in inaccurate and misleading interpretations. Nonetheless, employing these solutions presents various challenges, prominently the absence of foundational formalization regarding the appropriate application and expected outcomes. In this context, our focus centers on extracting salient edges, exploring backbone extraction methods, for the purpose of modeling and analyzing collective behavior. To address the gaps in the current literature regarding the use of such methods for modeling collective behavior, we undertake a comprehensive series of eff orts. These include formalizing, analyzing, discussing, applying, and validating existing methods, many of which are drawn from parallel fields of study to computer science, and finally introducing novel methods to advance the state-of-the-art. We also demonstrate the effectiveness of these methods as fundamental tools for uncovering relevant patterns, applying them across diverse phenomena each with distinct requirements. Our contributions are multifaceted, including innovative methods, case studies yielding specific insights, and a comprehensive methodology for the selection, application, and validation of these methods. Moreover, our outcomes wielded a substantial impact on both the scientific community and society. They not only unveiled numerous opportunities for fellow researchers but also catalyzed the initiation of new and impactful research.
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