Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications (0711.3199v3)

Abstract: The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches and in applications to real-world problems. Complex networks have fared extremely well in both of these aspects, with their sound theoretical basis developed over the years and with a variety of applications. In this survey, we analyze the applications of complex networks to real-world problems and data, with emphasis in representation, analysis and modeling, after an introduction to the main concepts and models. A diversity of phenomena are surveyed, which may be classified into no less than 22 areas, providing a clear indication of the impact of the field of complex networks.

• The paper systematically categorizes over 22 domains to demonstrate how complex networks unveil hidden patterns in real-world phenomena.
• The paper details foundational network theory and key metrics, providing a robust framework for analyzing system dynamics.
• The paper highlights the predictive potential of network models in areas like social, biomolecular, and financial systems, emphasizing practical applications.

Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications

This survey paper presents a detailed examination of the broad applicability of complex network theory to various real-world phenomena, encompassing both theoretical and empirical perspectives. The authors, Costa et al., systematically categorize these applications into at least 22 domains, thus highlighting the pervasive utility and integrative capability of complex networks across different scientific fields.

Complex networks offer a methodological framework that is both universal and adaptable, addressing the dynamics and connectivity of systems that display non-linear relationships. The survey begins with a foundational overview of network theory, including key concepts such as network representation, metrics, and models. This foundation is critical for framing the subsequent discussions on the diverse applications of complex networks.

The paper categorizes applications by domain, providing comprehensive examples and empirical results:

1. Social and Professional Networks: In the analysis of social connections, such as those between actors, acquaintances, and collaborations, complex networks reveal insights into phenomena like the "small-world" property and scale-free degree distributions. Networks of professional relationships, including those in scientific collaborations and email exchanges, further demonstrate the robustness and vulnerabilities inherent in these structures.
2. Biomolecular Networks: Complex networks have become indispensable in understanding protein-protein interactions, metabolic pathways, and genetic regulatory systems. Such networks are characterized by a modular and hierarchical structure, crucial for elucidating biological processes and functional dynamics within cells.
3. Infrastructure Systems: The examination of communication networks, including the Internet and WWW, coupled with transportation networks like global airline routes and road systems, demonstrates the resilience and complexity of these essential infrastructures. Modeling these systems helps in identifying potential points of failure and optimizing network performance under varying conditions.
4. Financial and Economic Systems: Among economic networks, trade and financial markets are analyzed for their intricate dependency structures. The ability to model correlations and cascades in financial data sets demonstrates the predictive power of network-based approaches in assessing market behaviors and global financial stability.
5. Linguistics and Cultural Phenomena: Language networks, constructed from text corpora and lexical databases, are explored to uncover underlying cognitive and communicative principles. These studies reveal the semiotic structures that guide language use and evolution, enhancing our understanding of linguistic systems.
6. Natural Sciences: Networks are employed to model ecological interactions, analysis of climate systems, seismological activity, and even the topology of chemical processes. These applications provide insights into environmental dynamics, offering robust tools for predicting changes and cascading effects in complex natural systems.
7. Medical and Epidemiological Applications: In the field of medicine, network models facilitate the understanding of disease spread and genetic disorders. Epidemiological models, particularly those based on scale-free networks, underscore the critical role of network topology in the transmission and control of infectious diseases.

The survey underscores the varying scales and sizes of these networks, from small neural systems to extensive communication networks encompassing millions of nodes. It reviews the plethora of measurements used to characterize these networks and the theoretical models employed or developed to correlate with empirical findings.

The paper serves as a vital resource for researchers looking to comprehend the multifaceted implications of network theory across different domains. It emphasizes the ongoing need for innovative models and comprehensive metrics to fully exploit the potential of complex networks for real-world problem-solving.

While the authors highlight the breadth of current applications, they also call attention to underexplored areas, suggesting that further interdisciplinary collaboration and methodological advancements are crucial for the continued evolution and impact of complex networks in science and industry.