Networks and Heterogeneous Media

Scope & Guideline

Fostering Interdisciplinary Research in Complex Networks

Introduction

Immerse yourself in the scholarly insights of Networks and Heterogeneous Media with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN1556-1801
PublisherAMER INST MATHEMATICAL SCIENCES-AIMS
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2006 to 2024
AbbreviationNETW HETEROG MEDIA / Netw. Heterog. Media
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES

Aims and Scopes

The journal 'Networks and Heterogeneous Media' primarily aims to advance the understanding of complex systems through mathematical modeling and computational techniques. It emphasizes interdisciplinary research that bridges various fields such as mathematics, physics, engineering, and computational sciences.
  1. Mathematical Modeling of Complex Systems:
    The journal focuses on developing mathematical frameworks for various complex systems, including traffic flow, epidemic dynamics, and network interactions. This includes both deterministic and stochastic modeling approaches.
  2. Numerical Analysis and Computational Methods:
    A significant emphasis is placed on numerical techniques for solving partial differential equations (PDEs), integro-differential equations, and other mathematical models. This includes innovative methods like finite element methods, spectral methods, and hybrid approaches.
  3. Interdisciplinary Applications:
    Research published in the journal often applies mathematical theories to real-world problems across different domains, such as epidemiology, environmental science, and engineering, highlighting its interdisciplinary nature.
  4. Stability and Control of Dynamical Systems:
    The journal explores stability analysis, control strategies, and optimization methods for various dynamical systems, particularly in the context of networked systems and fluid dynamics.
  5. Emerging Technologies and Theoretical Advances:
    It also showcases research on emerging technologies, such as machine learning and neural networks, in the context of mathematical modeling and analysis.
The journal has recently expanded its scope to include several trending themes that reflect current research interests and societal challenges. These emerging topics are increasingly prominent in the recent literature.
  1. Epidemic Modeling with Complex Dynamics:
    There is a growing emphasis on modeling disease dynamics that incorporate factors such as variable susceptibility, hybrid models, and the effects of public health interventions, particularly in the context of recent global health challenges.
  2. Machine Learning and Data-Driven Approaches:
    An increasing number of studies are leveraging machine learning techniques for predictive modeling and data analysis, particularly in applications related to traffic flow, epidemic dynamics, and network optimization.
  3. Fractional Calculus and Nonlocal Models:
    Research utilizing fractional calculus to model memory effects and nonlocal interactions in various systems is on the rise, reflecting a trend towards more sophisticated mathematical tools.
  4. Stochastic and Uncertainty Quantification Methods:
    There is a notable increase in papers addressing uncertainty quantification and stochastic methods, particularly in modeling complex systems affected by randomness and variability.
  5. Network Theory and Graph-Based Approaches:
    The application of network theory to understand complex interactions within various systems, such as social networks and biological systems, is becoming increasingly prevalent.

Declining or Waning

While the journal continues to cover a broad spectrum of topics, certain areas have shown a decline in publication frequency or emphasis over recent years. This reflects a shifting focus within the academic community.
  1. Traditional Traffic Flow Models:
    There has been a noticeable decrease in papers focusing on classic traffic models that do not incorporate modern complexities or technologies, such as adaptive traffic control systems or connected vehicle dynamics.
  2. Basic Epidemic Models Without Complex Interactions:
    Research centered around simple compartmental models (e.g., SIR) without considering heterogeneous populations, time delays, or network interactions appears to be waning as more sophisticated models gain traction.
  3. Static Mathematical Theories:
    The journal seems to be moving away from purely theoretical papers that lack practical applications or numerical validation, as the trend shifts towards applied research that demonstrates real-world relevance.
  4. Conventional Numerical Techniques Without Innovation:
    There is a decreasing trend in the publication of papers that present conventional numerical methods without significant advancements or novel applications.

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