Applied Network Science

Scope & Guideline

Exploring the frontiers of computational mathematics and networks.

Introduction

Immerse yourself in the scholarly insights of Applied Network Science 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
ISSN-
PublisherSPRINGERNATURE
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationAPPL NETW SCI / Appl. Netw. Sci.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressCAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND

Aims and Scopes

Applied Network Science focuses on the application of network theory and analysis to diverse fields including social sciences, epidemiology, economics, and environmental studies. The journal emphasizes innovative methodologies and interdisciplinary approaches that enhance our understanding of complex systems through network perspectives.
  1. Network Modeling and Analysis:
    The journal publishes research that develops models for various types of networks, including social, biological, and technological networks, focusing on their structure, dynamics, and properties.
  2. Epidemiological Studies:
    There is a strong emphasis on using network science to understand the spread of diseases and the dynamics of epidemics, particularly in the context of infectious diseases and public health.
  3. Social Network Analysis:
    Research on social networks, including their formation, evolution, and influence on behavior and decision-making, is a core area of focus.
  4. Interdisciplinary Applications:
    Applied Network Science encourages contributions that bridge multiple disciplines, showcasing how network analysis can inform fields such as economics, environmental science, and political science.
  5. Algorithm Development:
    The journal features advancements in algorithms for network analysis, including community detection, centrality measures, and predictive modeling, enhancing computational approaches to network science.
Recent publications indicate several emerging trends and themes that reflect the evolving interests of researchers in network science. This shift is characterized by a growing focus on real-time data applications, interdisciplinary approaches, and the integration of advanced computational techniques.
  1. Dynamic and Temporal Networks:
    There is an increasing focus on dynamic networks that evolve over time, capturing the complexities of real-world interactions, such as social media dynamics and epidemic spread.
  2. Machine Learning and AI in Network Analysis:
    The integration of machine learning techniques with network analysis is on the rise, enabling more robust predictive modeling and insights into network behaviors and structures.
  3. Health and Social Behavior Networks:
    Research on health networks, particularly in the context of public health crises like COVID-19, is gaining prominence, reflecting the importance of understanding social behaviors and their impact on health outcomes.
  4. Multilayer and Multiplex Networks:
    The study of multilayer and multiplex networks is emerging as a significant area, focusing on how different types of interactions and relationships can be modeled simultaneously.
  5. Disinformation and Social Media Dynamics:
    Research addressing the role of networks in the spread of disinformation, particularly through social media platforms, is increasingly relevant, highlighting the implications for public discourse and behavior.

Declining or Waning

While the journal has a diverse range of topics, certain areas have shown a decline in publication frequency or interest. This may reflect shifts in research priorities or the maturation of specific subfields within network science.
  1. Traditional Graph Theory Applications:
    There appears to be a waning interest in basic graph theory applications without a clear real-world context or application, as researchers increasingly focus on complex networks with practical implications.
  2. Static Network Models:
    Research focusing solely on static models is declining, as there is a growing emphasis on dynamic and temporal network analysis that reflects real-world changes over time.
  3. Simple Centrality Measures:
    The use of basic centrality measures in isolation is becoming less common, with a shift toward more sophisticated, context-aware metrics that consider network dynamics and attributes.
  4. Descriptive Studies:
    There is a noticeable decline in purely descriptive studies of networks without a clear hypothesis or predictive modeling, as the field moves towards more analytical and predictive frameworks.

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