GRAPHS AND COMBINATORICS

Scope & Guideline

Advancing the boundaries of discrete mathematics.

Introduction

Welcome to the GRAPHS AND COMBINATORICS information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of GRAPHS AND COMBINATORICS, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0911-0119
PublisherSPRINGER JAPAN KK
Support Open AccessNo
CountryJapan
TypeJournal
Convergefrom 1985 to 2024
AbbreviationGRAPH COMBINATOR / Graphs Comb.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressSHIROYAMA TRUST TOWER 5F, 4-3-1 TORANOMON, MINATO-KU, TOKYO 105-6005, JAPAN

Aims and Scopes

The journal 'Graphs and Combinatorics' is dedicated to the exploration of graph theory and combinatorial structures, addressing both theoretical advancements and practical applications. The journal publishes research that contributes to the understanding of graph properties, algorithms, and their relationships with various mathematical concepts.
  1. Graph Theory:
    Focus on the foundational aspects of graph theory, including properties of different types of graphs, graph coloring, connectivity, and domination.
  2. Combinatorial Structures:
    Research on combinatorial designs, configurations, and arrangements that relate to graph theory, including hypergraphs and matroids.
  3. Algorithm Development:
    Emphasis on the creation and analysis of algorithms for solving graph-related problems, particularly in computational graph theory.
  4. Extremal Graph Theory:
    Study of extremal problems in graph theory, such as Turán-type problems and Ramsey theory, which investigate the conditions under which certain properties hold.
  5. Spectral Graph Theory:
    Exploration of the relationships between graph spectra and graph properties, investigating how eigenvalues and eigenvectors can inform us about graph structure.
  6. Applications of Graph Theory:
    Application of graph theoretical concepts to other fields such as computer science, biology, and social networks, showcasing interdisciplinary connections.
Recent publications in 'Graphs and Combinatorics' indicate emerging trends and themes that reflect the current interests and advancements in the field. These topics highlight the journal's responsiveness to new challenges and interdisciplinary connections.
  1. Dynamic and Adaptive Graph Algorithms:
    An increasing focus on algorithms that adapt to changes in graph structures over time, reflecting real-world applications like network resilience and dynamic data analysis.
  2. Random Graph Theory:
    Growing interest in the properties of random graphs, particularly in connection with probabilistic methods and their implications for graph behavior under random conditions.
  3. Graph Neural Networks and Machine Learning:
    Emerging research on the application of graph theory to machine learning and neural networks, exploring how graph structures can enhance learning algorithms and data representation.
  4. Topological Graph Theory:
    Increased exploration of the connections between topology and graph theory, including studies on embeddings, planar graphs, and topological properties.
  5. Interdisciplinary Applications:
    A trend towards applying graph theoretical concepts to various fields, such as biology, sociology, and computer science, indicating a broader impact of graph theory in solving real-world problems.

Declining or Waning

While 'Graphs and Combinatorics' has a rich history of diverse research themes, certain areas have shown a decline in publication frequency or prominence over recent years. This shift may reflect evolving interests in the field or advancements in other related areas.
  1. Classical Graph Coloring Problems:
    Traditional coloring problems, while still relevant, appear to be receiving less emphasis compared to newer, more complex variations of graph coloring and dynamic coloring methods.
  2. Elementary Graph Algorithms:
    Basic algorithms for fundamental graph problems are becoming less frequently published, potentially overshadowed by more sophisticated approaches and applications in computational complexity.
  3. Basic Extremal Graph Theory:
    Foundational extremal graph theory topics, such as simple Turán problems, are less prominent as researchers explore more complex and nuanced variations of these problems.
  4. Geometric Graph Theory:
    Research focused on geometric aspects of graphs, such as intersection graphs and geometric representations, has seen a decrease in popularity, possibly due to a shift towards abstract combinatorial approaches.

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