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International Journal of Data and Network Science - Growing Science | 2024 Cite Score:9.7 | Q2

International Journal of Data and Network Science With Cite Score

Cite Score and Journal Rank of International Journal of Data and Network Science

  • About: International Journal of Data and Network Science (IJDNS) is a peer-reviewed journal that focuses on the intersection of data science and network science. It explores advanced methods and technologies for data analysis, network modeling, and their applications across various domains. The journal covers a broad spectrum of topics including big data analytics, network dynamics, data mining, machine learning, network optimization, and complex systems. IJDNS aims to advance the field by providing a platform for high-quality research and innovative contributions in data and network science.
  • Objective:
    The primary objective of IJDNS is to advance the understanding and application of data science and network science through the publication of significant research findings and technological advancements. The journal seeks to offer a comprehensive resource for researchers, practitioners, and policymakers to explore new theories, methodologies, and applications in data analysis and network science. IJDNS aims to support the development of effective solutions and strategies that address complex challenges in these fields.
  • Interdisciplinary Approach:
    IJDNS adopts an interdisciplinary approach, encouraging contributions from various fields such as computer science, information technology, applied mathematics, statistics, and engineering. This approach ensures a broad exploration of data and network science topics, integrating different perspectives and methodologies. By promoting interdisciplinary research, the journal addresses multifaceted challenges and fosters the development of integrated solutions that advance the state of the art in data and network science.
  • Impact:
    The journal has a notable impact on both academic research and practical applications in data and network science. It is widely cited by researchers, practitioners, and industry professionals interested in the latest developments in data analytics, network modeling, and related areas. The research published in IJDNS contributes to the advancement of new technologies, methodologies, and applications that enhance the capabilities and effectiveness of data and network analysis. The journal serves as a valuable resource for those involved in research, development, and implementation in these fields.
  • Significance:
    IJDNS plays a crucial role in advancing the study and application of data and network science by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative solutions and technologies that address current and emerging challenges in data analysis and network science. The journals commitment to excellence and its interdisciplinary focus make it an essential resource for anyone involved in research, development, and application in these domains. Through its rigorous scholarship and broad coverage, IJDNS helps shape the future of data and network science.

  • Editor-in-Chief:  Mohammad Reza Ghaeli

  • Scope: The International Journal of Data and Network Science focuses on advancements and research in the areas of data science and network science. Its scope includes, but is not limited to:
  • Data Science: Research on methodologies, algorithms, and tools for data analysis, including data mining, machine learning, big data analytics, and statistical analysis.
  • Network Science: Studies on the structure, dynamics, and functions of networks, including social networks, communication networks, biological networks, and technological networks.
  • Graph Theory: Exploration of theoretical and applied aspects of graph theory, including network modeling, graph algorithms, and network optimization.
  • Data Management and Processing: Research on techniques for managing, storing, and processing large datasets, including data warehousing, distributed databases, and cloud computing.
  • Network Modeling and Simulation: Studies on the modeling and simulation of network systems, including network traffic modeling, network performance analysis, and simulation of network dynamics.
  • Data Mining and Knowledge Discovery: Research on methods for discovering patterns, relationships, and insights from large datasets, including clustering, classification, and association rule mining.
  • Machine Learning and Artificial Intelligence: Exploration of machine learning algorithms and AI techniques applied to data analysis and network problems, including supervised and unsupervised learning, deep learning, and reinforcement learning.
  • Network Security and Privacy: Studies on methods and technologies for ensuring the security and privacy of networks and data, including encryption, access control, and intrusion detection.
  • Big Data Technologies: Research on technologies and frameworks for handling and analyzing big data, including Hadoop, Spark, and other distributed computing platforms.
  • Social Network Analysis: Exploration of the structure and behavior of social networks, including community detection, influence analysis, and social network dynamics.
  • Complex Networks: Research on networks characterized by complex interactions and emergent behaviors, including small-world networks, scale-free networks, and dynamic networks.
  • Data Visualization: Studies on techniques and tools for visualizing data and network structures, including interactive visualization, graphical representations, and visual analytics.
  • Internet of Things (IoT) Networks: Research on networks of interconnected devices in IoT environments, including IoT architecture, communication protocols, and data management.
  • Network Algorithms and Protocols: Exploration of algorithms and protocols for network operations, including routing algorithms, network protocols, and resource management.
  • Data Ethics and Governance: Studies on the ethical implications and governance of data use, including data stewardship, compliance, and ethical data practices.
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  • Print ISSN:  25618148

    Electronic ISSN:  

  • Abstracting and Indexing:  Scopus

  • Imapct Factor :  

  • Subject Area and Category:   Computer Science, Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, Information Systems, Software, Social Sciences, Communication

  • Publication Frequency:  

  • H Index:  40

  • Best Quartile:

    Q1:  

    Q2:  Communication

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  • Cite Score:  9.7

  • SNIP:  1.149

  • Journal Rank(SJR):  0.488