Call for Chapters

Book Title: Networks and Machine Learning:  Models, Algorithms, and Applications

Publisher: The CRC Press, USA 

Editors: Keshab Nath, Vijayakumar Varadarajan

Download Brochure: Link

About this Book:

"Networks and Machine Learning: Models, Algorithms, and Applications" is an essential guide that delves into the intersection of complex networks with artificial intelligence and machine learning. The book comprehensively covers challenges and opportunities in network-based machine learning, including scalability, efficiency, and ethical implications.

Key sections include Graph Neural Networks, Deep Generative Models, and Probabilistic Graphical Models for Networks, offering insights into various models and their applications. It also discusses Learning Representations for Networks, detailing node, edge, and network embedding techniques. The book addresses Scalable Algorithms for Large-scale Networks, highlighting distributed computing and parallel processing techniques, and explores Distributed Machine Learning for Networks with a focus on Federated Learning and Decentralized Learning. Practical applications are explored in fields such as Social Network Analysis, Network Medicine and Biology, and Network Security and Anomaly Detection. The book concludes with a look into Multi-modal Network Analysis and Learning, emphasizing the integration of heterogeneous data in networks. "Networks and Machine Learning" is a valuable resource for anyone interested in the practical and theoretical aspects of applying machine learning to complex networks.

Important Topics:

1. Challenges and Opportunities in Network-based Machine Learning

   • Data Collection and Preprocessing Challenges, Scalability and Efficiency Issues, Interpretable and Explainable Results, Ethical and Social Implications of Network-based Machine Learning

2. Graph Neural Networks: Theory and Applications

   • Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Recurrent Neural Networks (GRNNs), Applications of Graph Neural Networks

3. Deep Generative Models for Networks

   • Variational Autoencoders for Networks, Generative Adversarial Networks for Networks, Deep Markov Models for Networks, Applications of Deep Generative Models for Networks

4. Probabilistic Graphical Models for Networks

   • Bayesian Networks for Networks, Markov Networks for Networks, Conditional Random Fields for Networks

5. Learning Representations for Networks

   • Node Embedding Techniques, Edge Embedding Techniques, Network Embedding Techniques, Applications of Learning Representations for Networks

6. Scalable Algorithms for Large-scale Networks

   • Distributed Computing Techniques for Networks, Parallel Processing Techniques for Networks, Incremental Learning Techniques for Networks

7. Distributed Machine Learning for Networks

   • Federated Learning for Networks, Split Learning for Networks, Decentralized Learning for Networks, Applications of Distributed Machine Learning for Networks

8. Network Applications: Social Network Analysis and Recommender Systems

   • Community Detection in Social Networks, Link Prediction in Social Networks, Personalized Recommendations in Networks, Applications of Social Network Analysis and Recommender Systems

9. Network Medicine and Biology

   • Disease Diagnosis and Prognosis in Networks, Drug Discovery and Design in Networks, Biological Pathway Analysis in Networks, Applications of Network Medicine and Biology

10.  Network Security and Anomaly Detection

    • Intrusion Detection in Networks, Botnet Detection in Networks, Anomaly Detection in Networks, Applications of Network Security and Anomaly Detection

11. Multi-modal Network Analysis and Learning

    • Integration of Heterogeneous Data in Networks, Multi-view Network Analysis, Fusion of Different Types of Networks

Publication Schedule:

The schedule of the book publication is as follows:

Deadline for chapter submission: March 25, 2024

Author notification (First Round): April 30, 2024

Revised Chapter Submission: May 25, 2024

Author notification (Final Round): June 30, 2024

Camera-ready submission: July 31, 2024

Submission Procedure:

Authors are encouraged to submit their original, high-quality, and unpublished research work focused on the diverse applications of Complex Networks using Artificial Intelligence and Machine Learning.

Submitted manuscripts should conform to the author’s guidelines in the specific CRC press format available, then go to Resources/For Authors. Click to download the LATEX format. Edit in the chapter folder. Download guideline. Latex will be more preferred format. 

Prospective authors need to electronically submit their contributions using EasyChair submission system (EasyChair Submission).

Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. The accepted contributions will be included as a chapter in the book "Networks and Machine Learning:  Models, Algorithms, and Application", by CRC Press, Taylor & Francis Group.

No personal email will be accepted for full chapter submission.

Publication and Indexing:

CRC Press, Florida, USA, is a premier global publisher of science, technology, and medical resources. CRC Press is a member of Taylor & Francis Group, an Informa business. For additional information regarding the publisher, please visit https://www.crcpress.com CRC Taylor & Francis takes pride in having publications indexed by major indices worldwide. So long as they meet the required criteria, all CRC publications are submitted for indexing to indices Thomson Reuters Book Citation Index and SCOPUS.

                Important links:

        Chapter Submission: https://easychair.org/conferences/?conf=netml2024 

        Author Guidelines: https://www.routledge.com/our-customers/authors/why-publish-with-us

        Book Chapter Template: https://github.com/nathkeshab/CRC-Press/raw/main/Alon_v1.13.zip

  Book Editors: