Next-Generation Big Graph Learning and Analytics (Next-Gen-BiGLA)

Introduction to the Workshop

In today's rapidly evolving digital landscape, the significance of graph-based data structures and algorithms cannot be overstated. Graphs, representing complex networks of data from social media interactions to biological networks and beyond, are fundamental in extracting valuable insights that drive major technological and social advancements. However, as the size and complexity of these graphs continue to expand exponentially, existing graph processing techniques often fall short, unable to effectively support learning and analytic workloads simultaneously or adapt to dynamic changes in real time. Moreover, in an era where data is ubiquitous, ensuring the interoperability, privacy, security, and efficiency of graph-based systems is more critical than ever.

Recognizing the urgent need to address these challenges, we propose the "Next-Generation Big Graph Learning and Analytics (Next-Gen-BiGLA)" workshop. This initiative is dedicated to pioneering advanced solutions in the realm of graph learning and analytics, focusing on scalability, dynamism, and innovative algorithmic approaches. Our workshop will delve into a broad array of cutting-edge topics, including scalable algorithms, graph neural networks, and quantum graph computing, among others.

By convening leading experts and innovators from diverse fields such as bioinformatics, cybersecurity, and social networking, Next-Gen-BiGLA aims to foster a collaborative environment where interdisciplinary approaches and ideas can merge to propel the frontier of graph computing forward. This workshop is not just about theoretical enhancements; it is geared towards realizing practical, scalable solutions that can have a broad socio-economic impact.

Through engaging discussions, collaborative research efforts, and benchmarking the next wave of graph technologies, we envision Next-Gen-BiGLA as a cornerstone event that will shape the future of graph computing. It is an opportunity for the community to share insights, challenge existing paradigms, and collectively advance toward next-generation technologies that harness the full potential of big graph data analytics.

Research Topics Included in the Workshop

• The theoretical foundation for graph management and learning
• Scalable algorithms for graph learning and analytics
• Innovative algorithms and systems for streaming/dynamic graphs
• Graph neural networks and deep learning on graphs
• Bridging foundational models with graph computing
• Temporal graph learning and analytics
• Novel graph applications at large scale
• Multi-model data management that involves graph data
• Quantum algorithms for graph computing and learning
• Applications of scalable graph analytics in domains such as social networks, bioinformatics, and cybersecurity
• Benchmarking and evaluation frameworks for scalable graph processing
• Interoperability and standards for graph data and analytics
• Privacy and security issues in graph learning and analytics
• New hardware/platforms for graph computing
• Energy-saving algorithms/systems for scalable graph computing

Important Dates

Program Co-Chairs

• Dr. Xiaofei Zhang, University of Memphis

• Dr. Xiang Lian, Kent State University

• Dr. Qiang Guan, Kent State University

Program Committee Members

Instructions for Submission

1. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (https://www.ieee.org/conferences/publishing/templates.html). Papers should be up to 10 pages (references included), in the IEEE 2-column format.
2. You are strongly encouraged to print and double-check your PDF file before its submission, especially if your paper contains Asian/European language symbols (such as Chinese/Korean characters or English letters with European fonts).
3. Please submit through the Cyberchair system, at the following address: https://wi-lab.com/cyberchair/2024/bigdata24/scripts/submit.php?subarea=S20&undisplay_detail=1&wh=/cyberchair/2024/bigdata24/scripts/ws_submit.php
4. The workshop adopts a single-blind review policy.

Contact

For questions or inquiries, please email us at xiaofei.zhang@memphis.edu.