With the integration and interaction between the ever-increasing Cyber-Physical-Social Systems (CPSS), the world is becoming broadly connected where the connections among people, things, behaviors, events, and sub-systems are ubiquitous and complex. The effective analysis of such complex connections spread to a broad spectrum is critical for building next-generation methodologies, techniques and systems to resolve newly emerging practical challenges, e.g., epidemic tracing, fraud detection, crowdsourcing computing, social management, and artificial intelligence governance. Broad connection analytics (BCAS) aims to explore the ubiquitous and complex relationships between the objects of any kinds and cross domains in CPSS, with the special interests in those implicit, sparse, high-order and heterogeneous relationships between objects. The explored connections may take a broad variety of forms, including but not limited to co-occurrence, alignment, relevance, correlation, dependency, coupling, spatial-temporal patterns, and causal relationships. This Special Issue on Broad Connection Analytics will collect and report the latest advancements in artificial intelligence, data science, and applications of analyzing broad connections in big data.

Scope of Interest:
This special issue will solicit the recent theoretical and practical advancements in broad connection analytics in areas relevant but not limited to the following:
• The quantification and measurement of broad connections.
• The theory of the associative amplification effect in broad connections.
• Representation learning of broad connections in high-dimensional and sparse data.
• Representation learning of broad connections in spatial-temporal data.
• Broad connection mining across multiple domains and modalities.
• Deep learning based broad connection analysis.
• Explainable learning of broad connections.
• Visual analytics and visualization of broad connections.
• Accelerated or parallel computing of broad connection analysis.
• Hybrid intelligent systems for broad connection analysis.
• Applications and tools for broad connection analysis in financial technology, cyber security, knowledge discovery, etc.

• Guest editors (TBD)
Xueqi Cheng, Institute of Computing Technology, CAS, China
Francesco Bonchi, Center for Artificial Intelligence, Italy
Huan Liu, Arizona State University, USA
Enhong Chen, University of Science and Technology of China, China

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• Important Dates
Submission deadline: Dec 30th, 2022
Notification of final decision: Mar 30th, 2023
Final manuscript (camera ready) submission deadline: July 30th, 2023
Issue of Publication: Oct 2023

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• Submission and Review of Papers
Submitted papers should be original and not be under consideration elsewhere for publication. The authors should follow the journal guidelines, regarding the manuscript content and its format when preparing their manuscripts. All papers will be reviewed by at least three independent reviewers for their suitability in terms of technical novelty, scientific rigor, scope, and relevance to this special
issue.

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• PAPER SUBMISSION GUIDELINES
Paper submission should conform to the information for authors available at
https://www.springer.com/journal/41060/. Please submit your papers through the online system and be sure to select the special issue name “SI: CCF BigData on BCAS.”