Large Scale Graph Data Analytics

  in Special Issue   Posted on October 23, 2020

Information for the Special Issue

Submission Deadline: Sun 31 Jan 2021
Journal Impact Factor : 1.405
Journal Name : World Wide Web
Journal Publisher:
Website for the Special Issue: https://www.springer.com/journal/11280/updates/18507552
Journal & Submission Website: https://www.springer.com/journal/11280

Special Issue Call for Papers:

Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, Ying ZhangImportant DatesManuscript submission due: 31 January 2021First notification: 30 April 2021Revised version due: 30 June 2021Final notification: 31 July 2021Final paper due: 15 August 2021Various application domains such as social networks, communication networks, collaboration networks, biological networks, transportation networks, knowledge networks naturally generate large scale graph data to capture the connectedness among entities. Driven by these applications, there is an increasing demand for the development of novel graph analytics models and scalable graph analytics techniques and systems.  The special issue encourages submissions of high-quality research papers in the topic of large-scale graph data analytics from various disciplines including databases, data mining, machine learning, graph theory and algorithms. We also encourage submissions with novel applications of graph techniques to various domains including cybersecurity, healthcare, social networks, business data analytics, etc.

Topics of interest include but are not limited to:

  • Graph data model, storage, indexing and query processing techniques
  • Graph mining techniques
  • Techniques for parallel and distributed graph data processing
  • Graph visualization techniques and system interfaces
  • Dynamic and streaming graph data analytics
  • Spatial-temporal graph analytics
  • AI techniques for graphs
  • Graph analytics in various application domains such as social networks, semantic web, biological data, business processes, transport data, etc.
  • Vision papers to survey the area of graph data analytics as well as describe the future research directions

Submission guidelines:

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the IJCV website at: https://www.springer.com/11280 .

Please select “Large Scale Graph Data Analytics” at the beginning of the submission process.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

All papers will be reviewed following standard reviewing procedures for the Journal. 

Papers must be prepared in accordance with the Journal guidelines: www.springer.com/11280

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Guest Editor Bios: 

Short Bio:

Xuemin Lin received the BSc degree in applied math from Fudan University, in 1984, and the PhD degree in computer science from the University of Queensland, in 1992. He is a scientia professor with the School of Computer Science and Engineering, the University of New South Wales (UNSW). He has been the head of the Database Research Group at UNSW since 2002. Before joining UNSW, he held various academic positions at the University of Queensland and the University of Western Australia. During 1984-1988, he studied for PhD in Applied Math at Fudan University. He is a fellow of the IEEE. His current research interests include the data streams, approximate query processing, spatial data analysis, and graph visualization.

Lu Qin received the BEng degree from the Department of Computer Science and Technology, Renmin University of China, in 2006, and the PhD degree from the Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, in 2010. He is now an Associate Professor with the Centre for Artificial Intelligence, University of Technology Sydney. His research interests include big graph processing and I/O efficient algorithms.

Wenjie Zhang received the PhD degree in computer science and engineering from the University of New South Wales, in 2010. She is currently an associate professor and ARC DECRA (Australian Research Council Discovery Early Career Researcher Award) fellow in the School of Computer Science and Engineering, the University of New South Wales, Australia. Her research interests lie in big data management and processing.

Ying Zhang received the BSc and MSc degrees in computer science from Peking University, and the PhD degree in computer science from the University of New South Wales. He is a professor and ARC future fellow (2017-2021) at CAI, the University of Technology, Sydney (UTS). His research interests include query processing on data stream, uncertain data, and graphs. He was an ARC APD (2011-2013) and ARC DECRA (2014-2016) holder.

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