LSGDA 2020

The 2nd International Workshop on Large Scale Graph Data Analytics

Aim and Scope. Various 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 2nd International Workshop on Large Scale Graph Data Analytics (LSGDA 2020) aims to provide a forum for researchers from academia and industry to exchange ideas, techniques and application scenarios in large scale graph data analytics as well as discuss open challenges and identify new research directions in the area. Besides regular research papers, we also welcome vision papers, demonstration papers and papers with industry showcase from various applications.

The workshop will be of interest to researchers in developing techniques for large scale graph data analytics in various application domains. The intended audiences include researchers from both academia and industry who are interested in exploiting the value of large scale graph data.

Publication. Accepted papers will be published in the springer proceedings. Accepted papers with top quality will be recommended for publication in WWW Journal.

Research Interest

Topics of interest include but not limited to:

  • Graph data model, storage, indexing and query processing techniques
  • Graph mining techniques
  • Techniques for distributed graph analytics
  • Graph visualization techniques and system interfaces
  • Dynamic and streaming graph data analytics
  • Spatial-temporal graph analytics
  • AI techniques for graphs
  • Machine learning techniques for graphs
  • Graph analytics in various application domains such as social networks multimedia, 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

The proceedings of the workshops will be published jointly with the conference proceedings. We welcome research papers (full or short), vision papers, demo papers and industry papers showcasing graph analytics in real applications.

Including the bibliography and any possible appendices,

  • Full papers and vision papers should be a maximum of 14 pages in length.
  • Short papers, demo papers and industry papers should be a maximum of 6 pages in length.
  • Please format your paper based on Springer LNCS template.

Please upload your submission to the LSGDA 2020 Research Track through the CMT system at: https://cmt3.research.microsoft.com/LSGDA2020

Important Dates

Paper submission: April 26, 2020 June 10, 2020

Paper notification: June 7, 2020 June 28, 2020

Camera ready deadline: July 10, 2020

Members of the workshop organizers:

General Chair: Xuemin Lin, University of New South Wales, Australia

PC Co-chairs:

  • Lu Qin, University of Technology Sydney, Australia
  • Wenjie Zhang, University of New South Wales, Australia
  • Ying Zhang, University of Technology Sydney, Australia
Publicity Chair: Kai Wang, University of New South Wales, Australia
Publication Chair: You Peng, University of New South Wales, Australia
Web Chair: Dong Wen, University of Technology Sydney, Australia

Members of the program committee:

  • Anil Pacaci, University of Waterloo, Canada
  • Bolin Ding, Data Analytics and Intelligence Lab, Alibaba Group, USA
  • Chuan Xiao, Nagoya University, Japan
  • Chunbin Lin, Amazon Web Services, USA
  • Donatella Firmani, Roma Tre University, Italy
  • Huasong Shan, JD.COM, USA
  • Jiafeng Hu, Google, China
  • Jianye Yang, Hunan University, China
  • Lijun Chang, University of Sydney, Australia
  • Matteo Lissandrini, Aalborg University, Denmark
  • Rong-Hua Li, Beijing Institute of Technology, China
  • Sergey Pupyrev, Facebook, USA
  • Stefano Leucci, University of L'Aquila, Italy
  • Verena Kantere, National Technical University of Athens, Greece
  • Vijil Chenthamarakshan, IBM AI Research, USA
  • Weiren Yu, University of Warwick, UK
  • Xiang Zhao, National University of Defence Technology, China
  • Xin Cao, University of New South Wales, Australia
  • Yuanyuan Zhu, Wuhan University, China
  • Zhaonian Zou, Harbin Institute of Technology, China

Workshop Programs

Currently not available ...