1st High Performance Graph Data Mining and Machine Learning workshop
18th November 2017 | IEEE ICDM 2017 conference, New Orleans, USA

Call for papers

Topics of Interest

The topics of interest of the HPGDML'17 include multiple aspects of graph processing and machine learning on high performance systems, but not limited to:

  • Novel large graph data management systems
  • Deep Learning and its applications
  • Novel large graph processing frameworks and programming paradigms
  • Graph processing in many core processors such as GPGPUs/FPGAs, Xeon Phi, etc.
  • Graph data mining in HPC Clouds
  • Workflows which involve both graph data mining and machine learning
  • HPC graph databases and query languages
  • Novel graph partitioning algorithms
  • Application experiences of large graph processing on HPC environments
  • Benchmarks for large graph processing workloads
  • Performance characterization of large graph mining tasks
  • Scalable graph analysis algorithms and novel data structures
  • High performance streaming graph processing algorithms

Type of Papers

HPGDML'17 will host two types of papers:

  • Full research papers (8 Pages)
  • Short papers (4 Pages)

Authors should indicate in their abstracts the kind of submission that the paper belongs to, to help reviewers better understand their contributions.

Submission Format

Submissions must be made in PDF formatted according to IEEE 2-column template.
Submissions must be up to 8 pages full papers or 4 page short papers including references, diagrams, and appendices, if any. Submissions exceeding the above specified page limits will be rejected without review.


All articles accepted will be published by the IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library.