This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in graph analysis. In doing so, we aim to better understand the overarching principles and the limitations of our current methods and to inspire research on new algorithms and techniques for mining and learning with graphs.

To reflect the broad scope of work on mining and learning with graphs, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications, empirical studies and reflection papers. As an example, the growth of user-generated content on blogs, microblogs, discussion forums, product reviews, etc., has given rise to a host of new opportunities for graph mining in the analysis of social media. More recently, the advent of neural methods for learning graph representations has spurred numerous works in embedding network entities for diverse applications including ranking and retrieval, traffic routing and drug-discovery. We encourage submissions on theory, methods, and applications focusing on a broad range of graph-based approaches in various domains.

Topics of interest include, but are not limited to:

• Theoretical aspects:
  • Computational or statistical learning theory related to graphs
  • Theoretical analysis of graph algorithms or models
  • Sampling and evaluation issues in graph algorithms
  • Analysis of dynamic graphs
• Algorithms and methods:
  • Graph mining
  • Probabilistic and graphical models for structured data
  • Heterogeneous/multi-model graph analysis
  • Graph neural networks and graph representation learning
  • Statistical models of graph structure
  • Combinatorial graph methods
  • Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph
• Applications and analysis:
  • Analysis of social media
  • Analysis of biological networks
  • Knowledge graph construction
  • Large-scale analysis and modeling

We welcome many kinds of papers, such as, but not limited to:

• Novel research papers
• Demo papers
• Work-in-progress papers
• Visionary papers (white papers)
• Appraisal papers of existing methods and tools (e.g., lessons learned)
• Evaluatory papers which revisit validity of domain assumptions
• Relevant work that has been previously published
• Work that will be presented at the main conference

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions.
All papers will be peer reviewed, single-blinded. Submissions must be in PDF, no more than 8 pages long — shorter papers are welcome — and formatted according to the standard double-column ACM Proceedings Style.
The accepted papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.
Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set will also be chosen for oral presentation.

For paper submission, please proceed to the submission website.

Please send enquiries to chair@mlgworkshop.org.

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