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MLG 2018 : 14th International Workshop on Mining and Learning with Graphs


Conference Series : Mining and Learning with Graphs
When Aug 20, 2018 - Aug 20, 2018
Where London, UK
Abstract Registration Due May 8, 2018
Submission Deadline May 15, 2018
Notification Due Jun 8, 2018
Final Version Due Jul 28, 2018
Categories    data mining   complex networks   network science   machine learning

Call For Papers

14th International Workshop on Mining and Learning with Graphs (MLG 2018)
August 20, 2018
London, UK (co-located with KDD 2018)

Call for papers:

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 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 and empirical studies. 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. 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
- Network embedding models
- Statistical models of graph structure
- Combinatorial graph methods
- Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graphs

Applications and analysis:
- Analysis of social media
- Analysis of biological networks
- Knowledge graph construction
- Large-scale analysis and modeling

All papers will be peer reviewed, single-blinded. 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)
- 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. 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 and considered for $1,000 best paper award sponsored by Kyndi.

Submission Deadline: May 15, 2018
Notification: June 8, 2018
Final Version: June 28, 2018
Workshop: August 20, 2018

Submission instructions can be found on
Please send enquiries to

We look forward to seeing you at the workshop!

Shobeir Fakhraei (University of Southern California, ISI)
Danai Koutra (University of Michigan, Ann Arbor)
Julian McAuley (University of California, San Diego)
Bryan Perozzi (Google Research)
Tim Weninger (University of Notre Dame)

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Find a PDF copy of this CFP here:

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