MLG: Mining and Learning with Graphs

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2021 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
MLG 2020 Mining and Learning with Graphs
Aug 24, 2020 - Aug 24, 2020 Virtual Jun 15, 2020
MLG 2019 Mining and Learning with Graphs
Aug 5, 2019 - Aug 5, 2019 Anchorage, Alaska, USA May 12, 2019
MLG 2018 14th International Workshop on Mining and Learning with Graphs
Aug 20, 2018 - Aug 20, 2018 London, UK May 15, 2018 (May 8, 2018)
MLG 2017 13th International Workshop on Mining and Learning with Graphs
Aug 14, 2017 - Aug 14, 2017 Halifax, Nova Scotia, Canada May 26, 2017
MLG 2016 12th International Workshop on Mining and Learning with Graphs
Aug 14, 2016 - Aug 14, 2016 San Francisco, CA May 27, 2016
MLG 2013 Eleventh Workshop on Mining and Learning with Graphs
Aug 11, 2013 - Aug 11, 2013 Chicago, USA Jun 6, 2013
MLG 2012 Tenth workshop on Mining and Learning with Graphs
Jul 1, 2012 - Jul 1, 2012 Edinburgh, Scotland May 7, 2012
MLG 2011 Ninth Workshop on Mining and Learning with Graphs (MLG 2011)
Aug 20, 2011 - Aug 21, 2011 San Diego, CA TBD
MLG 2010 Workshop on Mining and Learning with Graphs
Jul 24, 2010 - Jul 25, 2010 Washington, USA May 7, 2010
MLG 2009 7th International Workshop on Mining and Learning with Graphs
Jul 2, 2009 - Jul 4, 2009 Leuven, Belgium Apr 3, 2009
MLG 2008 6th International Workshop on Mining and Learning with Graphs
Jul 4, 2008 - Jul 5, 2008 Helsinki, Finland Apr 1, 2008
 
 

Present CFP : 2020


Call for Papers

Due to public health concerns in light of the unfolding COVID-19 outbreak. We will follow exactly the option that ACM SIGKDD and the KDD 2020 organizing committee will suggest and will follow the style that the KDD conference adopts. Please check this website regularly for updates.

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 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 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)
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.
 

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