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Present CFP : 2017
13th International Workshop on Mining and Learning with Graphs (MLG 2017)
August 14, 2017 - Halifax, Nova Scotia, Canada (co-located with KDD 2017)
Submission Deadline: May 26, 2017
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. In terms of application areas, 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. Social media analytics is a fertile ground for research at the intersection of mining graphs and text. As such, this year we especially encourage submissions on theory, methods, and applications focusing on the analysis of social media.
Topics of interest include, but are not limited to:
* 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
* Relationships between MLG and statistical relational learning or inductive logic programming
Algorithms and methods:
* Graph mining
* Kernel methods for structured data
* Probabilistic and graphical models for structured data
* (Multi-) Relational data mining
* Methods for structured outputs
* Statistical models of graph structure
* Combinatorial graph methods
* Spectral graph methods
* Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graph
Applications and analysis:
* Analysis of social media
* Social network analysis
* Analysis of biological networks
* Knowledge graph construction
* Large-scale analysis and modeling
We invite the submission of regular research papers (6-8 pages) as well as position papers (2-4 pages). Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some set may also be chosen for oral presentation.
Submission instructions can be found on
Please send enquiries to firstname.lastname@example.org
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