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ACM SIGKDD 12th International Workshop on Mining and Learning with Graphs (MLG 2016)

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12th International Workshop on Mining and Learning with Graphs (MLG 2016)
August 14, 2016 - San Francisco, CA (co-located with KDD 2016) 
Submission Deadline:  May 27, 2016

Leman Akoglu, Stony Brook University
Lars Backstrom, Facebook
Tamara Kolda, Sandia National Labs
Jennifer Neville, Purdue University
S.V.N. Vishwanathan, University of California Santa Cruz, and Amazon

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
* 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). We recommend papers be formatted according to the standard double-column ACM Proceedings Style. All papers will be peer-reviewed, single-blinded Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set may also be chosen for oral presentation. The accepted papers will be published online and will not be considered archival.

Submission Deadline: May 27, 2016
Notification:  June 13, 2016 
Final Version:  June 25, 2016
Workshop: August 14, 2016

Submission instructions can be found on 

Please send enquiries to [email protected]

We look forward to seeing you at the workshop!

Shobeir Fakhraei (University of Maryland College Park)
Lise Getoor (University of California Santa Cruz)
Danai Koutra (University of Michigan Ann Arbor)
Julian McAuley (University of California San Diego) 
Sean J. Taylor (Facebook)

Program Committee: 
Leman Akoglu (Stony Brook University), Aris Anagnostopoulos (Sapienza University of Rome), Arindam Banerjee (University of Minnesota), Christian Bauckhage (University of Bonn), Hendrik Blockeel (K.U. Leuven), Ulf Brefeld (Leuphana University of Lüneburg), Aaron Clauset (University of Colorado Boulder), Seshadhri Comandur (University of California Santa Cruz), Bing Tian Dai (Singapore Management University), Thomas Gärtner (University of Nottingham), David Gleich (Purdue University), Mohammad Hasan (Indiana University Purdue University), Jake Hofman (Microsoft Research), Larry Holder (Washington State University), Bert Huang (Virginia Tech), Kristian Kersting (Technical University of Dortmund), Jennifer Neville (Purdue University), Ali Pinar (Sandia National Laboratories), Jan Ramon (K.U. Leuven), Jiliang Tang (Yahoo Labs), Hanghang Tong (Arizona State University), Chris Volinsky (AT&T Labs-Research), Stefan Wrobel (University of Bonn), Xifeng Yan (University of California at Santa Barbara), Mohammed Zaki (Rensselaer Polytechnic Institute), Elena Zheleva (Vox Media), Zhongfei Zhang (Binghamton University)

This post first appeared on Beamtenherrschaft, please read the originial post: here

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ACM SIGKDD 12th International Workshop on Mining and Learning with Graphs (MLG 2016)


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