posted by user: shobeir || 7377 views || tracked by 19 users: [display]

MLG 2016 : 12th International Workshop on Mining and Learning with Graphs

FacebookTwitterLinkedInGoogle


Conference Series : Mining and Learning with Graphs
 
Link: http://www.mlgworkshop.org/2016/
 
When Aug 13, 2016 - Aug 13, 2016
Where San Francisco, CA
Submission Deadline May 16, 2016
Notification Due Jun 13, 2016
Final Version Due Jun 25, 2016
Categories    graph mining   complex networks   network science   data mining
 

Call For Papers

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

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:

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). 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
http://www.mlgworkshop.org/2016/

Related Resources

MLDM 2021   17th International Conference on Machine Learning and Data Mining
ASONAM 2020   The 2020 IEEE/ACM International Conference on. Advances in Social Networks Analysis and Mining
ICDM 2021   21th Industrial Conference on Data Mining
WSDM 2021   14th ACM Conference on Web Search and Data Mining
CISDM 2020   2020 2nd Euro-Asia Conference on Information System and Data Mining (CISDM 2020)
FAIML 2020-Ei Compendex & Scopus 2020   2020 2nd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2020)
3rd ICTEL 2021   International Conference on Teaching, Education & Learning, 23-24 March, Singapore
ICDE 2021   International Conference on Data Engineering
COMIT 2020   4th International Conference on Computer Science and Information Technology
SDM 2021   SIAM International Conference on Data Mining