[CFP] ACM KDD MLG'20 -- 16th International Workshop on Mining and Learning with Graphs

30 views

Skip to first unread message

Shobeir Fakhraei

unread,

Jun 4, 2020, 4:05:31 PM6/4/20

to mlg-...@googlegroups.com

16th International Workshop on Mining and Learning with Graphs (MLG 2020)

August 24, 2020

In conjunction with KDD (Virtual Conference)

http://www.mlgworkshop.org/2020/

Submission Deadline: June 15, 2020

Due to public health concerns in light of the unfolding COVID-19 outbreak, we follow ACM SIGKDD and the KDD 2020 organizing committee guidelines and will hold MLG as a virtual workshop.

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 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. 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 re-submission 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.

Timeline:

Submission Deadline: June 15, 2020

Notification: July 15, 2020

Final Version: August 1, 2020

Workshop: August 24, 2020

Submission instructions can be found on http://www.mlgworkshop.org/2020/

Please send enquiries to ch...@mlgworkshop.org

Organizers:

Shobeir Fakhraei (Amazon)

Aude Hofleitner (Facebook)

Julian McAuley (University of California, San Diego)

Bryan Perozzi (Google Research)

Tim Weninger (University of Notre Dame)

Program Committee:

Siddharth Bhatia (National University of Singapore), Jundong Li (University of Virginia), Xin-Zeng Wu (Information Sciences Institute), Stefano Leucci (University of L'Aquila), Jin Kyu Kim (Facebook), Hocine Cherifi (University of Burgundy), Dhivya Eswaran (Amazon), Ting Chen (University of California, Los Angeles), Ivan Brugere (University of Illinois at Chicago), Yuan Fang (Singapore Management University), Blaz Novak (Jozef Stefan Institute), Sucheta Soundarajan (Syracuse University), Fred Morstatter (University of Southern California), Acar Tamersoy (NortonLifeLock Research Group), John Palowitch (Google), Austin Benson (Cornell University), Hanghang Tong (University of Illinois at Urbana-Champaign), Larry Holder (Washington State University), Aaron Clauset (University of Colorado Boulder), Jan Ramon (INRIA), Christian Bauckhage (Fraunhofer), Bryan Hooi (National University of Singapore), William Hamilton (Stanford University), Aris Anagnostopoulos (Sapienza University of Rome), Ulf Brefeld (Leuphana Universität Lüneburg), Ali Pinar (Sandia National Laboratories), Alessandro Epasto (Google), Danai Koutra (University of Michigan), Evangelos Papalexakis (University of California Riverside), Stefan Wrobel (Fraunhofer IAIS & Univ. of Bonn), Ana Paula Appel (IBM Research Brazil), Marco Bressan (Sapienza University of Rome)

To receive updates about the current and future workshops and the Graph Mining community, please join the mailing list: https://groups.google.com/d/forum/mlg-list