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

August 24, 2020

In conjunction with KDD (Virtual Conference)  < >

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


Submission Deadline: June 15, 2020

Notification: July 15, 2020

Final Version: August 1, 2020

Workshop: August 24, 2020

Submission instructions can be found on 
< >

Please send enquiries to [log in to unmask]


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)

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Mining community, please join the mailing list: 

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We look forward to seeing you at the workshop!

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