[Final CFP] ACM KDD MLG'18 - 14th International Workshop on Mining and Learning with Graphs (London, UK)
Shobeir Fakhraei
August 20, 2018
London, UK (co-located with KDD 2018)
http://www.mlgworkshop.org/2018/
Deadlines: (Abstract) May 8, 2018 - (Submission) May 15, 2018
Keynotes:
Tanya Berger-Wolf (University of Illinois Chicago)
Luna Dong (Amazon)
Christos Faloutsos (Carnegie Mellon University)
Kristina Lerman (University of Southern California - ISI)
Sujith Ravi (Google Research)
Taha Yasseri (University of Oxford)
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
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 graphs
Applications and analysis:
Analysis of social media
Analysis of biological networks
Knowledge graph construction
Large-scale analysis and modeling
All papers will be peer reviewed, single-blinded. 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 resubmission 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 and considered for $1,000 best paper award sponsored by Kyndi.
Timeline:
Abstract Deadline: May 8, 2018
Submission Deadline: May 15, 2018
Notification: June 8, 2018
Final Version: June 28, 2018
Workshop: August 20, 2018
Submission instructions can be found on http://www.mlgworkshop.org/2018/
Please send enquiries to ch...@mlgworkshop.org
Organizers:
Shobeir Fakhraei (University of Southern California, ISI)
Danai Koutra (University of Michigan, Ann Arbor)
Julian McAuley (University of California, San Diego)
Bryan Perozzi (Google Research)
Tim Weninger (University of Notre Dame)
Program Committee:
Aris Anagnostopoulos (Sapienza University of Rome), Ana Paula Appel (I.B.M.), Miguel Araujo (Feedzai), Arindam Banerjee (University of Minnesota), Christian Bauckhage (Fraunhofer IAIS), Ulf Brefeld (Leuphana Universität Lüneburg), Ivan Brugere (University of Illinois at Chicago), Aaron Clauset (University of Colorado at Boulder), Alessandro Epasto (Google), Emilio Ferrara (University of Southern California), Thomas Gärtner (University of Nottingham), David Gleich (Purdue University), Mohammad Hasan (Indiana U.–Purdue U. Indianapolis), Jake Hofman (Microsoft Research), Larry Holder (Washington State University), Bert Huang (Virginia Tech), Kristian Kersting (TU Darmstadt), Stefano Leucci (ETH Zurich), Fred Morstatter (University of Southern California), Vagelis Papalexakis (University of California Riverside), Ali Pinar (Sandia National Laboratories), Aditya Prakash (Virginia Tech), Arti Ramesh (Binghamton University), Jan Ramon (INRIA), Xiang Ren (University of Southern California), Neil Shah (Snap Inc.), Sucheta Soundarajan (Syracuse University), Yizhou Sun (University of California, Los Angeles), Acar Tamersoy (Symantec Research Labs), Jiliang Tang (Michigan State University), Hanghang Tong (Arizona State University), Stefan Wrobel (Fraunhofer IAIS), Xin-Zeng Wu (Information Sciences Institute), Zhongfei Zhang (Binghamton University), Elena Zheleva (University of Illinois at Chicago)
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
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Find a PDF copy of this CFP here:
http://www.mlgworkshop.org/2018/MLG2018_CFP.pdf
We look forward to seeing you at the workshop!