Held in conjunction with KDD'18
Aug 20, 2018 - London, United Kingdom
14th International Workshop on
Mining and Learning with Graphs
Call for Papers

Introduction

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, and many others. These graphs are typically multi-modal, multi-relational and dynamic. In the era of big data, the importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.
There are many challenges involved in effectively mining and learning from this kind of data, including:

  • Understanding the different techniques applicable, including graph mining algorithms, network embeddings, graphical models, latent variable models, matrix factorization methods and more.
  • Dealing with the heterogeneity of the data.
  • The common need for information integration and alignment.
  • Handling dynamic and changing data.
  • Addressing each of these issues at scale.

Traditionally, a number of subareas have contributed to this space: communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

Keynote Speakers

Tanya Berger-Wolf

Tanya Berger-Wolf

Professor
U. of Illinois Chicago

Luna Dong

Luna Dong


Principal Scientist
Amazon

Christos Faloutsos

Christos Faloutsos

Professor
Carnegie Mellon U.

Kristina Lerman

Kristina Lerman


Associate Professor
U. of Southern California

Sujith Ravi

Sujith Ravi


Research Scientist
Google Research

Taha Yasseri

Taha Yasseri


Assistant Professor
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 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.
All papers will be peer reviewed, single-blinded. 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.

For paper submission, please proceed to the submission website.

Please send enquiries to chair@mlgworkshop.org.

To receive updates about the current and future workshops and the Graph Mining community, please join the Mailing List, or follow the Twitter Account.

Important Dates

 

Paper Submission Open: April 1, 2018

Paper Submission Deadline: May 8, 2018

Author Notification: June 8, 2018

Camera Ready: June 28, 2018

Workshop: August 20, 2018

Workshop Organizers

 
Shobeir Fakhraei

Shobeir Fakhraei

Research Scientist
University of Southern California (ISI)

Danai Koutra

Danai Koutra

Assistant Professor
University of Michigan Ann Arbor

Julian McAuley

Julian McAuley

Assistant Professor
University of California San Diego

Bryan Perozzi

Bryan Perozzi

Research Scientist
Google Research
 

Tim Weninger

Tim Weninger

Assistant Professor
University of Notre Dame

 

Program Committee

 

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)

 

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