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MLG 2022 : 17th International Workshop on Mining and Learning with Graphs


Conference Series : Mining and Learning with Graphs
When Aug 15, 2022 - Aug 15, 2022
Where Washinton, DC
Submission Deadline May 26, 2022
Notification Due Jun 20, 2022
Final Version Due Jul 9, 2022
Categories    graph mining   network science   machine learning   deep learning

Call For Papers

17th International Workshop on Mining and Learning with Graphs (MLG 2022)

August 15, 2022
In conjunction with KDD
Submission Deadline: May 26, 2022

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, empirical studies and reflection papers. 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. More recently, the advent of neural methods for learning graph representations has spurred numerous works in embedding network entities for diverse applications including ranking and retrieval, traffic routing and drug-discovery. 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 and graph neural network 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

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)
Evaluatory papers which revisit validity of domain assumptions
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.

Submission Deadline: May 26, 2022
Notification: June 20, 2022
Final Version: July 9, 2022
Workshop: August 15, 2022

Submission instructions can be found on
Please send enquiries to

Shobeir Fakhraei (Amazon)
Tim Weninger (University of Notre Dame)
Neil Shah (Snap)
Sami Abu-El-Haija (Google Research)
Saurabh Verma (Meta)
Tara Safavi (Microsoft Research)

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