Machine Learning on Graphs
MLoG Workshop at WSDM'22
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About

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way.

MLoG at WSDM'22 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.


Topics of Interest

Graphs are a kind of universal data structure for representing pair-wise relationships between entities, which can be ubiquitously observed in different domains ranging from computer science, social science, physics, chemistry, to biology. Many real-world applications can be treated as computational tasks on graphs. For example, friend recommendation in social networks can be regarded as a link prediction task and predicting properties of chemical compounds can be treated as a graph classification task. To facilitate these tasks, machine learning techniques have been widely adopted to perform analysis. As our ability of generating and collecting data constantly increasing in a unprecedented way, the graph-structure data we are facing in the modern era (especially coming from the web) are becoming more and more diverse, complex and large-scale. Hence, more efforts are required for developing more effective algorithms and deploying them efficiently for real-world applications. In this workshop, we aim to discuss the recent research progress of machine learning on graphs in both theoretical foundations and practical applications. We invite submissions that focus on recent advances in research/development of machine learning on graphs along with their applications.

Theory and methodology papers are welcome from any of the following areas, including but not limited to:
  • Graph Kernels
  • Graph Summarization
  • Graph Coarsening
  • Graph Alignment
  • Graph Generative Models
  • Graph Mining
  • Graph Neural Networks
  • Network Embedding
  • Machine Learning for Graph Combinatorial Optimization
  • Graph Feature Engineering and Selection
  • Scalable Graph Learning Models and Methods
and application papers focused on but not limited to:
  • Recommender Systems
  • Computer Vision
  • Natural Language Processing
  • Bioinformatics (e.g., drug discovery)
  • Cybersecurity (e.g., malware detection/propagation)
  • Financial security (e.g., fraudster detection)
  • Transportation/mobility networks (e.g., traffic prediction)
  • Graph ML Platforms and Systems (e.g., in-database machine learning)


Important Dates

  • Submission deadline: January 8th, 2022
  • Notification of Acceptance: January, 20th, 2022
  • Camera-ready paper due: February, 4th, 2022
  • MLoG at WSDM'22 Workshop day: February 25th, 2022

Submission Details

We invite both long research papers (5-10 pages) and short research/application papers (2-4 pages) including references. All submissions must be in PDF format and formatted according to the new ACM format published in ACM guidelines (e.g., using the ACM LaTeX template on Overleaf here) and selecting the "sigconf" sample. Following the WSDM conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted works will be assessed based on their novelty, technical quality, potential impact, and clarity of writing (and should be in English). For papers that primarily rely on empirical evaluations, the experimental settings and results should be clearly presented and repeatable. We encourage authors to make data and code available publicly when possible. Accepted papers will be posted on this workshop website and will not appear in the WSDM proceedings and are thus non-archival (allowing you to submit works to MLoG at WSDM'22 even if they are current under review elsewhere). The best paper (according to the reviewers' ratings) will be announced at the end of the workshop.

All submissions must be uploaded electronically to EasyChair at: Submission Page

At least one of the authors of the accepted workshop papers must register for the workshop and be present on the day of the workshop.

For questions regarding submissions, please contact us at: wsdm2022mlog@easychair.org

Workshop Program

Accepted Papers

Workshop Program. (Local time MST in Phoenix, AZ at WSDM'22).

Morning Session (MST)

08:30 - 08:40 Welcome and Opening Remarks

08:40 - 09:15 Keynote - Yinglong Xia of Meta AI
09:15 - 09:50 Keynote - Neil Shah of Snap Inc.
09:50 - 10:25 Keynote - Xin Luna Dong of Meta

10:25 - 10:40 Coffee break/Social Networking

10:40 - 11:15 Keynote - Jingrui He of UIUC
11:15 - 11:50 Keynote - Dan McCreary of Optum
11:50 - 11:55 Short break
11:55 - 12:30 Keynote - Jay Yu of TigerGraph

Lunch Break (12:30 - 13:30)

Afternoon Session (MST)


13:30 - 14:05 Keynote - Leman Akoglu of CMU
14:05 - 14:40 Keynote - Meng Jiang of University of Notre Dame

14:40 - 15:00 Coffee break/Social Networking

15:00 - 15:35 Keynote - Yizhou Sun of UCLA
15:35 - 15:55 Future Directions Panel (Keynote Speakers)
15:55 - 16:00 Short break
16:00 - 16:25 Contributed Research Lightning Talks

16:25 - 16:30 Best Paper Award Ceremony & Final Remarks
16:30 - 17:00 Contributed Research Poster Session

Keynote Speakers

Graph-level Embedding:
Speed, Interpretation, Expressiveness


Leman Akoglu

Dean's Associate Professor


CMU

Zero to One Billion:
The Journey to a Rich Product Graph


Xin Luna Dong

Head Scientist


Meta

Towards Understanding
Rare Cateogries on Graphs


Jingrui He

Associate Professor


UIUC

Learning to Augment Graph Data


Meng Jiang

Assistant Professor


University of Notre Dame


Dan McCreary

Distinguished Engineer


Optum

Scaling up Graph Neural Networks at Snap


Neil Shah

Lead Research Scientist


Snap Inc.

Combining Representation Learning
and Logical Rule Reasoning for
Knowledge Graph Inference


Yizhou Sun

Associate Professor


UCLA


When Graph Learning Meets
Industrical Applications


Yinglong Xia

Applied Research Scientist


Meta AI



Graph + ML: TigerGraph's Approach


Jay Yu

VP of Product and Innovation


TigerGraph

Organization


Workshop Co-Chairs

Tyler Derr

Assistant Professor

Vanderbilt University

Yao Ma

Assistant Professor

New Jersey Institute of Technology

Lingfei Wu

Principle Scientist

JD.COM Silicon Valley Research Center

Bill Shi

Senior ML Solution Architect

TigerGraph

Victor Lee

VP of Machine Learning & AI

TigerGraph




Additional Workshop Organizers

Proceedings Chair


Tong Zhao

PhD Student

University of Notre Dame

Web Chair


Yu Wang

PhD Student

Vanderbilt University

Publicity Chair


Hannes Stärk

Research Intern

MIT