Machine Learning on Graphs
MLoG Workshop at ICDM'22
More Details!

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 ICDM'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: September 17nd, 2022
  • Notification of Acceptance: September, 23th, 2022October, 8th, 2022
  • Camera-ready paper due: October, 1st, 2022 October, 15th, 2022
  • MLoG at ICDM'22 Workshop day: November 28th, 2022
(All deadlines are 11:59pm Pacific Daylight Time. We have updated the above to align with ICDM's key dates.)

Submission Details

All submissions should be 2 to 8 pages (including all references, tables, and figures), double-column pdfs, and following the IEEE conference format - please refer to the ICDM'22 website for futher details here.

Submissions will be reviewed 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. The best paper (according to the reviewers' ratings) will be announced at the end of the workshop. Following ICDM tradition, all accepted works at the MLoG workshop will be published in formal proceedings by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for other workshops (that are archival), conferences, or journals.

To submit your work, please use the following Submission Link

Note that at least one of the authors of the accepted workshop papers must register for the workshop (details to come on the main ICDM'22 website). For questions about submission, please contact us at: mlog-workshop-2022@googlegroups.com

Workshop Tentative Program. (Local time EST in Orlando, FL at ICDM'22).

Morning Session (EST)

09:00 - 09:10 Welcome and Opening Remarks

09:10 - 10:00 Keynote presentation 1 - Yue Ning

10:00 - 10:30 Break

10:30 - 11:15 Keynote presentation 2 - Hanghang Tong
11:15 - 12:00 Keynote presentation 3 - Tara Savafi

12:00 - 13:00 Lunch break

13:00 - 13:15 Contributing paper - Representing Social Networks as Dynamic Heterogeneous Graphs
13:15 - 13:30 Contributing paper - LSP: Acceleration of Graph Neural Networks via Locality Sensitive Pruning of Graphs

13:30 - 14:15 Keynote presentation 4 - Bryan Perozzi
14:15 - 15:00 Keynote presentation 5 - Tong Zhao

15:00 - 15:30 Break

15:30 - 16:15 Keynote presentation 6 - Vagelis Papalexakis

16:15 - 16:30 Contributing paper - EnD: Enhanced Dedensification for Graph Compressing and Embedding

16:30 - 16:45 Contributing paper - Demystify Degree-related Bias in Link Prediction

16:45 - 17:00 Closing Remarks

Keynote Speakers

TBD


Yue Ning

Assistant Professor


Stevens Institute of Technology

TBD


Vagelis Papalexakis

Associate Professor


University of California Riverside

TBD


Bryan Perozzi

Senior Staff Research Scientist


Google Research

TBD


Tara Safavi

Research Scientist


Microsoft Research

TBD


Hanghang Tong

Associate Professor


University of Illinois Urbana-Champaign

TBD


Tong Zhao

Research Scientist


Snap Research

Organization


Workshop Co-Chairs

Yao Ma

Assistant Professor

New Jersey Institute of Technology

Tyler Derr

Assistant Professor

Vanderbilt University

Benedek Rozemberczki

Senior Machine Learning Engineer

AstraZeneca

Neil Shah

Lead Research Scientist

Snap Inc.

Shirui Pan

ARC Future Fellow and Senior Lecturer

Monash University




Additional Workshop Organizers

Web Chair


Yu Wang

PhD Student

Vanderbilt University

Publicity Chair


Vijay Prakash Dwivedi

PhD Student

Nanyang Technological University