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)

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