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)