Introduction

Deep Learning models are at the core of research in Artificial Intelligence research today. In the era of big data, the importance of being able to effectively mine and learn from graph data is growing, as more and more structured and semi-structured data is becoming available. The success of graph can be attributed to its ability to capture the structural information of the data, extract meaningful features, and reveal the causal inference behind the data. It is a great deal of interest in analyzing data that is best represented as a graph, such as social networks, biological networks, communication networks, and molecular structure networks. The intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, automated planning, reinforcement learning, and data security. Despite these successes, graph neural networks still face many methodological, applicable, or interpretable challenges. This workshop aims to bring together researchers from data mining and machine learning domains to discuss the latest developments and applications of graph-related works. We encourage a lively exchange of ideas and perceptions to share and discuss their latest findings, focused on data-driven science and data mining. The workshop will feature invited talks and contributed papers to provide a platform for exchanging ideas and fostering collaborations.