A Survey on Graph Neural Networks for Graph Summarization

As large-scale graphs become more widespread today, it exposes computational challenges to extract, process, and interpret large graph data. It is therefore natural to search for ways to summarize the original graph while maintaining its key characteristics. In this survey, we outline the most current progress of deep learning on graphs for graph summarization explicitly concentrating on Graph Neural Networks (GNNs) methods. We structure the paper into four categories, including graph recurrent networks, graph convolutional networks, graph autoencoders, and graph attention networks. We also discuss a new booming line of research which is elaborating on using graph reinforcement learning for evaluating and improving the quality of graph summaries. Finally, we conclude this survey and discuss a number of open research challenges that would motivate further study in this area.

Towards Mining Creative Thinking Patterns from Educational Data

Creativity, i.e., the process of generating and developing fresh and original ideas or products that are useful or effective, is a valuable skill in a variety of domains. Creativity is called an essential 21st-century skill that should be taught in schools. The use of educational technology to promote creativity is an active study field, as evidenced by several studies linking creativity in the classroom to beneficial learning outcomes. Despite the burgeoning body of research on adaptive technology for education, mining creative thinking patterns from educational data remains a challenging task. In this paper, to address this challenge, we put the first step towards formalizing educational knowledge by constructing a domain-specific Knowledge Base to identify essential concepts, facts, and assumptions in identifying creative patterns. We then introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. Finally, we present a rule-based approach to learning from the Knowledge Base, and facilitate mining creative thinking patterns from contextualized data and knowledge. We evaluate our approach with real-world datasets and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students' activities and assessment tasks.