A CURRICULUM STUDY: HUKOU QINGYANG OPERA KNOWLEDGE TRANSMISSION IN JIANGXI PROVINCE, CHINA

Qingyang opera is a cultural feature of Hukou, with the "Xiulan Ban" singing on the banks of Poyang Lake and the Yangtze River. The objective was to study the transmission process of Hukou Qinyang Opera knowledge in Jiangxi Province, China. The research tools include interviews and observation to obtain research data. Qualitative research uses observation, experimentation, and analysis to determine the essential characteristics of things. The informants are classified into three groups: key, Qingyang opera performers, and general. The results showed that the transmission of Qinyang Opera has two systems: one is the informal transmission, with local artists as the main transmission, and the second is the formal transmission, with university and academic institution participants as the main participants in the transmission. &nbsp

A Metadata-Driven Approach to Understand Graph Neural Networks

Graph Neural Networks (GNNs) have achieved remarkable success in various applications, but their performance can be sensitive to specific data properties of the graph datasets they operate on. Current literature on understanding the limitations of GNNs has primarily employed a $\textit{model-driven}$ approach that leverage heuristics and domain knowledge from network science or graph theory to model the GNN behaviors, which is time-consuming and highly subjective. In this work, we propose a $\textit{metadata-driven}$ approach to analyze the sensitivity of GNNs to graph data properties, motivated by the increasing availability of graph learning benchmarks. We perform a multivariate sparse regression analysis on the metadata derived from benchmarking GNN performance across diverse datasets, yielding a set of salient data properties. To validate the effectiveness of our data-driven approach, we focus on one identified data property, the degree distribution, and investigate how this property influences GNN performance through theoretical analysis and controlled experiments. Our theoretical findings reveal that datasets with more balanced degree distribution exhibit better linear separability of node representations, thus leading to better GNN performance. We also conduct controlled experiments using synthetic datasets with varying degree distributions, and the results align well with our theoretical findings. Collectively, both the theoretical analysis and controlled experiments verify that the proposed metadata-driven approach is effective in identifying critical data properties for GNNs