Property Graph

Property graphs, which represent data as nodes and edges with associated attributes, are increasingly used to model complex relationships in diverse domains. Current research focuses on efficiently mining patterns within these graphs, employing techniques like graph attention networks and rule-based reasoning to identify recurring structures and associations, as well as developing effective graph embedding methods that leverage node and edge properties for improved machine learning tasks. This work has significant implications for various applications, including vulnerability detection in software, urban planning through building pattern recognition, and enhancing the performance of graph databases through integration with neural networks.