Network Motif

Network motifs are recurring, small subgraphs within larger networks that reveal important structural patterns and functional roles. Current research focuses on developing efficient algorithms, often leveraging graph neural networks and deep learning techniques, to identify and analyze these motifs in diverse datasets, including social networks, biological systems, and artificial neural networks. This work aims to improve the speed and accuracy of motif detection, enabling better understanding of network dynamics and facilitating applications in areas such as disease prediction, drug discovery, and A/B testing optimization. The ability to effectively characterize and utilize network motifs promises significant advancements in various scientific fields and practical applications.