"斯坦福CS224W：复杂网络分析与构建方法"

Networks are ubiquitous in various fields of study, ranging from social media to biological systems. In the Stanford CS224W Analysis of Networks course taught by Jure Leskovec, students learn how to construct and infer networks from raw data. This involves creating feature matrices, relationship tables, time series, document corpora, image datasets, and more. One example discussed in the course is the study by Jonas Richiardi et al., which explores how correlated gene expression supports synchronous activity in brain networks. By analyzing gene expression data, researchers were able to infer connections between different regions of the brain and understand how they communicate and function together. This highlights the importance of network construction and inference in uncovering hidden patterns and relationships within complex systems. Throughout the course, students are introduced to various methods and algorithms for constructing networks, such as network science, graph theory, machine learning, and statistical modeling. These tools are essential for extracting meaningful information from large and messy datasets, allowing researchers to gain valuable insights into the underlying structure and dynamics of networks. Overall, the Stanford CS224W Analysis of Networks course provides students with the knowledge and skills needed to handle and analyze complex networks effectively. By understanding how to construct and infer networks from raw data, researchers can make significant advancements in a wide range of disciplines, from biology and neuroscience to social sciences and computer science. This course serves as a valuable resource for anyone interested in exploring the fascinating world of networks and uncovering the hidden connections that shape our understanding of the world.