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41000图理论讲师：Luca Trevisan本课程是关于研究网络的算法和分析技术，尤其是从有趣的生成模型中采样的随机网络。该课程对有向和无向图的基本熟悉，连接性和较强的连接性的概念，以及图形及其属性的BFS和DFS访问。我们将研究线性代数技术在图形上的应用，有关光谱图理论和光谱算法的各种结果，我们将在随机块模型中分析用于社区检测的光谱算法，并在随机图中找到种植的集团。第1周：连通性，剪切和光谱图理论讲座1：拉普拉斯（Laplacian）和连通性讲座的无向图，特征值的拉普拉斯矩阵：图形分配的光谱算法及其分析第3：拉普拉卡（Laplacian eigenvalues and Combinix Properties properix pertrix pertrix pertrix pertrix pertrix pertrix pertrix properrix and pertrix propertrix and pertrix propertrix propertry propertrix propertrix： algorithms for random graphs Lecture 4: spectrum of the adjacency matrix of random graphs, Matrix Chernoff bounds, applications Lecture 5: spectral algorithms for finding planted cliques in random graphs Lecture 6: spectral algorithms for community detection in the stochastic block model Week 3: other matrix norms and semidefinite programming algorithms Lecture 7: semidefinite programming, Grothendieck inequality, and more on community detection Lecture 8: semidefinite programming for community detection in the stochastic block model Lecture 9: semidefinite programming and robustness Week 4: spectra of graphs, random walks, and other random processes Lecture 10: the spectrum of Cayley graphs Lecture 11: expanders, random walks and MCMC algorithms Lecture 12: percolation
41000图理论讲师：Luca Trevisan
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