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博士论文答辩:大型网络的动态

PhD Thesis Defense: Dynamics of large networks
课程网址: http://videolectures.net/sep08_leskovec_tdef/  
主讲教师: Jure Leskovec
开课单位: 斯坦福大学
开课时间: 2008-10-22
课程语种: 英语
中文简介:
研究大型网络背后的基本前提是交互导致复杂的集体行为。在我们的工作中,我们发现了时间演变网络的非常有趣和违反直觉的模式,这些模式改变了过去做出的一些基本假设。然后,我们开发模型来解释控制网络演变的过程,将这些模型与真实网络相匹配,并使用它们生成逼真的图形或对其属性进行正式解释。此外,我们的工作有广泛的应用:它可以帮助我们发现异常的图形和异常值,预测未来的图形结构和运行网络演变的模拟。我们研究的另一个重要方面是研究“本地”和“本地”。网络中的传播模式和结构。我们的目标是识别网络的构建块,并找出这些块对网络上的信息或病毒传播的影响模式。我们最近的工作包括研究影响在大型个人产品推荐网络中的传播及其对购买的影响。我们还模拟了博客圈上信息的传播,并提出了有效发现网络中有影响的节点的算法。我们论文的一个中心主题也是对大型数据集的分析,因为某些网络属性只会出现,因此在处理大量数据时变得可见。我们分析了拥有2.4亿人口和2550亿次会话的Microsoft Instant Messenger的全球社交和通信网络。我们还对网络社区结构进行了有趣和违反直觉的观察,这些结构表明只存在小型网络集群,并且随着它们的发展它们会合并并消失。
课程简介: A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and counterintuitive patterns for time evolving networks, which change some of the basic assumptions that were made in the past. We then develop models that explain processes which govern the network evolution, fit such models to real networks, and use them to generate realistic graphs or give formal explanations about their properties. In addition, our work has a wide range of applications: it can help us spot anomalous graphs and outliers, forecast future graph structure and run simulations of network evolution. Another important aspect of our research is the study of “local” patterns and structures of propagation in networks. We aim to identify building blocks of the networks and find the patterns of influence that these blocks have on information or virus propagation over the network. Our recent work included the study of the spread of influence in a large personto- person product recommendation network and its effect on purchases. We also model the propagation of information on the blogosphere, and propose algorithms to efficiently find influential nodes in the network. A central topic of our thesis is also the analysis of large datasets as certain network properties only emerge and thus become visible when dealing with lots of data. We analyze the world’s social and communication network of Microsoft Instant Messenger with 240 million people and 255 billion conversations. We also made interesting and counterintuitive observations about network community structure that suggest that only small network clusters exist, and that they merge and vanish as they grow.
关 键 词: 社交网络; 网络分析; 大网络研究
课程来源: 视频讲座网
最后编审: 2020-06-29:zyk
阅读次数: 43