CIF: Small: Compressive Network Analytics

计算机和人类网络产生的丰富信息为网络分析创造了令人兴奋的机会，即通过挖掘由各种监视器收集的大量网络数据来获得知识和洞察力的过程。要实现有效的网络分析，必须解决几个重大挑战：-(I)可扩展性。计算机和人类网络的巨大规模使得以可扩展的方式分析海量网络数据集变得具有挑战性。-(Ii)复杂性。真实世界的网络数据集是复杂的，并且经常违反现有分析技术的操作条件。-(三)稳健性。在真实世界的网络数据集中，异常和不完美是很常见的。-(四)多样性。网络分析经常需要从具有不同特征和数据质量的不同数据源挖掘信息。本研究通过开发一系列新的压缩感知技术来有效地利用现实世界网络数据集中存在的结构和冗余来解决上述挑战，包括：-(I)簇谱图嵌入，一种新的技术，用于将巨大的图缩减为更小的图，同时保留原始图的基本的聚类和谱信息，-(Ii)透镜分解，一种新的方法，用于将网络数据矩阵精确地分解为低秩矩阵、误差项、噪声矩阵和稀疏矩阵，以及-(Iii)多源光谱学习，这是一种有效集成来自不同数据源的信息的新框架。这项研究承诺显著增强分析海量网络数据集的能力。由此产生的工具和技术在商业、信息技术、网络和网络安全方面具有潜在的应用。最后，这项研究包括一个重要的教育和培训部分。研究成果将纳入本科生和研究生课程以及外联活动。

The rich information generated by computer and human networks creates exciting opportunities for network analytics, namely, the process of gaining knowledge and insights by mining a large amount of network data collected by a diverse set of monitors. To enable effective network analytics, several significant challenges must be addressed:--- (i) Scalability. The enormous scale of computer and human networks makes it challenging to analyze massive network datasets in a scalable fashion. --- (ii) Complexity. Real-world network datasets are complex and often violate the operational conditions of existing analysis techniques. --- (iii) Robustness. Anomalies and imperfections are common in real-world network datasets. --- (iv) Diversity. Network analytics often requires mining information from diverse data sources with different characteristics and data quality.This research addresses the above challenges by developing a series of novel compressive sensing techniques to effectively exploit the presence of structure and redundancy in real-world network datasets, including:--- (i) clustered spectral graph embedding, a novel technique for reducing a massive graph to a much smaller graph while preserving essential clustering and spectral information of the original graph,--- (ii) LENS decomposition, a novel method for accurately decomposing a network data matrix into a Low-rank matrix, an Error term, a Noise matrix, and a Sparse matrix, and --- (iii) multi-source spectral learning, a novel framework for effectively integrating information from diverse data sources. The research promises to significantly enhance the ability to analyze massive network datasets. The resulting tools and techniques have potential applications in business, information technology, networking and cyber security. Finally, the research includes a significant education and training component. The research results will be integrated into both undergraduate and graduate curricula as well as outreach activities.