CIF: Small: Compressive Network Analytics

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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.