Graph similarity and matching

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 85-88).Measures of graph similarity have a broad array of applications, including comparing chemical structures, navigating complex networks like the World Wide Web, and more recently, analyzing different kinds of biological data. This thesis surveys several different notions of similarity, then focuses on an interesting class of iterative algorithms that use the structural similarity of local neighborhoods to derive pairwise similarity scores between graph elements. We have developed a new similarity measure that uses a linear update to generate both node and edge similarity scores and has desirable convergence properties. This thesis also explores the application of our similarity measure to graph matching. We attempt to correctly position a subgraph GB within a graph GA using a maximum weight matching algorithm applied to the similarity scores between GA and GB. Significant performance improvements are observed when the topological information provided by the similarity measure is combined with additional information about the attributes of the graph elements and their local neighborhoods. Matching results are presented for subgraph matching within randomly-generated graphs; an appendix briefly discusses matching applications in the yeast interactome, a graph representing protein-protein interactions within yeast.by Laura Zager.S.M

Robust Anomaly Detection with Applications to Acoustics and Graphs

Our goal is to develop a robust anomaly detector that can be incorporated into pattern recognition systems that may need to learn, but will never be shunned for making egregious errors. The ability to know what we do not know is a concept often overlooked when developing classifiers to discriminate between different types of normal data in controlled experiments. We believe that an anomaly detector should be used to produce warnings in real applications when operating conditions change dramatically, especially when other classifiers only have a fixed set of bad candidates from which to choose. Our approach to distributional anomaly detection is to gather local information using features tailored to the domain, aggregate all such evidence to form a global density estimate, and then compare it to a model of normal data. A good match to a recognizable distribution is not required. By design, this process can detect the "unknown unknowns" [1] and properly react to the "black swan events" [2] that can have devastating effects on other systems. We demonstrate that our system is robust to anomalies that may not be well-defined or well-understood even if they have contaminated the training data that is assumed to be non-anomalous. In order to develop a more robust speech activity detector, we reformulate the problem to include acoustic anomaly detection and demonstrate state-of-the-art performance using simple distribution modeling techniques that can be used at incredibly high speed. We begin by demonstrating our approach when training on purely normal conversational speech and then remove all annotation from our training data and demonstrate that our techniques can robustly accommodate anomalous training data contamination. When comparing continuous distributions in higher dimensions, we develop a novel method of discarding portions of a semi-parametric model to form a robust estimate of the Kullback-Leibler divergence. Finally, we demonstrate the generality of our approach by using the divergence between distributions of vertex invariants as a graph distance metric and achieve state-of-the-art performance when detecting graph anomalies with neighborhoods of excessive or negligible connectivity. [1] D. Rumsfeld. (2002) Transcript: DoD news briefing - Secretary Rumsfeld and Gen. Myers. [2] N. N. Taleb, The Black Swan: The Impact of the Highly Improbable. Random House, 2007

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data