Economic Networks: Theory and Computation

This textbook is an introduction to economic networks, intended for students and researchers in the fields of economics and applied mathematics. The textbook emphasizes quantitative modeling, with the main underlying tools being graph theory, linear algebra, fixed point theory and programming. The text is suitable for a one-semester course, taught either to advanced undergraduate students who are comfortable with linear algebra or to beginning graduate students.Comment: Textbook homepage is https://quantecon.github.io/book-networks/intro.htm

Proceedings of the 3rd International Workshop on Optimal Networks Topologies IWONT 2010

Peer Reviewe

Efficient randomised broadcasting in random regular networks with applications in peer-to-peer systems

We consider broadcasting in random d-regular graphs by using a simple modification of the random phone call model introduced by Karp et al. (Proceedings of the FOCS ’00, 2000). In the phone call model, in every time step, each node calls a randomly chosen neighbour to establish a communication channel to this node. The communication channels can then be used bi-directionally to transmit messages. We show that, if we allow every node to choose four distinct neighbours instead of one, then the average number of message transmissions per node required to broadcast a message efficiently decreases exponentially. Formally, we present an algorithm that has time complexity O(logn) and uses O(nloglogn) transmissions per message. In contrast, we show for the standard model that every distributed algorithm in a restricted address-oblivious model that broadcasts a message in time O(logn) requires Ω(nlogn/logd) message transmissions. Our algorithm efficiently handles limited communication failures, only requires rough estimates of the number of nodes, and is robust against limited changes in the size of the network. Our results have applications in peer-to-peer networks and replicated databases. Preliminary version published in the Proceedings of the 27th Annual ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC 2008)

Distributed Intrusion Detection for Secure Consensus Computations

This work focuses on trustworthy computation systems and proposes a novel intrusion detection scheme for consensus networks with misbehaving nodes. This prototypical control problem is relevant in network security applications. The objective is for each node to detect and isolate the misbehaving nodes using only the information flow adopted by standard averaging protocols. We focus mainly on the single misbehaving node problem. Our technical approach is based on the theory of Unknown Input Observability. First, we give necessary and sufficient conditions for the misbehavior to be observable and for the identity of the faulty node to be detectable. Second, we design a distributed unknown input estimator, and we characterize its convergence rate in the "equal-neighbor" model and in the general case. Third and finally, we propose a complete detection and isolation scheme and provide some remarks on the filter convergence time. We also analyze the multiple misbehaving nodes problem, and we describe an algorithm to deal with it. We conclude the document with the numerical study of a consensus problem, of a robot deployment problem, and of an averaging problem

On coding labeled trees

Trees are probably the most studied class of graphs in Computer Science. In this thesis we study bijective codes that represent labeled trees by means of string of node labels. We contribute to the understanding of their algorithmic tractability, their properties, and their applications. The thesis is divided into two parts. In the first part we focus on two types of tree codes, namely Prufer-like codes and Transformation codes. We study optimal encoding and decoding algorithms, both in a sequential and in a parallel setting. We propose a unified approach that works for all Prufer-like codes and a more generic scheme based on the transformation of a tree into a functional digraph suitable for all bijective codes. Our results in this area close a variety of open problems. We also consider possible applications of tree encodings, discussing how to exploit these codes in Genetic Algorithms and in the generation of random trees. Moreover, we introduce a modified version of a known code that, in Genetic Algorithms, outperform all the other known codes. In the second part of the thesis we focus on two possible generalizations of our work. We first take into account the classes of k-trees and k-arch graphs (both superclasses of trees): we study bijective codes for this classes of graphs and their algorithmic feasibility. Then, we shift our attention to Informative Labeling Schemes. In this context labels are no longer considered as simple unique node identifiers, they rather convey information useful to achieve efficient computations on the tree. We exploit this idea to design a concurrent data structure for the lowest common ancestor problem on dynamic trees. We also present an experimental comparison between our labeling scheme and the one proposed by Peleg for static trees

Topology and congestion invariant in global internet-scale networks

PhDInfrastructures like telecommunication systems, power transmission grids and the Internet are complex networks that are vulnerable to catastrophic failure. A common mechanism behind this kind of failure is avalanche-like breakdown of the network's components. If a component fails due to overload, its load will be redistributed, causing other components to overload and fail. This failure can propagate throughout the entire network. From studies of catastrophic failures in di erent technological networks, the consensus is that the occurrence of a catastrophe is due to the interaction between the connectivity and the dynamical behaviour of the networks' elements. The research in this thesis focuses particularly on packet-oriented networks. In these networks the tra c (dynamics) and the topology (connectivity) are coupled by the routing mechanisms. The interactions between the network's topology and its tra c are complex as they depend on many parameters, e.g. Quality of Service, congestion management (queuing), link bandwidth, link delay, and types of tra c. It is not straightforward to predict whether a network will fail catastrophically or not. Furthermore, even if considering a very simpli ed version of packet networks, there are still fundamental questions about catastrophic behaviour that have not been studied, such as: will a network become unstable and fail catastrophically as its size increases; do catastrophic networks have speci c connectivity properties? One of the main di culties when studying these questions is that, in general, we do not know in advance if a network is going to fail catastrophically. In this thesis we study how to build catastrophic 5 networks. The motivation behind the research is that once we have constructed networks that will fail catastrophically then we can study its behaviour before the catastrophe occurs, for example the dynamical behaviour of the nodes before an imminent catastrophe. Our theoretical and algorithmic approach is based on the observation that for many simple networks there is a topology-tra c invariant for the onset of congestion. We have extended this approach to consider cascading congestion. We have developed two methods to construct catastrophes. The main results in this thesis are that there is a family of catastrophic networks that have a scale invariant; hence at the break point it is possible to predict the behaviour of large networks by studying a much smaller network. The results also suggest that if the tra c on a network increases exponentially, then there is a maximum size that a network can have, after that the network will always fail catastrophically. To verify if catastrophic networks built using our algorithmic approach can re ect real situations, we evaluated the performance of a small catastrophic network. By building the scenario using open source network simulation software OMNet++, we were able to simulate a router network using the Open Shortest Path First routing protocol and carrying User Datagram Protocol tra c. Our results show that this kind of networks can collapse as a cascade of failures. Furthermore, recently the failure of Google Mail routers [1] con rms this kind of catastrophic failure does occur in real situations

USER AUTHENTICATION ACROSS DEVICES, MODALITIES AND REPRESENTATION: BEHAVIORAL BIOMETRIC METHODS

Biometrics eliminate the need for a person to remember and reproduce complex secretive information or carry additional hardware in order to authenticate oneself. Behavioral biometrics is a branch of biometrics that focuses on using a person’s behavior or way of doing a task as means of authentication. These tasks can be any common, day to day tasks like walking, sleeping, talking, typing and so on. As interactions with computers and other smart-devices like phones and tablets have become an essential part of modern life, a person’s style of interaction with them can be used as a powerful means of behavioral biometrics. In this dissertation, we present insights from the analysis of our proposed set of contextsensitive or word-specific keystroke features on desktop, tablet and phone. We show that the conventional features are not highly discriminatory on desktops and are only marginally better on hand-held devices for user identification. By using information of the context, our proposed word-specific features offer superior discrimination among users on all devices. Classifiers, built using our proposed features, perform user identification with high accuracies in range of 90% to 97%, average precision and recall values of 0.914 and 0.901 respectively. Analysis of the word-based impact factors reveal that four or five character words, words with about 50% vowels, and those that are ranked higher on the frequency lists might give better results for the extraction and use of the proposed features for user identification. We also examine a large umbrella of behavioral biometric data such as; keystroke latencies, gait and swipe data on desktop, phone and tablet for the assumption of an underlying normal distribution, which is common in many research works. Using suitable nonparametric normality tests (Lilliefors test and Shapiro-Wilk test) we show that a majority of the features from all activities and all devices, do not follow a normal distribution. In most cases less than 25% of the samples that were tested had p values \u3e 0.05. We discuss alternate solutions to address the non-normality in behavioral biometric data. Openly available datasets did not provide the wide range of modalities and activities required for our research. Therefore, we have collected and shared an open access, large benchmark dataset for behavioral biometrics on IEEEDataport. We describe the collection and analysis of our Syracuse University and Assured Information Security - Behavioral Biometrics Multi-device and multi -Activity data from Same users (SU-AIS BB-MAS) Dataset. Which is an open access dataset on IEEEdataport, with data from 117 subjects for typing (both fixed and free text), gait (walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. The dataset consists a total of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; 1.7 million datapoints for swipes and is listed as one of the most popular datasets on the portal (through IEEE emails to all members on 05/13/2020 and 07/21/2020). We also show that keystroke dynamics (KD) on a desktop can be used to classify the type of activity, either benign or adversarial, that a text sample originates from. We show the inefficiencies of popular temporal features for this task. With our proposed set of 14 features we achieve high accuracies (93% to 97%) and low Type 1 and Type 2 errors (3% to 8%) in classifying text samples of different sizes. We also present exploratory research in (a) authenticating users through musical notes generated by mapping their keystroke latencies to music and (b) authenticating users through the relationship between their keystroke latencies on multiple devices