Domination problems in social networks

The thesis focuses on domination problems in social networks. Domination problems are one of the classical types of problems in computer science. Domination problems are fundamental and widely studied problems in algorithms and complexity theory. They have been extensively studied and adopted in many real-life applications. In general, a set D of vertices of a simple (no loops or multiple edges), undirected graph G = (V,E) is called dominating if each vertex in V −D is adjacent to some vertex in D. The computational problem of computing a dominating set of minimum size is known as “the dominating set problem”. The dominating set problem is NP-hard in general graphs. A social network - the graph of relationships and interactions within a group of individuals - plays a fundamental role as a medium for the spread of information, ideas, and influence among its members. In a social network, people, who have problems such as drinking, smoking and drug use related issues, can have both positive and negative impact on each other and a person can take and move among different roles since they are affected by their peers. As an example, positive impacts of intervention and education programs on a properly selected set of initial individuals can diffuse widely into society via various social contacts: face to face, phone calls, email, social networks and so on. Exploiting the relationships and influences among individuals in social networks might offer considerable benefit to both the economy and society. In order to deal with social problems, the positive influence dominating set (PIDS) is a typical one to help people to alleviate these social problems. However, existing PIDS algorithms are usually greedy and finding approximation solutions that are inefficient for the growing social networks. By now these proposed algorithms can deal with social problems only in undirected social networks with uniform weight value. To overcome the shortcomings of the existing PIDS model, a novel domination model namely weight positive influence dominating set (WPIDS) is presented. A main contribution of the thesis is that the proposed WPIDS model can be applied in weighted directed social networks. It considers the direction and degree of users’ influence in social networks in which the PIDS model does not. The experimental results have revealed that the WPIDS model is more effective than the PIDS model. At the same time, thanks to the publication of Dijkstra’s pioneering paper, a lot of self-stabilizing algorithms for computing minimal dominating sets have been proposed, such as the self-stabilizing algorithms for minimal single dominating sets and minimal k-dominating sets (MKDS). However, for the MKDS problem, so far there is no self-stabilizing algorithm that works in arbitrary graphs. The proposed algorithms for the MKDS either work for tree graphs or find a minimal 2-dominating set. So, in the thesis, for the MKDS problem, two self-stabilizing algorithms are presented that can operate on general graphs. For the weighted dominating set (WDS) problem, most of the proposed algorithms find approximation solutions to a WDS. For the non-uniform WDS problem, there is no self-stabilizing algorithm for the WDS. In the thesis, self-stabilizing algorithms for the minimal weighted dominating set (MWDS) and minimal positive influence dominating set (MPIDS) are presented when operating in any general network. The worst case convergence time of the two algorithms from any arbitrary initial state are also proved. Finally, in order to reduce cost in an education/intervention programme arising from the PIDS problem, two cooperative cost games about PIDS problem are constructed

Fast and Efficient Classification, Tracking, and Simulation in Wireless Sensor Networks

Wireless sensor networks are composed of large numbers of resource-lean sensors that collect low-level inputs from the physical world. The applications present challenges for programmers. On the one hand, lightweight algorithms are required given the limited capacity of the constituent devices. On the other, the algorithms must be scalable to accommodate large networks. In this thesis, we focus on the design and implementation of fast and lean (yet scalable) algorithms for classification, simulation, and target tracking in the context of wireless sensor networks. We briefly consider each of these challenges in turn. The first challenge is to achieve high precision classification of high-level events in-network using limited computational and energy resources. We present in-network implementations of a Bayesian classifier and a condensed kd-tree classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multi-dimensional Bayesian classifier used to classify sensor data in real-time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies. The second challenge is to achieve high performance parallel simulation of sensor network hardware. This is achieved by reducing the synchronization overhead among distributed simulation processes. Traditional parallel simulation strategies introduce significant synchronization overhead, reducing the simulation speed. We present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. Concretely, we implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently. The third challenge is to efficiently track a moving target in a network. The difficulty again lies in the conflict between the limited resource capacity of typical sensors and the significant processing requirements of typical tracking algorithms. We introduce an in-network object tracking framework for tracking mobile objects using resource-lean sensors. The framework is based on a distributed, dynamically scoped tracking algorithm which adaptively scopes the event detection region based on object speed. A leader node records the samples across an event region (without the aid of time synchronization) and estimates the object\u27s location in situ. To minimize the number of radio transmissions, the location snapshotting rate is also adjusted based on the object speed. In this dissertation, focusing on the above challenges, we present the design, implementation, and evaluation of classification, simulation, and tracking contributions

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

A Radio Link Quality Model and Simulation Framework for Improving the Design of Embedded Wireless Systems

Despite the increasing application of embedded wireless systems, developers face numerous challenges during the design phase of the application life cycle. One of the critical challenges is ensuring performance reliability with respect to radio link quality. Specifically, embedded links experience exaggerated link quality variation, which results in undesirable wireless performance characteristics. Unfortunately, the resulting post-deployment behaviors often necessitate network redeployment. Another challenge is recovering from faults that commonly occur in embedded wireless systems, including node failure and state corruption. Self-stabilizing algorithms can provide recovery in the presence of such faults. These algorithms guarantee the eventual satisfaction of a given state legitimacy predicate regardless of the initial state of the network. Their practical behavior is often different from theoretical analyses. Unfortunately, there is little tool support for facilitating the experimental analysis of self-stabilizing systems. We present two contributions to support the design phase of embedded wireless system development. First, we provide two empirical models that predict radio-link quality within specific deployment environments. These models predict link performance as a function of inter-node distance and radio power level. The models are culled from extensive experimentation in open grass field and dense forest environments using all radio power levels and covering up to the maximum distances reachable by the radio. Second, we provide a simulation framework for simulating self-stabilizing algorithms. The framework provides three feature extensions: (i) fault injection to study algorithm behavior under various fault scenarios, (ii) automated detection of non-stabilizing behavior; and (iii) integration of the link quality models described above. Our contributions aim at avoiding problems that could result in the need for network redeployment

A Dynamic Embedding Model of the Media Landscape

Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports suggest that the rising concentration of media ownership may void this assumption. This observation motivates the study of the impact of ownership on the global media landscape and its influence on the coverage the actual viewer receives. To this end, the selection of reported events has been shown to be informative about the high-level structure of the news ecosystem. However, existing methods only provide a static view into an inherently dynamic system, providing underperforming statistical models and hindering our understanding of the media landscape as a whole. In this work, we present a dynamic embedding method that learns to capture the decision process of individual news sources in their selection of reported events while also enabling the systematic detection of large-scale transformations in the media landscape over prolonged periods of time. In an experiment covering over 580M real-world event mentions, we show our approach to outperform static embedding methods in predictive terms. We demonstrate the potential of the method for news monitoring applications and investigative journalism by shedding light on important changes in programming induced by mergers and acquisitions, policy changes, or network-wide content diffusion. These findings offer evidence of strong content convergence trends inside large broadcasting groups, influencing the news ecosystem in a time of increasing media ownership concentration