Topological evolution of networks : case studies in the US airlines and language Wikipedias

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 195-198).This thesis examines the topology of engineering systems and how that topology changes over time. Topology refers to the relative arrangement and connectivity of the elements of a system. We review network theory relevant to topological evolution and use graph-theoretical methods to analyze real systems, represented as networks. Using existing graph generative models, we develop a profile of canonical graphs and tools to compare a real network to that profile. The developed metrics are used to track topology changes over the history of real networks. This theoretical work is applied to two case studies. The first discusses the US airline industry in terms of routes. We study various airlines and segments of the industry statistically and find commonly occurring patterns. We show that there are topology transitions in the history of airlines in the period 1990-2007. Most airline networks have similar topology and historical patterns, with the exception of Southwest Airlines. We show mathematically that Southwest's topology is different. We propose two heuristic growth models, one featuring hub-seeding derived from the underlying patterns of evolution of JetBlue Airways and one featuring local interconnectedness, derived from the patterns of growth of Southwest. The two models match the topologies of these airlines better than canonical models over time. Results suggest that Southwest is becoming more centralized, closer to the hub-spoke topologies of other airlines. Our second case study discusses the growth of language Wikipedia networks, where nodes are articles and hyperlinks are the connections between them. These knowledge networks are subject to different constraints than air transportation systems. The topology of these networks and their growth principles are completely different. Most Wikipedias studied grow by coalescence, with multiple disconnected thematic clusters of pages growing separately and over time, converging to a giant connected component via weak links. These topologies start out as simple trees, and coalesce into sparse hierarchical structures with random interlinking. One striking exception is the history of the Chinese Wikipedia, which grows fully connected from its inception. We discuss these patterns of growth comparatively across Wikipedias, and in general, compared to airline networks. Our work suggests that complex engineering systems are hybrids of pure canonical forms and that they undergo distinct phase transitions during their evolution. We find commonality among systems and uncover important differences by learning from the exceptions.by Gergana Assenova Bounova.Ph.D

Exploring the Modularity and Structure of Robots Evolved in Multiple Environments

Traditional techniques for the design of robots require human engineers to plan every aspect of the system, from body to controller. In contrast, the field of evolu- tionary robotics uses evolutionary algorithms to create optimized morphologies and neural controllers with minimal human intervention. In order to expand the capability of an evolved agent, it must be exposed to a variety of conditions and environments. This thesis investigates the design and benefits of virtual robots which can reflect the structure and modularity in the world around them. I show that when a robot’s morphology and controller enable it to perceive each environment as a collection of independent components, rather than a monolithic entity, evolution only needs to optimize on a subset of environments in order to maintain performance in the overall larger environmental space. I explore previously unused methods in evolutionary robotics to aid in the evolution of modularity, including using morphological and neurological cost. I utilize a tree morphology which makes my results generalizable to other mor- phologies while also allowing in depth theoretical analysis about the properties rel- evant to modularity in embodied agents. In order to better frame the question of modularity in an embodied context, I provide novel definitions of morphological and neurological modularity as well as create the sub-goal interference metric which mea- sures how much independence a robot exhibits with regards to environmental stimu- lus. My work extends beyond evolutionary robotics and can be applied to the opti- mization of embodied systems in general as well as provides insight into the evolution of form in biological organisms

Effective Task Transfer Through Indirect Encoding

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain

Multiagent Learning Through Indirect Encoding

Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding

Evolution and regulatory logic of an enhancer underlying a novel pigmentation pattern in Drosophila wings

Novel morphological traits originate largely from the novel expression patterns of genes controlled by enhancers during development. Enhancers bind and integrate the spatial-temporal activity of transcription factors, and their combinatorial interplay determines the time, location and levels of transcriptional output. New enhancers can arise through enhancer co-option by reusing some of the regulatory information from a preexisting enhancer. While enhancer co-option is thought to be a fast and likely way to evolve new enhancers, its genetic and molecular mechanisms still remain elusive. In this context, this thesis investigates the genetic origin of a novel enhancer and the regulatory logic underlying its function. I used the spot enhancer of the gene yellow as a model, which underlies the evolution of a morphological trait, the wing spot in Drosophila biarmipes. I sought to understand how the novel spot enhancer has evolved and what regulatory logic governs its function. Specifically: In the first chapter, I examined the evolutionary mechanism of spot enhancer in the context of the preexisting wing blade enhancer. By revisiting the entire D. biarmipes yellow 5´ region with a comprehensive and quantitative method, I mapped the full activities of the novel spot and preexisting wing blade enhancers to a much larger region (3.5 kb) than previously described (1.1 kb together). Within the region, the regulatory information necessary and sufficient for the spot activity was inseparable from, and extensively overlapping with the wing blade activity. Further dissection of the shared core region revealed a pleiotropic binding site that contributed to both activities by regulating the local chromatin accessibility. I therefore confirmed that the novel spot activity originates from the co-option of the preexisting wing blade activity. The pleiotropic site for chromatin accessibility suggests a possible model where a new enhancer could evolve by co-option of chromatin accessibility input from the ancestral element, and that might facilitate the emergence and diversification of morphological traits. In the second chapter, I investigated how the various aspects of regulatory information encoded in the spot enhancer sequences influenced its activity. Through introducing systematic mutations along the enhancer sequences and implementing a quantitative framework, the spatial activities on the wing of all the mutant enhancers were measured. The analysis showed an unexpected density of regulatory information within the spot enhancer. Moreover, it reveals an unanticipated regulatory logic underlying the activity of this enhancer and how it reads the wing trans-regulatory landscape to encode a spatial pattern. The gene yellow is required for black pigment production and its expression in late pupal stage prefigures the adult wing spot pigmentation pattern. Therefore, understanding the dynamics of yellow expression is essential to elucidate the process of yellow enhancer regulation as well as pigment formation during development. Chapter three investigates the process of pigment formation in space and time using the pigment gene yellow in D. melanogaster. Firstly, a fluorescent protein-tagged yellow allele was generated, then the dynamics of yellow expression and cellular localization in relationship to the process of pigment formation was examined during development. It was found that yellow is expressed in a few neurons in the brain and the ventral nerve chord from the second larval instar to adult stage, indicating a neuro-developmental function of yellow. In addition, the results mainly showed how yellow expression in the adult cuticle is determined by regulated developmental processes affecting the body color, and suggested a structural role of Yellow in the establishment of pigmentation patterns

MCMC methods: graph samplers, invariance tests and epidemic models

Markov Chain Monte Carlo (MCMC) techniques are used ubiquitously for simulation-based inference. This thesis provides novel contributions to MCMC methods and their application to graph sampling and epidemic modeling. The first topic considered is that of sampling graphs conditional on a set of prescribed statistics, which is a difficult problem arising naturally in many fields: sociology (Holland and Leinhardt, 1981), psychology (Connor and Simberloff, 1979), categorical data analysis (Agresti, 1992) and finance (Squartini et al., 2018, Gandy and Veraart, 2019) being examples. Bespoke MCMC samplers are proposed for this setting. The second major topic addressed is that of modeling the dynamics of infectious diseases, where MCMC is leveraged as the general inference engine. The first part of this thesis addresses important problems such as the uniform sampling of graphs with given degree sequences, and weighted graphs with given strength sequences. These distributions are frequently used for exact tests on social networks and two-way contingency tables. Another application is quantifying the statistical significance of patterns observed in real networks. This is crucial for understanding whether such patterns indicate the presence of interesting network phenomena, or whether they simply result from less interesting processes, such as nodal-heterogeneity. The MCMC samplers developed in the course of this research are complex, and there is great scope for conceptual, analytic, and implementation errors. This motivates a chapter that develops novel tests for detecting errors in MCMC implementations. The tests introduced are unique in being exact, which allows us to keep the false rejection probability arbitrarily low. Rather than develop bespoke samplers, as in the first part of the thesis, the second part leverages a standard MCMC framework Stan (Stan Development Team, 2018) as the workhorse for fitting state-of-the-art epidemic models. We present a general framework for semi-mechanistic Bayesian modeling of infectious diseases using renewal processes. The term semi-mechanistic relates to statistical estimation within some constrained mechanism. This research was motivated by the ongoing SARS-COV-2 pandemic, and variants of the model have been used in specific analyses of Covid-19. We present epidemia, an R package allowing researchers to leverage the epidemic models. A key goal of this work is to demonstrate that MCMC, and in particular, Stan’s No-U-Turn (Hoffman and Gelman, 2014) sampler, can be routinely employed to fit a large-class of epidemic models. A second goal is to make the models accessible to the general research community, through epidemia.Open Acces

Active module identification in biological networks

This thesis addresses the problem of active module identification in biological networks. Active module identification is a research topic in network biology that aims to identify regions in network showing striking changes in activity. It is often associated with a given cellular response and expected to reveal dynamic and process-specific information. The key research questions for this thesis are the practical formulations of active module identification problem,the design of effective, efficient and robust algorithms to identify active modules, and the right way to interpret identified active module. This thesis contributes by proposing three different algorithm frameworks to address the research question from three different aspects. It first explores an integrated approach of combining both gene differential expression and differential correlation, formulates it as a multi-objective problem, and solves it on both simulated data and real world data. Then the thesis investigates a novel approach that brings in prior knowledge of biological process, and balances between pure data-driven search and prior information guidance. Finally, the thesis presents a brand new framework of identifying active module and topological communities simultaneously using evolutionary multitasking, accompanied with a series of task-specific algorithm designs and improvements, and provides a new way of integrating topological information to help the interpretation of active module

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender