The embeddedness of organizational performance: multiple membership multiple classification models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical mer- its of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and par- tially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteris- tics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covari- ates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures

Cohesion, equivalence, and similarity of behavior: a theoretical and empirical assessment

Network analysts have debated the extent to which cohesion versus structural equivalence serves as a source of similar behavior among actors. More recently, role equivalence has emerged as an alternative to structural equivalence. Using data on the contribution patterns of corporate political action committees, I examine the effect of various indicators of cohesion, structural equivalence, and role equivalence on the extent to which firms behave similarly. Although various operationalizations of all three concepts are correlated with similar behavior, the most consistent predictor is the joint prominence of two firms in the network. I argue that this common location in central positions is a form of role equivalence, but one that is distinct from conventional definitions of the concept. I then suggest a distinction between what I term `central' and `peripheral' role equivalence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30604/1/0000241.pd

Graph inference and graph matching problems : tehory and algorithms

Tribunal: Alex Bronstein (Tel Aviv University), Marcelo Lanzilotta (Universidad de la República), Gonzalo Mateos (University of Rochester), Gadiel Seroussi (Universidad de la República)Almost every field has some problems related with graphs or networks. From natural examples in physics and mathematics, to applications in medicine and signal processing, graphs are either a very powerful tool, or a very rich object of interest. In this thesis we address two classes of graph-related problems. First, we focus on graph-inference problems, consisting in the estimation of a graph or network from a dataset. In this part of the manuscript, we modify the existing formulations of the inference problem to incorporate prior topological information of the graph, and to jointly infer several graphs in a collaborative way. We apply these techniques to infer genetic regulation networks, brain connectivity patterns, and economyrelated networks. We also present a new problem, which consists of the estimation of mobility patterns from highly asynchronous and incomplete data. We give a first formulation of the problem with its corresponding optimization, and present results for airplane routes and New York taxis mobility patterns. The second class consists of the so-called graph matching problems. In this type of problems two graphs are given, and the objective is to find the best alignment between them. This problem is of great interest both from an algorithmic and theoretical point of view, besides the very important applications. Its interest and difficulty lie in the combinatorial nature of the problem: the cost of seeking among all the possible permutations grows exponentially with the number of nodes, and hence becomes intractable even for small graphs. First, we focus on the algorithmic aspect of the graph matching problem. We present two methods based on relaxations of the discrete optimization problem. The first one is inspired in ideas from the sparse modeling community, and the second one is based on a theorem presented in this manuscript. The importance of these methods is illustrated with several applications. Finally, we address some theoretical aspects about graph matching and other related problems. The main question tackled in the last chapter is the following: when do the graph matching problem and its convex relaxation have the same solution? A probabilistic approach is first given, showing that, asymptotically, the most common convex relaxation fails, while a non-convex relaxation succeeds with probability one if the graphs to be matched are correlated enough, showing a phase-transition type of behavior. On the other hand, a deterministic approach is presented, stating conditions on the eigenvectors and eigenvalues of the adjacency matrix for guaranteeing the correctness of the convex relaxation solution. Other results and conjectures relating the spectrum and symmetry of a graph are presented as well.En prácticamente todos los campos hay problemas relacionados con grafos o redes. Desde los ejemplos más naturales en física y matem ática, hasta aplicaciones en medicina y procesamiento de señales, los grafos son una herramienta muy poderosa, o un objeto de estudio muy rico e interesante. En esta tesis atacamos dos clases de problemas relacionados con grafos. Primero, nos enfocamos en problemas de inferencia de grafos, que consisten en estimar un grafo o red a partir de cierto conjunto de datos. En esta parte del manuscrito, modificamos las formulaciones existentes de inferencia de grafos para incorporar información topológica previamente conocida sobre el grafo, y para inferir de manera conjunta varios grafos, en un modo colaborativo. Aplicamos estas técnicas para inferir redes de regulaci ón genética, patrones de conectividad cerebral, y redes relacionadas con el mercado accionario. También presentamos un nuevo problema, que consiste en la estimación de patrones de movimiento a partir de un conjunto de datos incompleto, y altamente asíncrono. Mostramos primero una formulación del problema con su correspondiente optimización, y presentamos resultados para rutas de aviones en Estados Unidos, y patrones de movilidad de taxis en New York. La segunda clase consiste en los llamados graph matching problems (problemas de apareamiento de grafos). En este tipo de problemas, dos grafos son dados, y el objetivo es encontrar el mejor alineamiento entre ellos. Este problema es de gran interés tanto desde un punto de vista algorítmico como teórico, además de las importantes aplicaciones que tiene. El interés y la dificultad de este problema tienen raíz en la naturaleza combinatoriadel mismo: el costo de buscar entre todas las permutaciones posibles crece exponencialmente con el número de nodos, y por lo tanto se vuelve rápidamente intratable, incluso para grafos chicos. Primero, nos enfocamos en el aspecto algorítmico del problema de graph match- ing. Presentamos dos métodos basados en relajaciones del problema de optimización discreta. El primero de ellos está inspirado en ideas de la comunidad de sparse modeling, y el segundo est a basado en un teorema presentado en este manuscritp. La importancia de estos m etodos es ilustrada con varias aplicaciones a lo largo del capítulo. Finalmente, atacamos algunos aspectos teóricos sobre graph matching y otros problemas relacionados. La pregunta principal que se encara en el último capítulo es la siguiente: >cuáando el problema de graph matching y su relajación convexa tienen la misma solucióon? Primero damos un enfoque probabilístico mostrando que, asintoticamente, la relajación convexa más común falla, mientras que una relajación no convexa es capaz de resolver el problema con probabilidad uno, siempre y cuando los grafos originales estén lo sufi cientemente correlacionados, mostrando un comportamiento del estilo de transicióon de fases. Por otro lado, un enfoque determinístico es también presentado, estableciendo condiciones sobre los valores y vectores propios de las matrices de adjacencia de los grafos, que garantizan que el problema de graph matching y su relajacióon convexa tienen la misma solución. Otros resultados y conjeturas relacionando el espectro y la simetría de un grafo son presentados también en este capítulo

FieldPlacer - A flexible, fast and unconstrained force-directed placement method for heterogeneous reconfigurable logic architectures

The field of placement methods for components of integrated circuits, especially in the domain of reconfigurable chip architectures, is mainly dominated by a handful of concepts. While some of these are easy to apply but difficult to adapt to new situations, others are more flexible but rather complex to realize. This work presents the FieldPlacer framework, a flexible, fast and unconstrained force-directed placement method for heterogeneous reconfigurable logic architectures, in particular for the ever important heterogeneous FPGAs. In contrast to many other force-directed placers, this approach is called ‘unconstrained’ as it does not require a priori fixed logic elements in order to calculate a force equilibrium as the solution to a system of equations. Instead, it is based on a free spring embedder simulation of a graph representation which includes all logic block types of a design simultaneously. The FieldPlacer framework offers a huge amount of flexibility in applying different distance norms (e. g., the Manhattan distance) for the force-directed layout and aims at creating adapted layouts for various objective functions, e. g., highest performance or improved routability. Depending on the individual situation, a runtime-quality trade-off can be considered to either produce a decent placement in a very short time or to generate an exceptionally good placement, which takes longer. An extensive comparison with the latest simulated annealing placement method from the well-known Versatile Place and Route (VPR) framework shows that the FieldPlacer approach can create placements of comparable quality much faster than VPR or, alternatively, generate better placements in the same time. The flexibility in defining arbitrary objective functions and the intuitive adaptability of the method, which, among others, includes different concepts from the field of graph drawing, should facilitate further developments with this framework, e. g., for new upcoming optimization targets like the energy consumption of an implemented design

Explicit Building Block Multiobjective Evolutionary Computation: Methods and Applications

This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of a more robust algorithm where MOEA concepts are implicitly employed. Testing shows that the new MOEA can be more effective and efficient than previous state-of-the-art explicit BB MOEAs for selected test suite multiobjective optimization problems (MOPs) and U.S. Air Force applications. Other contributions include the extension of explicit BB definitions to clarify the meanings for good single and multiobjective BBs. A new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in finding Pareto front vectors while tracking the size of the BBs. The visualization technique is the result of a BB tracing mechanism integrated into the new MOEA that enables one to determine the required BB sizes and assign an approximation epistasis level for solving a particular problem. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classifier type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to test suite, deception, bioinformatics, unmanned vehicle flight pattern, and digital symbol set design MOPs

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Joint Optimization of Fidelity and Commensurability for Manifold Alignment and Graph Matching

In this thesis, we investigate how to perform inference in settings in which the data consist of different modalities or views. For effective learning utilizing the information available, data fusion that considers all views of these multiview data settings is needed. We also require dimensionality reduction to address the problems associated with high dimensionality, or “the curse of dimensionality.” We are interested in the type of information that is available in the multiview data that is essential for the inference task. We also seek to determine the principles to be used throughout the dimensionality reduction and data fusion steps to provide acceptable task performance. Our research focuses on exploring how these queries and their solutions are relevant to particular data problems of interest