This research presents a structural model of the effect of the organization of military units upon their capability. This research is oriented towards a more complete understanding of military capability and policy decisions about the structure and development of military forces. We identify the types of national and military policy decisions that claims of military capability inform, and find that there are five distinct types of capability claims relevant to military policy. We show how these types of capability claims are logically related to each other, but have different premises, predicates, and standards of proof. We find that one of these types of claims, General Organization Capability Claims, ties together the various military policy decisions. The remainder of this research shows how these capability claims can be formally structured based on military doctrine and structurally evaluated using a network-science based model. The interaction between the structural elements of a military organization (personnel, materiel, and information) and the things it is supposed to do (military tasks) can be represented and analyzed with network science methods, and represents a type of general organization capability claim. We present a method for representing policy decisions about unit structure and tactical doctrine. We then develop two versions of a structural model of capability–one that links the individual elements of an organization to the tasks it performs; another that considers the capacity of a set of organizations to meet a set of requirements. We show that network statistics of organizations represented off of authoritative, rather than observational, data are still consistent with network science findings but require interpretation. We also show how alternate methods of aggregating organizations can expand the utility of the capability measurement. This research presents five new contributions to the fields of military policy analysis and network science–(1) a taxonomy of military capability claims, (2) a meta-network model of doctrinal organization and task data, (3) a structural model of organization capability, (4) a structural model of organization capacity, and (5) a network-based method integer programming method.

Since 2008, more than half of the world population lives in cities. To cope with this rapid urbanization in a sustainable manner, transit systems all around the world are likely to grow. By studying 33 networks in the world, this thesis identifies the properties and effects of metro network designs by using a graph theory approach. After the literature review, a new methodology was introduced to translate networks into graphs; it notably accounts for various transit specificities (e.g., presence of lines). Metro networks were then characterised according to their State, Form, and Structure; where State relates to the development phase of metros; Form investigates the link between metros and the built environment; Structure examines the intrinsic properties of metros, by notably looking at their connectivity. Subsequently, the complexity and robustness of metros were studied; metros were found to possess scale-free and small-world features although showing atypical topologies; robustness emphasizes on the presence of alternative paths. Three network design indicators (coverage, directness and connectivity) were then related to ridership (annual boardings per capita), and positive relations were observed, which suggests that network design plays an important role in their success. Finally, these concepts were applied to the Toronto metro plans announced by the Toronto regional transportation authority, Metrolinx; it was found that the grid-pattern nature of the plans could hinder the success of the metro; seven possible improvements were suggested. Overall, the topology of metro networks can play a key role in their success. The concepts presented here can particularly be useful to transit planners; they should also be used along with conventional planning techniques. New transit projects could benefit greatly from an analysis of their network designs, which in turn may play a relevant role in the global endeavour for sustainability.

abstract: Our ability to understand networks is important to many applications, from the analysis and modeling of biological networks to analyzing social networks. Unveiling network dynamics allows us to make predictions and decisions. Moreover, network dynamics models have inspired new ideas for computational methods involving multi-agent cooperation, offering effective solutions for optimization tasks. This dissertation presents new theoretical results on network inference and multi-agent optimization, split into two parts - The first part deals with modeling and identification of network dynamics. I study two types of network dynamics arising from social and gene networks. Based on the network dynamics, the proposed network identification method works like a `network RADAR', meaning that interaction strengths between agents are inferred by injecting `signal' into the network and observing the resultant reverberation. In social networks, this is accomplished by stubborn agents whose opinions do not change throughout a discussion. In gene networks, genes are suppressed to create desired perturbations. The steady-states under these perturbations are characterized. In contrast to the common assumption of full rank input, I take a laxer assumption where low-rank input is used, to better model the empirical network data. Importantly, a network is proven to be identifiable from low rank data of rank that grows proportional to the network's sparsity. The proposed method is applied to synthetic and empirical data, and is shown to offer superior performance compared to prior work. The second part is concerned with algorithms on networks. I develop three consensus-based algorithms for multi-agent optimization. The first method is a decentralized Frank-Wolfe (DeFW) algorithm. The main advantage of DeFW lies on its projection-free nature, where we can replace the costly projection step in traditional algorithms by a low-cost linear optimization step. I prove the convergence rates of DeFW for convex and non-convex problems. I also develop two consensus-based alternating optimization algorithms --- one for least square problems and one for non-convex problems. These algorithms exploit the problem structure for faster convergence and their efficacy is demonstrated by numerical simulations. I conclude this dissertation by describing future research directions. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Real-world networks are dynamic in that both the state of the network components and the structure of the network (topology) change over time. Most studies regarding network evolution consider either one or the other of these types of network processes. Here we consider the interplay of the two, specifically, we consider how changes in network structure effect the dynamics of the network components. To model the growth of a network we use the specialization model known to produce many of the well-known features observed in real-world networks. We show that specialization results in a nontrivial equitable partition of the network where the elements of the partition form clusters that have synchronous dynamics. In particular, we show that these synchronizing clusters inherit their ability to either locally or globally synchronize from the subnetwork from which they are specialized. Thus, network specialization allows us to model how dynamics and structure can co-evolve in real-world systems.

No description available.

A step towards renewable energy is the smart grid, i.e. a power grid that is capable of smart distribution and load managing. This requires the participating components, nodes, to be able to communicate in a reliable fashion. The work of this project was to take on a mathematical approach by using the study of network science, which resulted in the design of a communication network topology for the power grid. The main focus of this undertaking was to quantify how a node in the power grid reacts to change in power flow, which was denoted as its sensitivity. By using Matpower, a tool programmed in Matlab, and our developed method, a communication network was able to be designed using a threshold algorithm. This method worked for the small scale power grids considered in this project, but a different approach might be needed for larger grids.

Grasslands cover 40% of the earth’s land area and provide numerous valuable ecosystem services. However, climate change, global land use change and increasing intensive anthropogenic interventions make grasslands to one of the most endangered ecosystem types in the world. Effective protection in the future requires a fundamental understanding of the dynamics of grasslands and their major drivers. Field experiments have been conducted for impact analyses, for example, with different management intensities, plant community composition and altered climatic conditions. Complementary, ecological models allow to extend the analysis to long-term effects of changes as well as to a deeper understanding of the underlying ecological processes. In this thesis, an individual-based grassland model and network science were applied to understand the community structure and dynamics emerging from individual plant interactions – in relation to plant traits, ecological processes, environmental and anthropogenic impacts, and the small-scale spatial distribution of plants. In the first study, an individual-based process-oriented grassland model was parameterized to simulate field data of a local biodiversity experiment using the concept of plant functional types. The influence of various functional plant traits and ecological processes on grassland productivity and functional composition were analyzed. Different functional plant traits showed partly contrasting effects on plant growth. With regard to the modeled ecological processes, competition for space between plants affected grassland productivity more than shading of plants. In the second study, the parameterized grassland model was used to analyze the impact of functional diversity, mowing frequency and air temperature on ecological processes that lead to changes in grassland productivity. The model reproduced the increase of biomass yields with functional diversity as observed in the field experiment. Modeled plant competition for space showed to be the dominant process and was responsible for an increase in biomass yields in more frequently mown grasslands. In the third study, an approach to generate a regionally transferable parameterization of the grassland model is presented. The impact of management, environment and climate change on productivity and functional composition of grasslands was analyzed within a German-wide scenario analysis. Management intensity had more influence on grassland productivity than environmental factors and correlations of productivity with environmental factors become stronger in less managed grasslands. Climate change showed to have only a minor influence on simulated vegetation attributes. In the fourth study, network science was applied to forest megaplots to quantify the spatial neighborhood structure of species-rich ecosystems. Networks at the individual-tree and tree-species levels revealed similar structures at three investigated forest sites. Tropical tree species coexisted in small-scale networks and only up to 51% of all possible connections between species pairs were realized. A null community analysis showed that details on the tree position and tree size have no major influence on the network structures identified. In summary, this thesis presents the development of advanced methods and analysis tools as well as their application to vegetation ecosystems with high diversity. Thereby, complex structures and dynamics of ecological systems could be systematically explored by combining ecological models with extensive field measurements.

Many real life complex networks consists of a tremendous amount of nodes and edges which make them difficult to extract and analyze. This thesis aims to examine what network prop- erties that can be deduced when considering small samples of a complex network and how well they correspond to the characteristics of the complete network. This is of importance as sampling will most likely be the de facto method when analyzing complex networks in the future. The study examine the scale-free property, the small-world property and the com- munity structure of the network spanned by the KTH domain. The method consisted of gathering data about the network through sampling it in a breadth-first manner using a web crawler. The samples was then compared with respect to each property. The results was that good approximations of the scale-free property could be made from small samples of the KTH network. However, no good approximation could be made about the small-world property using the sampling technique. Good approximations about a node’s community affiliation could be observed. However, general conclusions of the com- plete network’s community structures could not be made. To summarize, the result indi- cate that small samples can be used to approximate some properties of the complete KTH network. However, to determine if the result is true for the general case more research is necessary. / Komplexa nätverk i vår omvärld består av ett stort antal hörn och kanter vilket gör dem svå- ra att extrahera och analysera. Denna rapport undersöker vilka nätverksegenskaper som kan härledas vid undersökningen av små stickprov av ett nätverk och hur bra dessa representerar egenskaperna hos det fullständiga nätverket. Detta är av betydelse eftersom användandet av små stickprov kommer troligtvis att vara standarden vid undersökningar av nätverk i framtiden. Denna studie undersökte scale-free egenskapen, small-world egenskapen och community strukturen för nätverket som omfattas av KTH domaänen. Metoden innefattade att samla in data om nätverket genom stickprov baserat på en bredden-först sökning. Detta realiserades genom att använda en sökrobot. Sedan jämfördes de olika stickproven med avseende på de olika nätverksegenskaperna. Resultetat visade att nätverkets scale-free egenskap kunde approximaeras med små stickprov. Däremot var det inte möjligt att approximera nätverkets small-world egenskap genom användet av den givna stickprovsmetoden. Goda approximationer observerades för att avgöra ett hörns community tillhörighet men den allmäna community strukturen kunde inte approximeras. Sammanfattningsvis visade resultatet att stickprov kan användas för att approximera vissa egenskaper hos det fullständiga KTH nätverket men att mer forskning krävs för att avgöra om resultaten kan generaliseras.

No description available.

Socially coupled systems are comprised of inter-dependent social, organizational, economic, infrastructure and physical networks. Today's urban regions serve as an excellent example of such systems. People and institutions confront the implications of the increasing scale of information becoming available due to a combination of advances in pervasive computing, data acquisition systems as well as high performance computing. Integrated modeling and decision making environments are necessary to support planning, analysis and counter factual experiments to study these complex systems. Here, we describe SIMFRASTRUCTURE -- a computational infrastructure that supports high performance computing oriented decision and analytics environments to study socially coupled systems. Simfrastructure provides a middleware with multiplexing mechanism by which modeling environments with simple and intuitive user-interfaces can be plugged in as front-end systems, and high-end computing resources -- such as clusters, grids and clouds -- can be plugged in as back-end systems for execution. This makes several key aspects of simulation systems such as the computational complexity, data management and resource management and allocation completely transparent to the users. The decoupling of user interfaces, data repository and computational resources from simulation execution allows users to run simulations and access the results asynchronously and enables them to add new datasets and simulation models dynamically.  Simfrastructure enables implementation of a simple yet powerful modeling environment with built-in analytics-as-aservice platform, which provides seamless access to high end computational resources, through an intuitive interface for studying socially coupled systems. We illustrate the applicability of Simfrastructure in the context of an integrated modeling environment to study public health epidemiology and network science. / Master of Science