Mining and analysis of real-world graphs

Networked systems are everywhere - such as the Internet, social networks, biological networks, transportation networks, power grid networks, etc. They can be very large yet enormously complex. They can contain a lot of information, either open and transparent or under the cover and coded. Such real-world systems can be modeled using graphs and be mined and analyzed through the lens of network analysis. Network analysis can be applied in recognition of frequent patterns among the connected components in a large graph, such as social networks, where visual analysis is almost impossible. Frequent patterns illuminate statistically important subgraphs that are usually small enough to analyze visually. Graph mining has different practical applications in fraud detection, outliers detection, chemical molecules, etc., based on the necessity of extracting and understanding the information yielded. Network analysis can also be used to quantitatively evaluate and improve the resilience of infrastructure networks such as the Internet or power grids. Infrastructure networks directly affect the quality of people\u27s lives. However, a disastrous incident in these networks may lead to a cascading breakdown of the whole network and serious economic consequences. In essence, network analysis can help us gain actionable insights and make better data-driven decisions based on the networks. On that note, the objective of this dissertation is to improve upon existing tools for more accurate mining and analysis of real-world networks --Abstract, page iv

Dynamics of polymeric solutions in complex kinematics bulk and free surface flows: Multiscale/Continuum simulations and experimental studies

While rheological and microstructural complexities have posed tremendous challenges to researchers in developing first principles models and simulation techniques that can accurately and robustly predict the dynamical behaviour of polymeric flows, the past two decades have offered several significant advances towards accomplishing this goal. These accomplishments include: (1). Stable and accurate formulation of continuum-level viscoelastic constitutive models and their efficient implementation using operator splitting methods to explore steady and transient flows in complex geometries, (2). Prediction of rheology of polymer solutions and melts based on micromechanical models as well as highly parallel self-consistent multiscale simulations of non-homogeneous flows. The main objective of this study is to leverage and build upon the aforementioned advances to develop a quantitative understanding of the flow-micro-structure coupling mechanisms in viscoelastic polymeric fluids and in turn predict, consistent with experiments, their essential macroscopic flow properties e.g. frictional drag, interface shape, etc. To this end, we have performed extensive continuum and multiscale flow simulations in several industrially relevant bulk and free surface flows. The primary motivation for the selection of the specific flow problems is based on their ability to represent different deformation types, and the ability to experimentally verify the simulation results as well as their scientific and industrial significance

Development and qualification of a scale-down model of a commercial mammalian cell culture bioreactor using Computational Fluid Dynamics

The use of computational fluid dynamics (CFD) techniques can be used to develop and/or optimize a scale-down model to investigate mixing, oxygen mass transfer characteristics and turbulence, strain rate, and bubble size distribution in laboratory-scale stirred-tank bioreactors. In this work, CFD was used to test and modify a laboratory-scale bioreactor model of a manufacturing-scale bioreactor. The laboratory-scale model was originally established based on power per volume (P/V) and volume of gas per bioreactor volume per minute (vvm). CFD simulations of mixing time, power input, and gas volume hold-up were performed to demonstrate comparability between the laboratory-scale model and the manufacturing-scale bioreactor. These simulations were verified with experimental measurement of mixing time and gas hold-up. The results were used to propose sparge rate and impeller agitation as factors in a Design of Experiments (DoE) study in laboratory-scale bioreactors. The impact of sparge rate and impeller agitation on cell growth, productivity, and product quality attributes were evaluated in the DOE study. The laboratory-scale production bioreactor model was compared to the manufacturing-scale production bioreactor. The results confirmed that CFD techniques could be used to establish sparge rate and impeller agitation to improve a scale-down model

Computational Fluid Dynamics (CFD) modelling and experimental confirmation of hollow fiber tangential flow filtration (HFTFF) and alternating tangential flow filtration (ATF) In a perfusion bioreactor

Hollow fiber tangential flow filtration (HFTFF) and Alternating tangential flow filtration (HFATF) are technologies of choice in continuous (perfusion) bioreactor operations. A major drawback of these technologies is membrane fouling and associated reduction in membrane permeability. Membrane fouling leads to a gradual decline in trans-membrane flux and the sieving of the protein product. Additionally, experimental data suggests that under otherwise similar conditions protein sieving may be different in TFF vs. ATF, indicating that flow behavior patterns in the two technologies may be different. Many models of fouling and protein sieving have been reported in the literature for HFTFF. In comparison, however, there is limited research work on HFATF, making it hard to compare mechanisms of fouling and product sieving between HFTFF and HFATF. Additionally, almost all mechanisms of fouling and predictive models make sweeping assumptions with regards to the complex flow patterns prevailing in HFTFF and HFATF. In this study, we provide experimental data and computational fluid dynamics (CFD) information to gain insight into factors that impact fouling and product sieving. Specifically, first we present the confirmation of CFD model outputs by comparing experimentally measured trans-membrane flux and pressure with model predictions. Next, we compare the CFD model predictions of pressure drop, shear rate profile and axial and radial fluid velocity distributions between HFTFF and HFATF. Subsequently, we investigate the shear effect on cell damage, using the concept of constant Camp number, defined as Gt = constant, where G is the prevailing shear rate and t is the exposure time. Our CFD model predicts that shear rate (G) and hence the resulting stress experienced by cells in HFATF has a distribution that is determined by the operation of the diaphragm pump. Finally, we use CFD to compare Gt profile generated by imposing different pump condition

FSMS: A Frequent Subgraph Mining Algorithm using Mapping Sets

With the increasing prevalence of data that model relationships between various entities, the use of a graph-based representation for real-world problems offers a logical strategy for organizing information and making knowledge-based decisions. In particular, often it is useful to identify the most frequent patterns or relationships amongst the data in a graph, which requires finding frequent subgraphs. Algorithms for addressing that problem have been proposed for over 15 years. In the worst case, all subgraphs in the graph must be examined, which is exponential in complexity, and subgraph isomorphisms must be computed, which is an NP-complete problem. Frequent subgraph algorithms may attempt to improve the actual runtime performance by reducing the size of the search space, avoiding duplicate comparisons, and/or minimizing the amount of memory required for compiling intermediate results. Herein we present a frequent subgraph mining algorithm that leverages mapping sets in order to eliminate the isomorphism computation during the search for non-edge-disjoint frequent subgraphs. Experimental results show that absence of isomorphism computation leads to much faster frequent subgraph detection when there is a need to identify all occurrences of those subgraphs

Motif-Level Robustness Analysis of Power Grids

Motifs are often called the building blocks of networks and can be used for better understanding of the local structure of networks. Certain motifs may occur more frequently in one network than in another network with the same global structural properties such as the same nodal degree distribution. On that note, analysis of motifs is found to be an invaluable tool to show that networks with similar global topological properties may be completely different in terms of local topological properties. Most studies on the robustness of power system networks against targeted attacks tend to only focus on global topological measures of power grids. However, the impact of local structures can also unveil hidden mechanisms behind the vulnerability of power grids and their dynamic response to failures. In this paper, we present a preliminary study to investigate the local structure of European power grids and find the correlation between their robustness against targeted attacks and the presence or absence of certain motifs

Interactive Visualization of Robustness Enhancement in Scale-free Networks with Limited Edge Addition (RENEA)

Error tolerance and attack vulnerability of scale-free networks are usually used to evaluate the robustness of these networks. While new forms of attacks are developed everyday to compromise infrastructures, service providers are expected to develop strategies to mitigate the risk of extreme failures. Recently, much work has been devoted to design networks with optimal robustness, whereas little attention has been paid to improve the robustness of existing ones. Herein we present RENEA, a method to improve the robustness of a scale-free network by adding a limited number of edges. While adding an edge to a network is an expensive task, our system, during each iteration, allows the user to select the best option based on the cost, amongst all proposed ones. The edge-addition interactions are performed through a visual user interface while the algorithm is running. RENEA is designed based on the evolution of the network\u27s largest component during a sequence of targeted attacks. Through experiments on synthetic and real-life data sets, we conclude that applying RENEA on a scale-free network while interacting with the user can significantly improve its attack survivability at the lowest cost