JGraphT -- A Java library for graph data structures and algorithms

Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze and query graphs are crucial in an era where large-scale spatial, societal and economic network data are abundantly available. One such package is JGraphT, a programming library which contains very efficient and generic graph data-structures along with a large collection of state-of-the-art algorithms. The library is written in Java with stability, interoperability and performance in mind. A distinctive feature of this library is the ability to model vertices and edges as arbitrary objects, thereby permitting natural representations of many common networks including transportation, social and biological networks. Besides classic graph algorithms such as shortest-paths and spanning-tree algorithms, the library contains numerous advanced algorithms: graph and subgraph isomorphism; matching and flow problems; approximation algorithms for NP-hard problems such as independent set and TSP; and several more exotic algorithms such as Berge graph detection. Due to its versatility and generic design, JGraphT is currently used in large-scale commercial, non-commercial and academic research projects. In this work we describe in detail the design and underlying structure of the library, and discuss its most important features and algorithms. A computational study is conducted to evaluate the performance of JGraphT versus a number of similar libraries. Experiments on a large number of graphs over a variety of popular algorithms show that JGraphT is highly competitive with other established libraries such as NetworkX or the BGL.Comment: Major Revisio

Computational and Analytical Tools for Resilient and Secure Power Grids

Enhancing power grids' performance and resilience has been one of the greatest challenges in engineering and science over the past decade. A recent report by the National Academies of Sciences, Engineering, and Medicine along with other studies emphasizes the necessity of deploying new ideas and mathematical tools to address the challenges facing the power grids now and in the future. To full this necessity, numerous grid modernization programs have been initiated in recent years. This thesis focuses on one of the most critical challenges facing power grids which is their vulnerability against failures and attacks. Our approach bridges concepts in power engineering and computer science to improve power grids resilience and security. We analyze the vulnerability of power grids to cyber and physical attacks and failures, design efficient monitoring schemes for robust state estimation, develop algorithms to control the grid under tension, and introduce methods to generate realistic power grid test cases. Our contributions can be divided into four major parts: Power Grid State Prediction: Large scale power outages in Australia (2016), Ukraine (2015), Turkey (2015), India (2013), and the U.S. (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. Power grid outages have devastating effects on almost every aspect of modern life as well as on interdependent systems. Despite their inevitability, the effects of failures on power grids' performance can be limited if the system operator can predict and understand the consequences of an initial failure and can immediately detect the problematic failures. To enable these capabilities, we study failures in power grids using computational and analytical tools based on the DC power flow model. We introduce new metrics to efficiently evaluate the severity of an initial failure and develop efficient algorithms to predict its consequences. We further identify power grids' vulnerabilities using these metrics and algorithms. Power Grid State Estimation: In order to obtain an accurate prediction of the subsequent effects of an initial failure on the performance of the grid, the system operator needs to exactly know when and where the initial failure has happened. However, due to lack of enough measurement devices or a cyber attack on the grid, such information may not be available directly to the grid operator via measurements. To address this problem, we develop efficient methods to estimate the state of the grid and detect failures (if any) from partial available information. Power Grid Control: Once an initial failure is detected, prediction methods can be used to predict the subsequent effects of that failure. If the initial failure is causing a cascade of failures in the grid, a control mechanism needs to be applied in order to mitigate its further effects. Power Grid Islanding is an effective method to mitigate cascading failures. The challenge is to partition the network into smaller connected components, called islands, so that each island can operate independently for a short period of time. This is to prevent the system to be separated into unbalanced parts due to cascading failures. To address this problem, we introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem and provide an efficient algorithm to partition the power grid into two operating islands. Power Grid Test Cases for Evaluation: In order to evaluate algorithms that are developed for enhancing power grids resilience, one needs to study their performance on the real grid data. However, due to security reasons, such data sets are not publicly available and are very hard to obtain. Therefore, we study the structural properties of the U.S. Western Interconnection grid (WI), and based on the results we present the Network Imitating Method Based on LEarning (NIMBLE) for generating synthetic spatially embedded networks with similar properties to a given grid. We apply NIMBLE to the WI and show that the generated network has similar structural and spatial properties as well as the same level of robustness to cascading failures. Overall, the results provided in this thesis advance power grids' resilience and security by providing a better understanding of the system and by developing efficient algorithms to protect it at the time of failure

Organisational Design & Mirroring in Construction

The mirroring hypothesis posits that an intrinsic connection exists between the architecture of a product and that of the organisation which produces it, which can influence operational efficiency. The mirroring hypothesis is applicable to construction wherein organisational design is concerned with the establishment of governance frameworks for the procurement of projects and product design is that of buildings and engineering structures. This thesis investigates the hypothesis that design data architecture mirrors component architecture in a construction project. A general procedure has emerged to investigate the mirroring hypothesis, consisting of three steps: the capturing of product architecture, the capturing of organisational architecture, and comparison of the two. The subject project is a completed building. The capturing of architecture is achieved by modelling functional dependency between components in the form of a node-link network structure. It was found that the subject project did not exhibit a high degree of visible or otherwise mirroring, hence the hypothesis is concluded to be false in this case. An explanation is that two architectures within one have been identified in the model. This makes senses because design data is structured into packages associated with design disciplines which are associated with sub-systems, which in turn corresponds to design team structure. On the other hand, the components model was prepared principally on the basis of physical connectivity. The result implies for organisational design in construction that the design management role should either be carried out by the architect for mirroring alignment, or, to mitigate misalignment, by a third party with design background as opposed to a construction background

Re-Spatializing Gangs in the United States: An Analysis of Macro- and Micro-Level Network Structures

Despite the significant contributions from location-based gang studies, the network structure of gangs beyond localized settings remains a neglected but important area of research to better understand the national security implications of gang interconnectivity. The purpose of this dissertation is to examine the network structure of gangs at the macro- and micro-level using social network analysis. At the macro-level, some gangs have formed national alliances in perpetuity with their goals and objectives. In order to study gangs at the macro-level, this research uses open-source data to construct an adjacency matrix of gang alliances and rivalries to map the relationships between gangs and analyze their network centrality across multiple metrics. The results suggest that native gangs are highly influential when compared to immigrant gangs. Some immigrant gangs, however, derive influence by “bridging” the gap between rival gangs. Mexican Drug Trafficking Organizations (MDTOs) play a similar role and feature prominently in the gang network. Moreover, removing MDTOs changes the network structure in favor of ideologically-motivated gangs over profit-oriented gangs. Critics deride macro-level approaches to studying gangs for their lack of national cohesion. In response, this research includes a micro-level analysis of gang member connections by mining Twitter data to analyze the geospatial distribution of gang members and, by proxy, gangs, using an exponential random graph model (ERGM) to test location homophily and better understand the extent to which gang members are localized. The findings show a positive correlation between location and shared gang member connections which is conceptually consistent with the proximity principle. According to the proximity principle, interpersonal relationships are more likely to occur in localized geographic spaces. However, gang member connections appear to be more diffuse than is captured in current location-based gang studies. This dissertation demonstrates that macro- and micro-level gang networks exist in unbounded geographic spaces where the interconnectivity of gangs transpose local issues onto the national security consciousness which challenges law and order, weakens institutions, and negatively impacts the structural integrity of the state

Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations

Inherent in any multivariate data is structure, which describes the general shape and distribution of the underlying point configuration. While there are potentially many types of structure that could be of interest, consider restricting interest to two general types: geometric structure, the general shape of a point configuration, and probabilistic structure, the general distribution of points within the configuration. The ability to quantify geometric structure is an important step in many common statistical analyses. For instance, general neighbourhood structure is captured using a k-nearest neighbour graph in dimension reduction techniques such as isomap and locally-linear embedding. Neighbourhood graphs are also used in sensor network localization, which has applications in fields such as environmental habitat monitoring and wildlife monitoring. Another geometric graph, the convex hull, is also used in wildlife monitoring as a rough estimate of an animal's home range. The identification of areas of high and low density is one example of measuring the probability structure of a configuration, which can be done using a wide variety of methods. One such method is using kernel density estimation, which can be viewed as a weighted sum of nearby points. Kernel density estimation has widely varying applications, including in regression analysis, and is used in general to assess certain features of the data (modality, skewness, etc.). Related to the idea of measuring structure is the concept of "Cognostics", which has been formalized as scatterplot diagnostics (or scagnostics). Scagnostics provides a framework through which interesting structure can be measured in a configuration. The central idea is to numerically summarize the structure of a large number of two-dimensional point configurations via measures calculated on geometric graphs. This allows the interesting views to be quickly identified, and ultimately examined visually, while the views deemed to be uninteresting are simply discarded. While a good starting point, several issues in the current framework need to be addressed. For instance, while each measure is designed to be in [0,1], there are some that, when measured over tens of thousands of configurations, fail to achieve this range. In addition, there is a lot of structure that could be considered interesting that is not captured by the current framework. These issues, among others, will be addressed and rectified so that the current scagnostic framework can continue to be built upon. With tools to measure structure, attention is turned to making use of the structural information contained in the configuration. Consider the problem of preserving measured structure during the task of data aggregation, more commonly known as binning. Existing methods of data aggregation tend to exist on two ends of the structure retention spectrum. Through experimentation, methods such as equal width and hexagonal binning will be shown to tend to retain the shape of the configuration, at the expense of the density, while methods such as equal frequency and random sampling tend to retain relative density at the expense of overall shape. Tree-based binning, a general binning framework inspired by classification and regression trees, is proposed to bridge the gap between these sets of specialist algorithms. GapBin, a specially designed tree-based binning algorithm, will be shown through experimentation to provide a trade-off in low dimensional space between geometric structure retention and probabilistic structure retention. In higher dimensions, it will be shown to be the superior algorithm in terms of structure retention among those considered. Next, the general problem of constructing a configuration with a given underlying structure is considered. For example, the minimal spanning tree is known to carry important clustering information. Of interest then, is the generation of configurations with a given minimal spanning tree structure. The problem of generating a configuration with a known minimal spanning tree is equivalent to completing a Euclidean distance matrix where the only known entries are those in the minimal spanning tree. For this problem, there are several solutions, including those of Alfakih et. al., Fang & O'Leary, and Trosset. None of these algorithms, however, are designed to retain the structure of the minimal spanning tree. In addition, the sparsity of the Euclidean distance matrix containing only the minimal spanning tree results in completions that are not accurate as compared to the known completion. This leads to issues in the point configurations of the resulting completions. To resolve these, two new algorithms are proposed which are designed to retain the structure of the minimal spanning tree, leading to more accurate completions of these sparse matrices. To complement the algorithms presented, implementation of these algorithms in the statistical programming language R will also be discussed. In particular, the R package treebinr for tree-based binning, and edmcr for Euclidean distance matrix completions will be presented

Examining supply chain agility using social network analysis : a thesis presented in partial fulfilment of the requirement for the degree of Master of Supply Chain Management at Massey University, Auckland, New Zealand

In the current literature of supply chain agility (SCA), the extant agility models are not only rare but are also usually developed from the viewpoint of a firm rather than from a network perspective. While social network analysis (SNA) has proven its power and capacity in the social sciences, it has been rarely applied to supply chain management (SCM) phenomena. As such, this is a primary motivation for this study to take shape. The main focus of the research is refined to build on the Scion project on Rural Value Chains. It seeks to explore the appropriateness of SNA to assess SCA and to simultaneously make a relative agility comparison between supply chains by SNA. The empirical data are collected by structured interviews in a rural area of New Zealand and then analysed as a network case by varying SNA metrics, tools, and techniques. This thesis sheds light on how SNA is appropriate to tap into the areas that are barely recognised by the extant approaches. The findings show that SNA is well able to consider interactions and linkages in complex networks, and it also enables the integrated lens of network and complex adaptive system (CAS) to examine network agility in a comprehensive and systematic manner. SNA lends itself well to phenomena that directly relates to, or results from, network topology, connectivity, and interconnectedness, such as network visibility, speed of responses, and the ability to have multiple connection options. However, if used exclusively, SNA is less appropriate to examine attributes that either have qualitative elements or which are associated with firm operations. This thesis has added to the literature the applicability of SNA to evaluate SCA and to model SCs. For policy makers, it offers a clearer understanding of the local network for regional development plans. For business owners, it proposes an alternative approach of evaluating SCA, SC relationships, and SC members, so as to build up effective SCM strategies

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available