Spatial Structure of Complex Network and Diffusion Dynamics Zi Hui
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Oracle Database 10G: Oracle Spatial Network Data Model
Oracle Database 10g: Oracle Spatial Network Data Model An Oracle Technical White Paper May 2005 Oracle Spatial Network Data Model Table of Contents Introduction ....................................................................................................... 3 Design Goals and Architecture ....................................................................... 4 Major Steps Using the Network Data Model................................................ 5 Network Modeling........................................................................................ 5 Network Analysis.......................................................................................... 6 Network Modeling ............................................................................................ 6 Metadata and Schema ....................................................................................... 6 Network Java Representation and API.......................................................... 7 Network Creation Using SQL and PL/SQL ................................................ 8 Creating a Logical Network......................................................................... 9 Creating a Spatial Network.......................................................................... 9 Creating an SDO Geometry Network ................................................ 10 Creating an LRS Geometry Network.................................................. 10 Creating a Topology Geometry Network........................................... 11 Network Creation -
A Network-Of-Networks Percolation Analysis of Cascading Failures in Spatially Co-Located Road-Sewer Infrastructure Networks
Physica A 538 (2020) 122971 Contents lists available at ScienceDirect Physica A journal homepage: www.elsevier.com/locate/physa A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks ∗ ∗ Shangjia Dong a, , Haizhong Wang a, , Alireza Mostafizi a, Xuan Song b a School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, United States of America b Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China article info a b s t r a c t Article history: This paper presents a network-of-networks analysis framework of interdependent crit- Received 26 September 2018 ical infrastructure systems, with a focus on the co-located road-sewer network. The Received in revised form 28 May 2019 constructed interdependency considers two types of node dynamics: co-located and Available online 27 September 2019 multiple-to-one dependency, with different robustness metrics based on their function Keywords: logic. The objectives of this paper are twofold: (1) to characterize the impact of the Co-located road-sewer network interdependency on networks' robustness performance, and (2) to unveil the critical Network-of-networks percolation transition threshold of the interdependent road-sewer network. The results Percolation modeling show that (1) road and sewer networks are mutually interdependent and are vulnerable Infrastructure interdependency to the cascading failures initiated by sewer system disruption; (2) the network robust- Cascading failure ness decreases as the number of initial failure sources increases in the localized failure scenarios, but the rate declines as the number of failures increase; and (3) the sewer network contains two types of links: zero exposure and severe exposure to liquefaction, and therefore, it leads to a two-phase percolation transition subject to the probabilistic liquefaction-induced failures. -
Planarity As a Driver of Spatial Network Structure
DOI: http://dx.doi.org/10.7551/978-0-262-33027-5-ch075 Planarity as a driver of Spatial Network structure Garvin Haslett and Markus Brede Institute for Complex Systems Simulation, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/ecal2015/27/423/1903775/978-0-262-33027-5-ch075.pdf by guest on 23 September 2021 Abstract include city science (Cardillo et al., 2006; Xie and Levin- son, 2007; Jiang, 2007; Barthelemy´ and Flammini, 2008; In this paper we introduce a new model of spatial network growth in which nodes are placed at randomly selected lo- Masucci et al., 2009; Chan et al., 2011; Courtat et al., 2011; cations in space over time, forming new connections to old Strano et al., 2012; Levinson, 2012; Rui et al., 2013; Gud- nodes subject to the constraint that edges do not cross. The mundsson and Mohajeri, 2013), electronic circuits (i Can- resulting network has a power law degree distribution, high cho et al., 2001; Bassett et al., 2010; Miralles et al., 2010; clustering and the small world property. We argue that these Tan et al., 2014), wireless networks (Huson and Sen, 1995; characteristics are a consequence of two features of our mech- anism, growth and planarity conservation. We further pro- Lotker and Peleg, 2010), leaf venation (Corson, 2010; Kat- pose that our model can be understood as a variant of ran- ifori et al., 2010), navigability (Kleinberg, 2000; Lee and dom Apollonian growth. -
Arxiv:2006.02870V1 [Cs.SI] 4 Jun 2020
The why, how, and when of representations for complex systems Leo Torres Ann S. Blevins [email protected] [email protected] Network Science Institute, Department of Bioengineering, Northeastern University University of Pennsylvania Danielle S. Bassett Tina Eliassi-Rad [email protected] [email protected] Department of Bioengineering, Network Science Institute and University of Pennsylvania Khoury College of Computer Sciences, Northeastern University June 5, 2020 arXiv:2006.02870v1 [cs.SI] 4 Jun 2020 1 Contents 1 Introduction 4 1.1 Definitions . .5 2 Dependencies by the system, for the system 6 2.1 Subset dependencies . .7 2.2 Temporal dependencies . .8 2.3 Spatial dependencies . 10 2.4 External sources of dependencies . 11 3 Formal representations of complex systems 12 3.1 Graphs . 13 3.2 Simplicial Complexes . 13 3.3 Hypergraphs . 15 3.4 Variations . 15 3.5 Encoding system dependencies . 18 4 Mathematical relationships between formalisms 21 5 Methods suitable for each representation 24 5.1 Methods for graphs . 24 5.2 Methods for simplicial complexes . 25 5.3 Methods for hypergraphs . 27 5.4 Methods and dependencies . 28 6 Examples 29 6.1 Coauthorship . 29 6.2 Email communications . 32 7 Applications 35 8 Discussion and Conclusion 36 9 Acknowledgments 38 10 Citation diversity statement 38 2 Abstract Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. -
Query Processing in Spatial Network Databases
Query Processing in Spatial Network Databases Dimitris Papadias Jun Zhang Department of Computer Science Department of Computer Science Hong Kong University of Science and Technology Hong Kong University of Science and Technology Clearwater Bay, Hong Kong Clearwater Bay, Hong Kong [email protected] [email protected] Nikos Mamoulis Yufei Tao Department of Computer Science and Information Systems Department of Computer Science University of Hong Kong City University of Hong Kong Pokfulam Road, Hong Kong Tat Chee Avenue, Hong Kong [email protected] [email protected] than their Euclidean distance. Previous work on spatial Abstract network databases (SNDB) is scarce and too restrictive Despite the importance of spatial networks in for emerging applications such as mobile computing and real-life applications, most of the spatial database location-based commerce. This necessitates the literature focuses on Euclidean spaces. In this development of novel and comprehensive query paper we propose an architecture that integrates processing methods for SNDB. network and Euclidean information, capturing Every conventional spatial query type (e.g., nearest pragmatic constraints. Based on this architecture, neighbors, range search, spatial joins and closest pairs) we develop a Euclidean restriction and a has a counterpart in SNDB. Consider, for instance, the network expansion framework that take road network of Figure 1.1, where the rectangles advantage of location and connectivity to correspond to hotels. If a user at location q poses the efficiently prune the search space. These range query "find the hotels within a 15km range", the frameworks are successfully applied to the most result will contain a, b and c (the numbers in the figure popular spatial queries, namely nearest correspond to network distance). -
Urban Street Network Analysis in a Computational Notebook
Urban Street Network Analysis in a Computational Notebook Geoff Boeing Department of Urban Planning and Spatial Analysis Sol Price School of Public Policy University of Southern California [email protected] Abstract: Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate re- producible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tu- torials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, vi- sualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help in- troduce methods to new users and help researchers reach broader audiences in- terested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban plan- ning, analytics, and related geocomputation disciplines should continue into the future.1 1 Introduction A traditional academic and professional divide has long existed between code creators and code users. The former would develop software tools and workflows for professional or research ap- plications, which the latter would then use to conduct analyses or answer scientific questions. -
Experimenting with Spatial Networks to Save Time When Experimenting, It Can Be Useful to Create a Subset of the Airport Dataset
Experimenting with Spatial Networks To save time when experimenting, it can be useful to create a subset of the airport dataset, for instance keeping only the airports of larger degree. Note also that when computing deterrence function, it is sometimes useful to work with random samples instead of computing distances between all pairs of nodes. This dataset is small enough however to compute all values in less than 1 minute on an average personal computer. To the best of my knowledge, there is no good library to work with spatial networks. networkx has functions to generate random spatial graphs (called geometric graphs https://networkx.org/ documentation/stable/reference/generators.html), but they are not designed to fit deterrence functions, and are not adapted to work with geographical coordinates. 1. Deterrence Function on the airport dataset When studying a network with spatial information, a first step to check if it can indeed be considered as a spatial network is to compute its deterrence function. (a) We will need to discretize distances using bins(similar to a histogram). Choose b bins, for instance every 500 km from 0km to 10,000km. Write a function which, provided a list of distances, return a list with b values, the number of values in each bin. A convenient way is to use numpy.digitize and collections.Counter . (b) Write a function which compute the distance between two positions on Earth in km. You can use function haversine from the package of the same name( pip install haversine ). To obtain standard latitude and longitude from the data in the airport dataset, you need to divide latitude and longitude values by 3600. -
Osmnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks Geoff Boeing Email: [email protected] Department of City and Regional Planning University of California, Berkeley May 2017 Abstract Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon. This is a preprint. Download the published article at: http://geoffboeing.com/publications/osmnx-complex- street-networks/ Cite as: Boeing, G. 2017. “OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks.” Computers, Environment and Urban Systems 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004 BOEING 1. -
Statistical Physics of Social Dynamics
Statistical physics of social dynamics Claudio Castellano∗ SMC, INFM-CNR and Dipartimento di Fisica, “Sapienza” Universit`adi Roma, Piazzale A. Moro 2, 00185 Roma I-ITALY Santo Fortunato† Complex Networks Lagrange Laboratory, ISI Foundation, Viale S. Severo 65, 10133, Torino, I-ITALY Vittorio Loreto‡ Dipartimento di Fisica, “Sapienza” Universit`adi Roma and SMC, INFM-CNR, Piazzale A. Moro 2, 00185 Roma I-ITALY and Complex Networks Lagrange Laboratory, ISI Foundation, Viale S. Severo 65, 10133, Torino, I-ITALY Statistical physics has proven to be a very fruitful framework to describe phenomena outside the realm of traditional physics. The last years have witnessed the attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. Here we review the state of the art by focusing on a wide list of topics ranging from opinion, cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, social spreading. We highlight the connections between these problems and other, more traditional, topics of statistical physics. We also emphasize the comparison of model results with empirical data from social systems. Contents 1. The Naming Game 30 2. Symmetry breaking: a controlled case 31 I. INTRODUCTION 2 3. The role of the interaction topology 32 C. Other models 33 II. GENERAL FRAMEWORK: CONCEPTS AND TOOLS 3 D. Language competition 33 A. Order and disorder: the Ising paradigm 4 1. Macroscopic models 34 B. Role of topology 5 2. Microscopic models 35 C. Dynamical systems approach 7 D. Agent-based modeling 7 VI. CROWD BEHAVIOR 35 A. Flocking 36 III. -
Spatial Science and Network Science: Review and Outcomes of a Complex Relationship César Ducruet, Laurent Beauguitte
Spatial science and network science: Review and outcomes of a complex relationship César Ducruet, Laurent Beauguitte To cite this version: César Ducruet, Laurent Beauguitte. Spatial science and network science: Review and outcomes of a complex relationship. Networks and Spatial Economics, Springer Verlag, 2014, 14 (3-4), pp.297-316. 10.1007/s11067-013-9222-6. halshs-01093664 HAL Id: halshs-01093664 https://halshs.archives-ouvertes.fr/halshs-01093664 Submitted on 10 Dec 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Spatial science and network science: Review and outcomes of a complex relationship DUCRUET César1 French National Centre for Scientific Research (CNRS) UMR 8054 Géographie-Cités, Paris, France Email: [email protected] BEAUGUITTE Laurent UMR 6266 IDEES, Rouen, France Email: [email protected] Pre-final version of the paper published in Networks and Spatial Economics, Vol. 14, Issue 3-4, pp. 297-316. Abstract For decades, the spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed. -
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
International Journal of Geo-Information Article Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling Chidubem Iddianozie * and Gavin McArdle School of Computer Science, University College Dublin, Dublin 4, Ireland; [email protected] * Correspondence: [email protected] Received: 30 September 2020; Accepted: 11 November 2020; Published: 13 November 2020 Abstract: Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these assumptions may not be sufficiently held. In this article, we study the problem of graph learning on spatial graphs. Particularly, we focus on transductive learning methods for the imbalanced case. Given the nature of these graphs, we hypothesize that taking the global structure of the graph into account when aggregating local information would be beneficial especially with respect to generalisability. Thus, we propose a novel approach to training GNNs for these type of graphs. We achieve this through a sampling technique: Structure-Aware Sampling (SAS), which leverages the intra-class and global-geodesic distances between nodes. We model the problem as a node classification one for street networks with high variance between class sizes. We evaluate our approach using large real-world graphs against state-of-the-art methods. In the majority of cases, our approach outperforms traditional methods by up to a mean F1-score of 20%. -
Interacting Particle Systems for Opinion Dynamics: the Deffuant Model and Some Generalizations
THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Interacting particle systems for opinion dynamics: the Deffuant model and some generalizations TIMO HIRSCHER Division of Mathematics Department of Mathematical Sciences CHALMERS UNIVERSITY OF TECHNOLOGY AND UNIVERSITY OF GOTHENBURG Göteborg, Sweden 2016 Interacting particle systems for opinion dynamics: the Deffuant model and some generalizations Timo Hirscher ISBN 978-91-7597-328-9 c Timo Hirscher, 2016. Doktorsavhandlingar vid Chalmers tekniska högskola Ny serie nr 4009 ISSN 0346-718X Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg 412 96 Göteborg Sweden Phone: +46 (0)31-772 1000 Printed in Göteborg, Sweden 2016 Interacting particle systems for opinion dynamics: the Deffuant model and some generalizations Timo Hirscher Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg Abstract In the field of sociophysics, various concepts and techniques taken from statisti- cal physics are used to model and investigate some social and political behavior of a large group of humans: their social network is given by a simple graph and neighboring individuals meet and interact in pairs or small groups. Although most of the established models feature rather simple microscopic interaction rules, the macroscopic long-time behavior of the collective often eludes an ana- lytical treatment due to the complexity, which stems from the interaction of the large system as a whole. An important class of models in the area of opinion dynamics is the one based on the principle of bounded confidence: Individuals hold and share opin- ions with others in random encounters. Their mutual influence will lead to up- dated opinions approaching a compromise, but only if the distance of opinions was not too large in the first place.