Geographies of transport II

2016 ◽  
Vol 41 (3) ◽  
pp. 355-364 ◽  
Author(s):  
Tim Schwanen
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Complex Networks ◽  
Network Science ◽  
Complexity Science ◽  
Transport Systems ◽  
Transport Geography ◽  
Space And Time ◽  
Critical Reflexivity ◽  
Mixed Blessing

Geographical scholarship on transport has been boosted by the emergence of big data and advances in the analysis of complex networks in other disciplines, but these developments are a mixed blessing. They allow transport as object of analysis to exist in new ways and raise the profile of geography in interdisciplinary spaces dominated by physics and complexity science. Yet, they have also brought back concerns over the privileging of generality over particularity. This is because they have once more made acceptable and even normalized a focus on supposedly universal laws that explain the functioning of mobility systems and on space and time independent explanations of hierarchies, inequalities and vulnerabilities in transport systems and patterns. Geographical scholarship on transport should remain open to developments in big data and network science but would benefit from more critical reflexivity on the limitations and the historical and geographical situatedness of big data and on the conceptual shortcomings of network science. Big data and network analysis need to be critiqued and re-appropriated, and examples of how this can be done are starting to emerge. Openness, critique and re-appropriation are especially important in a context where transport geography decentralizes away from its Euro-American core, and the development pathways of transport and mobility in localities beyond that core deserve their own, unique explanations.

Core Kernels for Complex Network Analysis

2018 ◽  
pp. 30-58
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Network Science ◽  
Efficient Algorithms ◽  
The Other ◽  
Software Systems ◽  
Layer By Layer ◽  
Graph Visualization ◽  
Graph Problems ◽  
Centrality Metrics

The emergence of Network Science has motivated a renewed interest in classical graph problems for the analysis of the topology of complex networks. For example, important centrality metrics, such as the betweenness, the stress, the eccentricity, and the closeness centralities, are all based on BFS. On the other hand, the k-core decomposition of graphs defines a hierarchy of internal cores and decomposes large networks layer by layer. The k-core decomposition has been successfully applied in a variety of domains, including large graph visualization and fingerprinting, analysis of large software systems, and fraud detection. In this chapter, the authors review known efficient algorithms for traversing and decomposing large complex networks and provide insights on how the decomposition of graphs in k-cores can be useful for developing novel topology-aware algorithms.

scholarly journals Dynamics in and dynamics of networks using DyNSimF

2021 ◽  
Author(s):  
Maarten van den Ende ◽  
Mathijs Mayer ◽  
Sacha Epskamp ◽  
Michael Lees ◽  
Han van der Maas
Keyword(s):  
Network Analysis ◽  
Data Collection ◽  
Open Source ◽  
Network Science ◽  
Complexity Science ◽  
Formal Theories

Advancements of formal theories, network science, and data collection technologies make network analysis and simulation an increasingly crucial tool in complexity science. We present DyNSimF; the first open-source package that allows for the modeling of com- plex interacting dynamics on a network a well as dynamics of (the structure of) a net- work. The package can deal with weighted as well as directional connections, is scalable and efficient, and includes a utility-based edge-altering framework. DyNSimF includes visualization methods and tools to help analyze models. It is designed to be easily ex- tendable and makes use of NetworkX graphs. It aims to be easy to learn and to work with, enabling non-experts to focus on the development of models, while at the same time being highly customizable and extensible to allow for complex custom models.

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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