Internet-human infrastructures: Lessons from Havana's StreetNet
- URL: http://arxiv.org/abs/2004.12207v1
- Date: Sat, 25 Apr 2020 18:26:18 GMT
- Title: Internet-human infrastructures: Lessons from Havana's StreetNet
- Authors: Abigail Z. Jacobs and Michaelanne Dye
- Abstract summary: StreetNet (SNET) is a distributed, community-run intranet that serves as the primary 'Internet' in Havana, Cuba.
We bridge ethnographies and the study of social networks and organizations to understand the way that power is embedded in the structure of Havana's SNET.
- Score: 4.9241264921748
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a mixed-methods approach to understanding the human infrastructure
underlying StreetNet (SNET), a distributed, community-run intranet that serves
as the primary 'Internet' in Havana, Cuba. We bridge ethnographic studies and
the study of social networks and organizations to understand the way that power
is embedded in the structure of Havana's SNET. By quantitatively and
qualitatively unpacking the human infrastructure of SNET, this work reveals how
distributed infrastructure necessarily embeds the structural aspects of
inequality distributed within that infrastructure. While traditional technical
measurements of networks reflect the social, organizational, spatial, and
technical constraints that shape the resulting network, ethnographies can help
uncover the texture and role of these hidden supporting relationships. By
merging these perspectives, this work contributes to our understanding of
network roles in growing and maintaining distributed infrastructures, revealing
new approaches to understanding larger, more complex Internet-human
infrastructures---including the Internet and the WWW.
Related papers
- A Taxonomy for Blockchain-based Decentralized Physical Infrastructure
Networks (DePIN) [0.1979158763744267]
We conduct a literature review and analysis of DePIN systems from a conceptual architecture.
We identify and define relevant components and attributes, establishing a clear hierarchical structure.
arXiv Detail & Related papers (2023-08-17T05:08:43Z) - Detecting Vulnerable Nodes in Urban Infrastructure Interdependent
Network [30.78792992230233]
We model the interdependent network as a heterogeneous graph and propose a system based on graph neural network with reinforcement learning.
The presented system leverages deep learning techniques to understand and analyze the heterogeneous graph, which enables us to capture the risk of cascade failure and discover vulnerable infrastructures of cities.
arXiv Detail & Related papers (2023-07-19T09:53:56Z) - IIVA: A Simulation Based Generalized Framework for Interdependent
Infrastructure Vulnerability Assessment [0.0]
This paper proposes a novel infrastructure vulnerability assessment framework that accounts for: various types of infrastructure interdependencies.
It is observed that higher the initial failure rate of the components, higher is the vulnerability of the infrastructure.
arXiv Detail & Related papers (2022-12-13T20:37:03Z) - A Bayesian Approach to Reconstructing Interdependent Infrastructure
Networks from Cascading Failures [2.9364290037516496]
Understanding network interdependencies is crucial to anticipate cascading failures and plan for disruptions.
Data on the topology of individual networks are often publicly unavailable due to privacy and security concerns.
We propose a scalable nonparametric Bayesian approach to reconstruct the topology of interdependent infrastructure networks.
arXiv Detail & Related papers (2022-11-28T17:45:41Z) - Stimulative Training of Residual Networks: A Social Psychology
Perspective of Loafing [86.69698062642055]
Residual networks have shown great success and become indispensable in today's deep models.
We aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing.
We propose a new training strategy to strengthen the performance of residual networks.
arXiv Detail & Related papers (2022-10-09T03:15:51Z) - TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
We propose a novel text-rich graph neural network with external knowledge (TeKo)
We first present a flexible heterogeneous semantic network that incorporates high-quality entities.
We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
arXiv Detail & Related papers (2022-06-15T02:33:10Z) - Rank Diminishing in Deep Neural Networks [71.03777954670323]
Rank of neural networks measures information flowing across layers.
It is an instance of a key structural condition that applies across broad domains of machine learning.
For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear.
arXiv Detail & Related papers (2022-06-13T12:03:32Z) - Introduction to the Artificial Intelligence that can be applied to the
Network Automation Journey [68.8204255655161]
The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps.
The recognize, generate intent, translate and refine features need a new way to implement algorithms.
arXiv Detail & Related papers (2022-04-02T08:12:08Z) - Urban Landscape from the Structure of Road Network: A Complexity
Perspective [0.0]
We investigate the relationship between the spatial scale of the modelled network entities against the amount of useful information contained within it.
We employ an entropy measure from complexity science and information theory to quantify the amount of information residing in each presentation of the network.
We find the critical spatial scale to be 85 m, at which the network obtained corresponds very well to the planning boundaries used by the local urban planners.
arXiv Detail & Related papers (2022-01-26T14:03:12Z) - GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning [50.90625274621288]
Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks.
We introduce a new class of problems, Graph Augmentation for Equitable Access (GAEA), to enhance equity in networked systems by editing graph edges under budget constraints.
arXiv Detail & Related papers (2020-12-07T18:29:32Z) - On the use of local structural properties for improving the efficiency
of hierarchical community detection methods [77.34726150561087]
We study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection.
We also check the performance impact of network prunings as an ancillary tactic to make hierarchical community detection more efficient.
arXiv Detail & Related papers (2020-09-15T00:16:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.