Month: August 2017

Understanding Human-Machine Networks

In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.


Understanding Human-Machine Networks: A Cross-Disciplinary Survey

Milena Tsvetkova et al.

ACM Computing Surveys
Volume 50 Issue 1, April 2017
Article No. 12


Comparison of traffic reliability index with real traffic data

Existing studies have developed different indices based on various approaches including network connectivity, delay time and flow capacity, estimating the traffic reliability states from different angles. However, these indices mainly estimate traffic reliability from single view and rarely consider the combined effect of city traffic dynamics and underlying network structure. Based on percolation theory, Li et al. has developed a traffic reliability index to address this issue (Proc. Natl. Acad. Sci. USA 112(3):669-672, 2015) [1]. Here we compare this percolation-based index with one of the well-known index – congestion delay index (CDI). Using real traffic data of Beijing and Shenzhen (two large cities in China), we compare the two indices in the macroscopic trends and microscopic extreme values. The two indices are found to indicate the state of real-time traffic reliability in different consideration. Our results can be used for better evaluation of traffic system reliability and mitigation measures of traffic jams.


Comparison of traffic reliability index with real traffic data
Limiao Zhang, Guanwen Zeng, Shengmin Guo, Daqing LiEmail author and Ziyou Gao
EPJ Data Science20176:19 


Effects of network modularity on the spread of perturbation impact in experimental metapopulations

The networks that form natural, social, and technological systems are vulnerable to the spreading impacts of perturbations. Theory predicts that networks with a clustered or modular structure—where nodes within a module interact more frequently than they do with nodes in other modules—might contain a perturbation, preventing it from spreading to the entire network. Gilarranz et al. conducted experiments with networked populations of springtail ( Folsomia candida ) microarthropods to show that modularity limits the impact of a local extinction on neighboring nodes (see the Perspective by Sales-Pardo). In networks with high modularity, the perturbation was contained within the targeted module, and its impact did not spread to nodes beyond it. However, simulations revealed that modularity is beneficial to the network only when perturbations are present; otherwise, it hinders population growth.

Science , this issue p. [199][1]; see also p. [128][2]

[1]: /lookup/doi/10.1126/science.aal4122
[2]: /lookup/doi/10.1126/science.aan8075