ABSTRACT
Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc) which intelligently combines existing kshell and sum of neighbors' degree methods with knowledge of the network's global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.
ABSTRACT
The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.
