We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populations from several sources, including the Census, transportation surveys, and geographical data. The resulting networks represent an approximation of daily or weekly human interaction. Our results indicate that the synthetically generated graphs according to our methodology are closer to the real world graphs, as measured across multiple structural measures, than a range of stylized graphs generated using common network models from the literature.
studied differences in social contact networks generated according to different methods @cite_37 and the differences between the networks generated for two different cities (Delhi and Los Angeles) @cite_27 . They used both static and dynamic measures including degree distribution, temporal degree, vulnerability distribution, simulated epidemic curves, and the efficacy of two different epidemic mitigation interventions. Vulnerability is the probability that a node gets infected during an infectious disease outbreak. They showed significant differences due to the network generation methodology as well as across the two cities.
@cite_27 @cite_37
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We describe the synthesis of detailed social contact networks of Delhi, India, and Los Angeles, USA, for urban-scale epidemiological simulations. The network synthesis is done by combining information from multiple data sources, since social contact information cannot be obtained through direct surveys. We compare the two networks on various structural and dynamical metrics. Through the comparison between the two cities, we show important similarities and dierences between urban regions in dierent parts of the world. Social contact networks play an increasingly important role in computational epidemiology. We focus on massive social contact networks that cover an urban region comprising of millions of individuals and billions of time varying interactions. It is impossible to obtain such networks by simply measuring social interactions. As a result, such networks are often synthesized using a diverse set of real-world data. The synthesis method can be viewed as a complex stochastic process that outputs one realization of such a network. The resulting networks are extremely large, dynamic and unstructured. Any meaningful description of such networks is usually done in terms of structural properties. Building on our earlier work, we synthesize a detailed social contact network for the National Capital Territory (NCT) of India. We first synthesize a social visitation network, representing people visiting locations during different time intervals. We then project it to synthesize a people-people contact network. We are not aware of other works on synthesizing the NCT network. Two important questions arise when synthesizing such massive dynamic social contact networks: (i) how does one compare the networks that span the same region and (ii) when is the synthesized network adequate. To address them, we compute a number of network measurements: some of which are classical, while others capture the semantics of social contact networks. These metrics are used to study the similarities and differences between two networks representing the same urban region. For question (ii), we study our ability to understand the dynamics and control of epidemics. Dynamical measures that capture the joint interaction between the local dynamical process and the network structure are presented and used to analyze the NCT networks.