Inferring Structural Characteristics of Social Network for Smart Grid Adoption and Diffusion

The smart grid is developing in the direction of two-way interaction. As an important platform for two-way interaction between the user side and the power system, the social network plays an important role in the development of the smart grid. The structural parameters of the social network include the total number of users and the network density. The network density is reflected by the number of neighbor nodes of the network node and the strength of the connection. Determining the total number of users in the network is beneficial to the smart grid for power allocation. Power system load peaks, insufficient power supply, etc. Deter-mining network density helps speed up the popularization and promotion of smart grid technology and helps to understand the social influence of users in social networks. Offline social network topologies are usually selected by interviewees and then asked to list their neighbors. This paper proposes an inference technique based on so-called “fixed choice” surveys, which requires the interviewee to enumerate all the strong ties and a fixed number of weak ties(e.g.,two or ten). Then use the method of moments to infer the total number of users and the network density of the social network. Experiments using simulated data show that the proposed inference algor-ithm performs well in various network topo-logies and measurement scenarios, and the est-imates obtained are significantly more accu-rate than the estimates obtained directly using the coarse observation network.