A review of regional energy internet in smart city from the perspective of energy community

http://www.cell.com/article/S2405844022013822/pdf

Converting data into knowledge with RCA methodology improved for inverters fault analysis

Information diffusion in social networks is a classic and, at the same time, very current problem. In fact, information diffusers are always looking for new techniques to disseminate information of their interest by creating backbones among them. In this paper, we focus on a specific, but very current and relevant, scenario regarding this way of proceeding. In fact, we propose an approach for the detection of possible backbones of information diffusers among different communities of a social network. Our approach is based on a new centrality measure that we call disseminator centrality. It is specifically designed to detect the so-called disseminator bridges, i.e., users belonging to multiple communities of a single social network, who want to disseminate information of their interest from one community to another by supporting each other. This paper describes the proposed approach, presents the disseminator centrality, illustrates the differences with respect to the related literature and presents the results of the experiments carried out to evaluate its performance. 

An approach to detect backbones of information diffusers among different communities of a social platform

Methodology for Researching the Lifecycle of Arctic Logistics Systems

Predicting Taxi Hotspots in Dynamic Conditions Using Graph Neural Network

In this paper, we propose an analytical model for information gathering and propagation in social networks using random sampling. We represent the social network using the Erdos–Renyi model of the random graph. When a given node is selected in the social network, information about itself and all of its neighbors are obtained and these nodes are considered to be discovered. We provide an analytical solution for the expected number of nodes that are discovered as a function of the number of nodes randomly sampled in the graph. We use the concepts of combinatorics, probability, and inclusion–exclusion principle for computing the number of discovered nodes. This is a computationally-intensive problem with combinatorial complexity. This model is useful when crawling and mining of the social network graph is prohibited. Our work finds application in several important real-world decision support scenarios such as survey sample selection, construction of public directory, and crowdsourced databases using social networks, targeted advertising, and recommendation systems. It can also be used for finding a randomized dominating set of a graph that finds applications in computer networks, document summarization, and biological networks. We have evaluated the performance both analytically as well as by means of simulation, and the results are comparable. The results have an accuracy of around 96% for random graphs and above 87% for the power-law graphs.

An analytical model for information gathering and propagation in social networks using random graphs

Mobile application based ride-hailing systems, eg, DiDi, Uber have become part of day to day life and natural choices of transport for urban commuters However, the pick-up demand in any area is not always matching with the supply or drop-off request in the same area Urban planners and researchers are working hard to balance this demand and supply situation for taxi requests The existing approaches have mainly focused on clustering of the spatial regions to identify hotspots, which refer to the locations with a high demand for pick-up requests In our study, we determined that if the hotspots focus on the clustering of high demand for pick-up requests, most of the hotspots pivot near the city center or two-three spatial regions, ignoring the other parts of the city In this work, we proposed a method, which can help in finding a local hotspot to cover the whole city area We proposed a dominating set problem based solution, which covers every part of the city This will help the drivers looking for near-by next customer in the region wherever they drop their last customer It will also reduce the waiting time for customers as well as for a driver looking for next pick-up request This would maximize their profit as well as help in improving their services

Saurabh Mishra

Papers

An analytical model for information gathering and propagation in social networks using random graphs

Optimal Placement of Taxis in a City Using Dominating Set Problem