PGSLM:Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles

With the rapid development of the 5G communications,the edge intelligence enables Internet of Vehicles(IoV)to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously.To enhance the forecasting performance,a novel edge-enabled probabilistic graph structure learning model(PGSLM)is proposed,which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network.To obtain the spatio-temporal dependencies of traffic data,the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module.During the training process,a new graph training loss is introduced,which is composed of the K nearest neighbor(KNN)graph constructed by the traffic feature tensors and the graph structure.Detailed experimental results show that,compared with existing models,the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.

Matching user identities across social networks with limited profile data

Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.