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 simu...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.展开更多
Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks.Recently,Graph Neural Network(GNN)-based recommendation systems exploit higher-order ...Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks.Recently,Graph Neural Network(GNN)-based recommendation systems exploit higher-order interactions within the user-item interaction graph,demonstrating cutting-edge performance in recommendation tasks.However,GNN-based recommendation models are susceptible to different types of noise attacks,such as deliberate perturbations or false clicks.These attacks propagate through the graph and adversely affect the robustness of recommendation results.Conventional two-stage method that purifies the graph before training the GNN model is suboptimal.To strengthen the model’s resilience to noise,we propose Graph Structure Learning for Robust Recommendation(GSLRRec),a joint learning framework that integrates graph structure learning and GNN model training for recommendation.Specifically,GSLRRec considers the graph adjacency matrix as adjustable parameters,and simultaneously optimizes both the graph structure and the representations of user/item nodes for recommendation.During the joint training process,the graph structure learning employs low-rank and sparse constraints to effectively denoise the graph.Our experiments illustrate that the simultaneous learning of both structure and GNN parameters can provide more robust recommendation results under various noise levels.展开更多
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...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.展开更多
基金supported by the project of the National Natural Science Foundation of China(No.61772562)the Knowledge Innovation Program of Wuhan-Basic Research(No.2022010801010225)the Fundamental Research Funds for the Central Universities(No.2662022YJ012)。
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.62272001 and 62206002)the Anhui Provincial Natural Science Foundation(No.2208085QF195)+2 种基金the Hefei Key Common Technology Project(No.GJ2022GX15)the University Collaborative Innovation Project of Anhui Province(No.GXXT-2021-087)the Anhui Province Key Research and Development Program(No.202104a05020058).
文摘Recommendation systems play a crucial role in uncovering concealed interactions among users and items within online social networks.Recently,Graph Neural Network(GNN)-based recommendation systems exploit higher-order interactions within the user-item interaction graph,demonstrating cutting-edge performance in recommendation tasks.However,GNN-based recommendation models are susceptible to different types of noise attacks,such as deliberate perturbations or false clicks.These attacks propagate through the graph and adversely affect the robustness of recommendation results.Conventional two-stage method that purifies the graph before training the GNN model is suboptimal.To strengthen the model’s resilience to noise,we propose Graph Structure Learning for Robust Recommendation(GSLRRec),a joint learning framework that integrates graph structure learning and GNN model training for recommendation.Specifically,GSLRRec considers the graph adjacency matrix as adjustable parameters,and simultaneously optimizes both the graph structure and the representations of user/item nodes for recommendation.During the joint training process,the graph structure learning employs low-rank and sparse constraints to effectively denoise the graph.Our experiments illustrate that the simultaneous learning of both structure and GNN parameters can provide more robust recommendation results under various noise levels.
文摘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.
