Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.

Knowledge-based recommendation with contrastive learning

Knowledge Graphs(KGs)have been incorporated as external information into recommendation systems to ensure the high-confidence system.Recently,Contrastive Learning(CL)framework has been widely used in knowledge-based recommendation,owing to the ability to mitigate data sparsity and it considers the expandable computing of the system.However,existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge:(1)For the knowledge view generation,they only perform simple data augmentation operations on KGs,resulting in the introduction of noise and irrelevant information,and the loss of essential information.(2)For the knowledge view encoder,they simply add the edge information into some GNN models,without considering the relations between edges and entities.Therefore,this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL)framework,which generates dual views from user–item interaction graph and KG.Specifically,through data enhancement technology,KRCL introduces historical interaction information,background knowledge and item–item semantic information.Then,a novel relation-aware GNN model is proposed to encode the knowledge view.Finally,through the designed contrastive loss,the representations of the same item in different views are closer to each other.Compared with various recommendation methods on benchmark datasets,KRCL has shown significant improvement in different scenarios.

Efficient Publication of Distributed and Overlapping Graph Data Under Differential Privacy

Graph data publication has been considered as an important step for data analysis and mining.Graph data,which provide knowledge on interactions among entities,can be locally generated and held by distributed data owners.These data are usually sensitive and private,because they may be related to owners’personal activities and can be hijacked by adversaries to conduct inference attacks.Current solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed manners.Therefore,this work proposes a novel framework for distributed graph publication.In this framework,differential privacy is applied to justify the safety of the published contents.It includes four phases,i.e.,graph combination,plan construction sharing,data perturbation,and graph reconstruction.The published graph selection is guided by one data coordinator,and each graph is perturbed carefully with the Laplace mechanism.The problem of graph selection is formulated and proven to be NP-complete.Then,a heuristic algorithm is proposed for selection.The correctness of the combined graph and the differential privacy on all edges are analyzed.This study also discusses a scenario without a data coordinator and proposes some insights into graph publication.