Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods com...Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views.Subsequently,they design a contrastive pretext task between these views with the goal of maximizing their agreement.These methods as-sume the augmented graph can fully preserve the semantics of the original.However,typical data augmentation strategies in GCL,such as random edge dropping,may alter the properties of the original graph.As a result,previous GCL methods overlooked graph differences,potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different.Therefore,we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples.In this work,we propose a novel graph contrastive learning framework,named Accurate Difference-based Node-Level Graph Contrastive Learning(DNGCL),which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs.Specifically,we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node.Furthermore,we employ multiple types of data augmentation commonly used in current GCL methods on the original graph,aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information.We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines,which strongly validates the effectiveness of our model.展开更多
The realm of cybersecurity is replete with challenges,not least among them being the art of social engineering.This form of attack leverages human tendencies such as trust,leading to potential breaches.Though more cov...The realm of cybersecurity is replete with challenges,not least among them being the art of social engineering.This form of attack leverages human tendencies such as trust,leading to potential breaches.Though more covert than brute force or technical hacks,social engineering can be insidiously effective.Within this exposition,we probe various manifestations of social engineering:from phishing to pretexting,baiting to tailgating,and the subtle act of shoulder surfing,concluding with mitigation strategies.展开更多
基金supported in part by the Zhejiang Provincial Natural Science Foundation of China(LDT23F01012F01 and LDT23F01015F01)in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang Grant GK229909299001-008the National Natural Science Foundation of China(62372146 and 61806061).
文摘Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views.Subsequently,they design a contrastive pretext task between these views with the goal of maximizing their agreement.These methods as-sume the augmented graph can fully preserve the semantics of the original.However,typical data augmentation strategies in GCL,such as random edge dropping,may alter the properties of the original graph.As a result,previous GCL methods overlooked graph differences,potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different.Therefore,we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples.In this work,we propose a novel graph contrastive learning framework,named Accurate Difference-based Node-Level Graph Contrastive Learning(DNGCL),which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs.Specifically,we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node.Furthermore,we employ multiple types of data augmentation commonly used in current GCL methods on the original graph,aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information.We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines,which strongly validates the effectiveness of our model.
文摘The realm of cybersecurity is replete with challenges,not least among them being the art of social engineering.This form of attack leverages human tendencies such as trust,leading to potential breaches.Though more covert than brute force or technical hacks,social engineering can be insidiously effective.Within this exposition,we probe various manifestations of social engineering:from phishing to pretexting,baiting to tailgating,and the subtle act of shoulder surfing,concluding with mitigation strategies.
