Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in practice. To fill this gap, we propose a novel attacking paradigm, named Generative Adversarial Fake Node Camouflaging (GAFNC), with its crux lying in crafting a set of fake nodes in a generative-adversarial regime. These nodes carry camouflaged malicious features and can poison the victim GNN by passing their malicious messages to the original graph via learned topological structures, such that they 1) maximize the devastation of classification accuracy (i.e., global attack) or 2) enforce the victim GNN to misclassify a targeted node set into prescribed classes (i.e., target attack). We benchmark our experiments on four real-world graph datasets, and the results substantiate the viability, effectiveness, and stealthiness of our proposed poisoning attack approach. Code is released in github.com/chao92/GAFNC.

Feature selection is a technique used to reduce an original set of features to a subset containing the most salient features. Reducing the feature set to the most significant subset of features typically results in an increase in the overall accuracy of a system. It has been shown that in some cases, the use of feature selection can make an underlying system susceptible to adversarial attacks. In this paper, we investigate the susceptibility of a feature selection-based Authorship Attribution System (AAS) to adversarial authorship attacks. The AAS studied is an instance of a Linear Support Vector Machine (LSVM). The feature selection algorithm used is an instance of Genetic & Evolutionary Feature Selection (GEFeS)In order to evaluate the GEFeS+LSVM-based AAS, we use three adversarial authorship masking techniques to generate adversarial texts to attack the AAS. Our results show that in some cases the GEFeS+LSVM-based AAS is more susceptible to adversarial authorship attacks. We provide a simple measurement to determine whether the use of GEFeS is beneficial or detrimental to a LSVM-based AAS.

This paper presents a method for test scenario generation based on formal specifications and usage profiles. It is a major component of a framework for testing object-oriented programs. In this framework, the requirements of a software system are formally specified. The anticipated application of the system is expressed in a usage profile, which is a state model that indicates the dynamic behavior of the system and execution probabilities for the behaviors. The state model is used as a guide to derive the anticipated operation scenarios. An enhanced state transition diagram is used to represent the state model, which incorporates hierarchy, usage and parameter information. Since the number of feasible scenarios can be extremely large, probability and importance criteria are used to select the most probable and important scenarios.

This paper presents a procedure for automatically drawing directed graphs.Our system, CG, uses a unique clan-based graph decomposition to determine intrinsic substructures (clans) in the graph and to produce a parse tree.The tree is given attributes that specify the node layout.CG then uses tree properties with the addition of "routing nodes" to route the edges.The objective of the system is to provide, automatically, an aesthetically pleasing visual layout for arbitrary directed graphs.The prototype has shown the strengths of this approach.The innovative strategy of clanbased graph decomposition is the first digraph drawing technique to analyze locality in the graph in two dimensions.The typical approach to drawing digraphs uses a single dimension, level, to arrange the nodes.

Article Free Access Share on Higher-order attribute grammars and editing environments Authors: Tim Teitelbaum Department of Computer Science, Cornell University, Ithaca, NY Department of Computer Science, Cornell University, Ithaca, NYView Profile , Richard Chapman Department of Computer Science, Cornell University, Ithaca, NY Department of Computer Science, Cornell University, Ithaca, NYView Profile Authors Info & Claims PLDI '90: Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementationJune 1990 Pages 197–208https://doi.org/10.1145/93542.93567Published:01 June 1990Publication History 17citation435DownloadsMetricsTotal Citations17Total Downloads435Last 12 Months20Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF