Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph share two big shortcomings. First, they essentially low-pass filters, thus potentially useful middle and high frequency band signals ignored. Second, bandwidth filters is fixed. Parameters a filter only transform inputs without changing curvature function. In reality,...

An uncertain graph (also known as probabilistic graph) is a generic model to represent many real-world networks from social biological. In recent times, analysis and mining of graphs have drawn significant attention the researchers data management community. Several noble problems been introduced, efficient methodologies developed solve those problems. Hence, there need summarize existing resul...

this article proposes composition of fuzzy sets theory with discrete-event simulation and puts it into application in order to model uncertain activity duration in simulating a real-world system. the purpose of this paper is to assess the usefulness of fuzzy simulation for modeling uncertain activity duration, especially when insufficient or no sample data is available. in this research, fuzzy ...

Subjective logic is a formalism for representing and reasoning under uncertain probabilistic information, with an explicit treatment of the uncertainty about the probability distributions. We introduce subjective networks as graph-based structures that generalize Bayesian networks to the theory of subjective logic. We discuss the prospectives of the subjective networks representation and the ch...

Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for diagnoses of complex systems. It performs well industry system such as nuclear power plants, chemical and spacecrafts. However, the variable state combination explosion in some cases still problem that may result inefficiency or even disability DUCG inference. In situation clinical diagnoses, when lot intermediate causes a...

A new method is proposed to measure software system security. Each component can be characterized by a composite net which combines the features of Finite State Machine and Operation Diagram. A fuzzy number is introduced to this net to represent uncertain elements that might affect the software security, and thus we construct Security Function for each component. The whole system Security Index...

This paper is concerned with robust synchronization of a system of uncertain agents whose mutual communications are constrained by the topology of the associated communication graph. The uncertainty of each agent is modeled as a gain-bounded multiplicative uncertainty. A sufficient condition for achieving the robust synchronization is derived based on the input-output stability argument under t...

In this paper we propose augmented interval Markov chains (AIMCs): a generalisation of the familiar interval Markov chains (IMCs) where uncertain transition probabilities are in addition allowed to depend on one another. This new model preserves the flexibility afforded by IMCs for describing stochastic systems where the parameters are unclear, for example due to measurement error, but also all...

This is the motivating question behind GFL-Web, which uses the Fragmentary Unlabeled Dependency Grammar, or FUDG (Schneider et al. 2013), to make syntactic representations easier on annotators. FUDG is written using the Graph Fragment Language or GFL, a simple ASCII-based notation, which annotators can master in a few minutes. This framework allows annotators to work quickly, underspecify where...

The aim of this paper is to propose a logical way to handle uncertain knowledge and change. Databases, diagnostic, planiication, taxonomy are some of the domains concerned by this problem. This paper focuses on the means Linear Logic ooers to represent taxonomical networks and to perform updates of databases containing incomplete information. The two problems are rst expressed in graph theory: ...