It has always been a challenge to research inductive learning, which can embed newly unseen nodes. Inductive learning is a frequently encountered problem in practical applications of graph networks, but there is little research on dynamic heterogeneous network link prediction. Therefore, we propose a Heterogeneous and Temporal Model Based on Transformer (HT-Trans) for dynamic heterogeneous network, which core idea is to introduce transformer to integrate better neighbor information to capture network structure. The goal of HT-Trans is to infer proper embedding for existing nodes and unseen nodes. Experimental results show that the algorithm proposed in this paper is significantly competitive compared with baselines for link prediction tasks on three real datasets.

This article is concerned with the stability and stabilization of delayed discrete-time T–S fuzzy systems. The purpose is to develop less conservative stability analysis and state-feedback controller design methods. First, a matrix-separation-based inequality is proposed, which can provide a tighter estimation for the augmented summation term. Then, by constructing a delay-product-type Lyapunov–Krasovskii functional, using the proposed inequality to estimate its forward difference and using a cubic functional negative-determination lemma to handle nonconvex conditions with respect to the delay, a delay and its variation-dependent stability criterion are obtained. Moreover, the corresponding controller design method for closed-loop delayed fuzzy systems is derived via parallel distributed compensation scheme. Finally, two examples are given to demonstrate the effectiveness and merits of the proposed approaches.

This article is concerned with the distributed covariance intersection (CI) fusion estimation for cyber-physical systems (CPSs) with delayed measurements and unknown inputs. The measurement transmission is subject to random delays described by a set of independent Bernoulli processes. Based on the provided finite-length buffers, the delayed measurements are retrieved within the corresponding buffer length. By modeling the unknown inputs with a noninformative prior distribution, a local minimum mean square error (MMSE) estimator is derived in the Bayesian framework. Then this result is extended to the multiple sensor scenario, where the sequential CI fusion approach is applied to design a recursively distributed fusion estimator. It is proved that the distributed sequential CI fusion estimator is consistent and performs better than each local estimator in state estimation. An illustrative example is provided to demonstrate the effectiveness of the proposed technique.

This study considers a networked system in which the measurement suffers from one‐step delay and packet dropouts because of the unreliability of the network. A new model applied to describe the arrival conditions of the measurements is proposed. Based on the new model and using a state augmentation method, optimal linear filter, predictor and smoother are obtained. A sufficient condition for the convergence of the system is given. Finally, the simulation results show the effectiveness of the proposed algorithms.

Detecting outliers is an important problem. Most of its applications typically possess high dimensional datasets. In high dimensional space, the data becomes sparse which implies that every object can be regarded as an outlier from the point of view of similarity. Furthermore, a fundamental issue is that the notion of which objects are outliers typically varies between users, problem domains or, even, datasets. In this paper, we present a novel robust solution which detects high dimensional outliers based on user examples and tolerates incorrect inputs. It studies the behavior of projections of such a few examples, to discover further objects that are outstanding in the projection where many examples are outlying. Our experiments on both real and synthetic datasets demonstrate the ability of the proposed method to detect outliers corresponding to the user examples.