This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs. Meanwhile the approximate calculation of fractional order gradients also overcomes the high computational complexity of fractional order derivations. We further prove that such an approximation is feasible and the FGNN is unbiased towards global optimization solution. Extensive experiments on citation networks show that FGNN achieves great advantage over baseline models when selected appropriate fractional order.
Graph Embedding in the Graph Fractional Fourier Transform Domain
School of Mathematics and Statistics, Nanjing University of Information Science and Technology（南京信息工程大学数学与统计学学院）；Hubei Key Laboratory of Applied Mathematics, Hubei University（湖北省应用数学重点实验室）；Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University（教育部系统控制与信息处理重点实验室，上海交通大学）；Nanjing Institute of Technology（南京理工大学）；Jiangsu Province Engineering Research Center of IntelliSense Technology and System, Nanjing（江苏省智能感知技术与系统工程研究中心，南京）
Sichuan University（四川大学）；China University of Petroleum（中国石油大学）
This paper offers a novel mathematical approach, the modified Fractional-order Steepest Descent Method (FSDM) for training BackPropagation Neural Networks (BPNNs); this differs from the majority of the previous approaches and as such. A promising mathematical method, fractional calculus, has the potential to assume a prominent role in the applications of neural networks and cybernetics because of its inherent strengths such as long-term memory, nonlocality, and weak singularity. Therefore, to improve the optimization performance of classic first-order BPNNs, in this paper we study whether it c…
Beijing Jiaotong University（北京交通大学）；University of Science and Technology of China（中国科学技术大学）；Anhui University（安徽大学）；Nanyang Technological University（南洋理工大学）；Lanzhou University（兰州大学）
University of Memphis（孟菲斯大学）；Department of Mathematical Sciences（数学科学系）；Memphis, TN 38152, U.S.A.（美国田纳西州孟菲斯市 38152）
Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by an algebraic sigmoid function. The approximations are pointwise and of the unifor…
Central China Normal University（华中师范大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
This paper introduces a novel algorithmic framework for a deep neural network (DNN), which in a mathematically rigorous manner, allows us to incorporate history (or memory) into the network -- it ensures all layers are connected to one another. This DNN, called Fractional-DNN, can be viewed as a time-discretization of a fractional in time nonlinear ordinary differential equation (ODE). The learning problem then is a minimization problem subject to that fractional ODE as constraints. We emphasize that an analogy between the existing DNN and ODEs, with standard time derivative, is well-known by …
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
In this paper, we firstly extend the Fourier neural operator (FNO) to discovery the soliton mapping between two function spaces, where one is the fractional-order index space $\{ε|ε\in (0, 1)\}$ in the fractional integrable nonlinear wave equations while another denotes the solitonic solution function space. To be specific, the fractional nonlinear Schrödinger (fNLS), fractional Korteweg-de Vries (fKdV), fractional modified Korteweg-de Vries (fmKdV) and fractional sine-Gordon (fsineG) equations proposed recently are studied in this paper. We present the train and evaluate progress by recording…
