In this paper, we propose a method for bracing direction optimization of grid shells using a Deep Deterministic Policy Gradient (DDPG) and Graph Convolutiona ...

We propose Hard Directional Graph Networks (HDGN), a point cloud model that both learns directional weight matrices and assigns a single matrix to each neighbor, achieving directional convolutions at ...

The adjacency matrix is a square matrix used to represent a finite graph, with rows and columns labeled by the graph's vertices. In the directed adjacency matrix, the entry in the ith row and jth ...

Direction - relationship between two nodes only applies one way, it's important to make sure all directional graphs are DAG (Directed Acyclic Graph) Connectivity - measures the minimum number of edges ...

We present an algorithm to estimate a single entry of the inverse of a matrix; it is derived using a bidirectional search on a flow graph. This method has immediate application in analyzing dynamical ...

Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among ...

Finally, lncRNA similarity, protein similarity, and LPI matrix were integrated to the weight graph-regularized matrix factorization model for computing the association scores for each lncRNA–protein ...

The graph below shows the total number of publications each year in Quantum Graphs and Random Matrix Theory in Chaotic Systems.