Many interconnection networks can be constructed with line digraph iterations. In this paper, we will establish a general result on super line-connectivity based on the line digraph iteration which improves and generalizes several existing results in the literature.

Hyperspectral image (HSI) super-resolution aims at improving the spatial resolution of HSI by fusing a high multispectral (MSI). To preserve local submanifold structures in super-resolution, novel superpixel graph-based method is proposed. Firstly, MSI segmented into blocks to form two-directional feature tensors, then two graphs are created using spectral–spatial distance between unfolded tens...

Graphoto is a framework that automatically generates a photo or adjusts an existing one to match a line graph. Since aesthetics is an important element in visualizing personal data, Graphoto provides users with aesthetically pleasing displays for casual line graph information visualization. More specifically, after creating a line graph of the input data, a photo that resembles the input data o...

Many interconnection networks can be constructed with line digraph iterations. In this paper, we will establish a general result on super line-connectivity based on the line digraph iteration which improves and generalizes several existing results in the literature.

For a graph G, the P2-path graph, P2(G), has for vertices the set of all paths of length 2 in G. Two vertices are connected when their union is a path or a cycle of length 3. We present lower bounds on the edge-connectivity, (P2(G)) of a connected graph G and give conditions for maximum connectivity. A maximally edge-connected graph is superif each minimum edge cut is trivial, and it is optimum...

We present a dynamic data structure for the incremental construction of a planar embedding of a planar graph. The data structure supports the following Ž . operations: i testing if a new edge can be added to the embedding without Ž . introducing crossing; and ii adding vertices and edges. The time complexity of Ž . Ž . each operation is O log n amortized for edge insertion , and the memory spac...

Bayesian structure learning is the NP-hard problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian structure learning under graph theoretic restrictions on the super-structure. The super-structure (a concept introduced by Perrier, Imoto, and Miyano, JMLR 2008) is an undirec...