Semi-supervised learning gets estimated marginal distribution X P with a large number of unlabeled examples and then constrains the conditional probability ) | ( x y p with a few labeled examples. In this paper, we focus on a regularization approach for semi-supervised classification. The label information graph is first defined to keep the pairwise label relationship and can be incorporated wi...

A {em Roman dominating function} on a graph $G$ is a function$f:V(G)rightarrow {0,1,2}$ satisfying the condition that everyvertex $u$ for which $f(u) = 0$ is adjacent to at least one vertex$v$ for which $f(v) =2$. {color{blue}A {em restrained Roman dominating}function} $f$ is a {color{blue} Roman dominating function if the vertices with label 0 inducea subgraph with no isolated vertex.} The wei...

Graph-based semi-supervised learning improves classification by combining labeled and unlabeled data through label propagation. It was shown that the sparse representation of graph by weighted local neighbors provides a better similarity measure between data points for label propagation. However, selecting local neighbors can lead to disjoint components and incorrect neighbors in graph, and thu...

This paper investigates graph-based approaches to labeled topic clustering of reader comments in online news. For graph-based clustering we propose a linear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract to...

A ranking on a graph is an assignment of positive integers to its vertices such that any path between two vertices with the same label contains a vertex with a larger label. The rank number of a graph is the fewest number of labels that can be used in a ranking. The rank number of a graph is known for many families, including the ladder graph P2 × Pn. We consider how ”bending” a ladder affects ...

Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. Propagation kernels leverage the power of continuous node label distributions as graph features and hence, enhance traditional graph kernels to ...

The problem of finding a low-complexity Tanner graph for a general lattice A is studied. The problem is divided into two subproblems: 1) Finding an orthogonal sublattice A‘ of A which minimizes the complexity of the label code of the quotient group A/A’. 2) Constructing a simple Tanner graph for the label code obtained in part 1. The proposed approach for solving subproblem 2 can also be applie...

We consider the complexity of the emptiness problem for various classes of graph languages deened by eNCE (edge label neighborhood controlled embedding) graph grammars. In particular, we show that the emptiness problem is undecidable for general eNCE graph grammars, DEXPTIME-complete for connuent and boundary eNCE graph grammars, PSPACE-complete for linear eNCE graph grammars, NL-complete for d...

This appendix presents additional discussion on several aspects of the proposed algorithm. Sec. A presents a modification of our algorithm which enables users to reflect local connectivity in link prediction. The remaining sections focus on the label propagation application. Sec. B and C discuss functionalities of active label acquisition and adding new images to the match graph while Sec. D di...

Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing w...