A square difference 3-equitable labeling of a graph G with vertex set V is a bijection f from V to {1, 2,.... | V | } jg such that if each edge uv is assigned the label -1 if |[f (u)]2 [f (v)]2 | = -1(mod 4), the label 0 if |[f (u)]2 [f (v)]2 | = 0(mod 4) and the label 1 if |[f (u)]2 [f (v)]| = 1(mod 4), then the number of edges labeled with i and the number of edges labelled with j differ by a...

Abstract. A divisor cordial labeling of a graph G with vertex set V is a bijection f from V to {1, 2,... | |} V such that an edge uv is assigned the label 1 if ( ) | ( ) f u f v or ( ) | ( ) f v f u and the label 0 otherwise, then number of edges labeled with 0 and the number of edges labeled with 1 differ by at most 1. A graph with a divisor cordial labeling is called a divisor cordial graph. ...

This paper is a contribution to the study of the general problem of characterizing those properties which can be computed on a graph or a network by means of local transformations. By using an abstract model based on graph relabelling systems we consider the majority problem : let G be a graph whose vertices have label A or B ; we say that label A has the majority if the number of A-labelled ve...

Image annotation as well as classification are both critical and challenging work in computer vision research. Due to the rapid increasing number of images and inevitable biased annotation or classification by the human curator, it is desired to have an automatic way. Recently, there are lots of methods proposed regarding image classification or image annotation. However, people usually treat t...

This paper is a contribution to the study of the general problem of characterizing those properties which can be computed on a graph or a network by means of local transformations. By using an abstract model based on graph relabelling systems we consider the majority problem : let G be a graph whose vertices have label A or B ; we say that label A has the majority if the number of A-labelled ve...

When visualizing graphs, it is essential to communicate the meaning of each graph object via text or graphical labels. Automatic placement of labels in a graph is an NP-Hard problem, for which efficient heuristic solutions have been recently developed. In this paper, we describe a general framework for modeling, drawing, editing, and automatic placement of labels respecting user constraints. In...

Labeled graphs provide a natural way of representing entities, relationships and structures within real datasets such as knowledge graphs and protein interactions. Applications such as question answering, semantic search, and motif discovery entail efficient approaches for subgraph matching involving both label and structural similarities. Given the NP-completeness of subgraph isomorphism and t...

Single labeled biometric recognition is one of the main challenges to graph-based transductive classification learning. To enhance the recognition rate of single labeled problem, sparse representation provides a feasible strategy for representation learning. In this paper, we developed a power l1-graph learning technique for semi-supervised learning, called label propagation by power l1-graph (...

Many web-based application areas must infer label distributions starting from a small set of sparse, noisy labels. Examples include searching for, recommending, and advertising against image, audio, and video content. These labeling problems must handle millions of interconnected entities (users, domains, content segments) and thousands of competing labels (interests, tags, recommendations, top...

Graph querying is crucial to fully exploit the knowledge within the widely used graph datasets. However, graph datasets are usually noisy which makes the approximate graph matching tools favored to overcome restrictive query answering. In this paper, we introduce a new framework of approximate graph matching based on aggregated search called Label and Structure Similarity Aggregated Search (LaS...