An edge magic total labeling of a graph with p vertices and q edges is a bijection from the set of vertices and edges to 1, 2, . . . , p+ q such that for every edge the sums of the label of the edge and the label of its two end vertices are constant. Otherwise if the sum is distinct, it is said to be an edge-antimagic total labeling. A graph is called edgeantimagic if it admits edge-antimagic t...

A Sum divisor cordial labeling of a graph G with vertex set V is bijection r from to {1,2,3,...,|V (G )|} such that an edge uv assigned the label 1 if 2 divides r(u)+ (v ) and 0 otherwise; number edges labeled 1differ by at most . sum called graph. In this research paper, we investigate bahevior for Grötzsch graph, fusion any two vertices in duplication arbitrary switching degree four three pat...

Graph embedding provides an ecient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embeddings can be processed eciently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be alwa...

The graph exploration problem is to visit all the nodes of a connected graph by a mobile entity, e.g., a robot. The robot has no a priori knowledge of the topology of the graph or of its size. Cohen et al. [3] introduced label guided graph exploration which allows the system designer to add short labels to the graph nodes in a preprocessing stage; these labels can guide the robot in the explora...

Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the dataset. It is often desirable for the constructed graph to satisfy two properties: first, data points that are similar in the feature space should be strongly connected on the graph, and second, the class label informati...

In the article “Computing the vertex separation of unicyclic graphs”, Information and Computation 192, pp. 123–161, 2004, Ellis et al. proposed an O(n log n) algorithm for computing both the vertex separation and an optimal layout of a unicyclic graph with n vertices. Using the data structures label and label array, we improve the time complexity of their algorithm to O(n).

Multi-relational learning on knowledge graphs infers high-order relations among the entities across graphs. This task can be solved by label propagation tensor product of to learn as a tensor. In this paper, we generalize widely used model normalized graph, and propose an optimization formulation scalable Low-rank Tensor-based Label Propagation algorithm (LowrankTLP) infer multi-relations for t...

In this paper, we present a new method for refining image annotation by integrating probabilistic latent semantic analysis (PLSA) with random walk (RW) model. First, we construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotating keywords for an image, and then a label similarity graph is constructed by a weighted linear combination of label simil...

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural network architectures, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training objective for neural networks, Neural Graph Machines, for combining the power of neural networks and label propagation. The new objective allows the neural netw...

This paper introduces a graph-based algorithm for sequence data labeling, using random walks on graphs encoding label dependencies. The algorithm is illustrated and tested in the context of an unsupervised word sense disambiguation problem, and shown to significantly outperform the accuracy achieved through individual label assignment, as measured on standard senseannotated data sets.