Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful labeled data remaining massive unlabeled via graph. As one most popular graph-based SSL approaches, recently proposed Graph Convolutional Networks (GCNs) have gained remarkable progress by combining sound expressiveness neural networks with graph structure. Nevertheless, existing methods do not directly address ...

We present a new perspective on graph-based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ratings given by the nearest neighbors, or low-rank approximation methods using convex optimization and the nuclear norm, we formulate matrix completion as a series of semi-supervised learning problems, and propagate th...

A graph is said to be total-colored if all the edges and vertices of are colored. path in a total proper (i) any two adjacent on differ color, (ii) internal (iii) vertex differs color from its incident path. called total-proper connected by graph. For [Formula: see text], connection number denoted defined as smallest colors required make text] connected. In this paper, we study for operations. ...

Let G=(V,E) be a graph; set D⊆V is total dominating if every vertex v∈V has, at least, one neighbor in D. The domination number γt(G) the minimum cardinality among all sets. Given an arbitrary graph G, we consider some operators on this S(G),R(G), and Q(G), give bounds or exact value of these new graphs using parameters original G.

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in ...