Graph-based semi-supervised learning (GSSL) is an important paradigm among approaches and includes the two processes of graph construction label inference. In most traditional GSSL methods, are completed independently. Once constructed, result inference cannot be changed. Therefore, quality directly determines GSSL’s performance. Most methods make certain assumptions about data distribution, re...

the dispersibility of graphene is modeled as a mathematical function of the molecular structure of solvent represented by simplified molecular input-line entry systems (smiles) together with the graph of atomic orbitals (gao). the gao is molecular graph where atomic orbitals e.g. 1s1, 2p4, 3d7 etc., are vertexes of the graph instead of the chemical elements used as the graph vertexes in the tra...

Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects performance on node classification tasks. We analyze that an alternative method, label propagation algorithm (LPA), avoids aforementioned problems thus it ...

This work is aimed at exploring semi-supervised learning techniques to improve the performance of Automatic Speech Recognition systems. Semi-supervised learning takes advantage of unlabeled data in order to improve the quality of the representations extracted from the data. The proposed model is a neural network where the weighs are updated by minimizing the weighted sum of a supervised and an ...

Semi-stremining model. The title of the paper is “Graph Sparsification in the Semi-streaming Model”. First, we should note that it says semi-streaming and not streaming. In graph problems there is a linear space lower bound for even the simple problems such as determining the connectedness of a graph. In other words, we have to store at least all vertices of the graph. Because of that, this pap...

We observe that ω(G) + χ(S( G)) = n = ω(S( G)) + χ(G) for any graph G with n vertices, where G is any acyclic orientation of G and where S( G) is the (complement of the) auxiliary line graph introduced in [1]. (Where as usual, ω and χ denote the clique number and the chromatic number.) It follows that, for any graph parameter β(G) sandwiched between ω(G) and χ(G), then Φβ( G) := n−β(S( G)) is s...