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Kulkarni, Sushil
- Graph Neural Network Learning in Large Graphs - A Critical Review
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Authors
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1 Department of Computer Science, University of Mumbai, IN
1 Department of Computer Science, University of Mumbai, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2416-2423Abstract
Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.Keywords
Graph Neural Networks, Graph Convolutional Networks, Graph Representation Learning, Large Graph Dataset.
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- Automatic Generation Of Parameters In Density-based Spatial Clustering
Abstract Views :170 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, University of Mumbai, IN
1 Department of Computer Science, University of Mumbai, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 2 (2022), Pagination: 2533-2539Abstract
As a result of emerging new techniques for scientific way of collecting data, we are able to accumulate data in large scale pertaining to various fields. One such method of data mining is Cluster analysis. Of all clustering algorithms, density-based clustering is better in terms of clustering quality and the way the data are handled. Density based clustering is advantageous over other clustering algorithms in the following ways – arbitrary shaped clusters are formed; number of clusters need not be known and noise is handled. However, there are two main points that are critical in density-based clustering. Firstly, it is not effective while handling datasets of varied density. Secondly, the selection of input parameters ε and Min Pts play a critical role in the quality of clustering. This paper proposes a model – Automatic Generation of Parameters in Density-Based Spatial Clustering (AGPDBSCAN) that aims at improving the density-based clustering by generating different candidate parameters. With these candidates, we will be able to handle both uniform density and varied density datasets. The results of experiments also look promising for different clustering datasets.Keywords
Clustering Algorithms, Density-based Clustering, Density Parameters, Generation of ParametersReferences
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