Analyzing and Improving Prediction of Spatiotemporal Signal Data Using Grid Search on Graph Convolutional Networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mohammadiyeh Seyyed Ali; SOLEIMANI NEYSIANI BEHZAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Analyzing and Improving Prediction of Spatiotemporal Signal Data Using Grid Search on Graph Convolutional Networks

Author(s)

Mohammadiyeh Seyyed Ali | SOLEIMANI NEYSIANI BEHZAD

Keywords

Web Mining

Traffic Prediction

Spatiotemporal

Signal Processing

Graph Neural Network (GNN)

Graph Convolutional Network (GCN)

Abstract

Spatiotemporal Signal Processing is one of the complex and hot topics, especially in Web Mining like web traffic analysis. The web pages and their links are a graph, and their content (e. g., visits) can be a signal. The PyTorch Geometric Temporal is introduced for Spatiotemporal signal mining. This study analyzes Wikipedia mathematics pages using the PyTorch Geometric Temporal library to improve their visit prediction during the time using a grid search for hyper-parameter adjustment and analyzing the effect of each parameter. The results show more than 8. 03% relative improvement for the GConvGRU algorithm versus basic related work in state-of-the-art based on about 129, 000 experiments. Besides, it should be considered that lags and node feature parameters must be the same, and lower learning rate and epochs, and higher training ratio and filter size are the best possible values.

Cites

No record.

References

No record.

Cite

APA: Copy

Mohammadiyeh, Seyyed Ali, & SOLEIMANI NEYSIANI, BEHZAD. (). . . SID. https://sid.ir/paper/1046855/en

Vancouver: Copy

Mohammadiyeh Seyyed Ali, SOLEIMANI NEYSIANI BEHZAD. . . Available from: https://sid.ir/paper/1046855/en

IEEE: Copy

Seyyed Ali Mohammadiyeh, and BEHZAD SOLEIMANI NEYSIANI, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046855/en

Related Journal Papers