Link Prediction in Facebook using Web Scrapping and Deep Learning Techniques
Journal: International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) (Vol.10, No. 1)Publication Date: 2021-02-15
Authors : Kalpana Prajapati Harshal Shah Rutvik Mehta;
Page : 194-197
Keywords : Graph Convolution Network; Link Prediction; Random forest; Social network;
Abstract
As Internet technologies develop continuously social networks are getting more popular day by day. People are connected with each other via virtual applications. Using the Link Prediction in social networks more people get connected, may be they are friends, may be work together at the same workplace and may be their education are. Machine learning techniques are used to analyze the link between the nodes of the network and also create a better link prediction model through deep learning. The objective of this research is to measure the performance using the different techniques to predict link between the social networks. Using deep learning, feature engineering can be reduced for link prediction. In this research, the feature based learning is used to predict the link for better performance. Dataset is obtained by scraping the profile of Facebook users and they are used along with the random forest and graph convolution neural network to measure the performance of link prediction in social networks
Other Latest Articles
- Quality assessment of the cloud-oriented environment for flipped learning of the future IT specialists
- IoT for a More Reliable and Safer Patient Monitoring Healthcare Service during the Pandemic
- Exploring applications of Frozen Newton Method in the design of digital filter
- Monitoring and Tracking Group Traveler Application Using GPS Technology
- Ontology-Based Transformation and Verification of UML Qualified Association
Last modified: 2021-02-18 19:45:26