Publication Type : Conference Proceedings
Publisher : Springer
Source : Lecture Notes in Networks and Systems, 2023
Url : https://link.springer.com/chapter/10.1007/978-981-19-5331-6_41
Campus : Amritapuri
School : School of Computing
Center : Computational Bioscience
Department : Computer Science
Year : 2023
Abstract : This research shows how to find missing or soon-to-appear links using a community-based link prediction approach. In the large-scale distribution of information, online social networks play a key role. Many attempts have been made to understand this phenomenon, ranging from the detection of hot topics to the modelling of information distribution, including the identification of influential spreaders. Using a community-based link prediction method, missing links were identified based on an information diffusion algorithm. First, we show when using a community discovery approach to split the network into clusters. Afterwards when a unique approach based on information diffusion and community structure is presented to anticipate target links. Finally, to improve the accuracy of the current approach, we are using the graph convolution network
Cite this Research Publication : Samuel, L., Ashok, A., " Missing Link Prediction in the Social Network Using Graph Convolutional Networks," Lecture Notes in Networks and Systems, 2023, ICT Infrastructure and Computing pp 399–407
Publisher : Springer