Research Article
NODDLE: Node2vec Based Deep Learning Model for Link Prediction
@INPROCEEDINGS{10.1007/978-3-031-33614-0_14, author={Kazi Zainab Khanam and Aditya Singhal and Vijay Mago}, title={NODDLE: Node2vec Based Deep Learning Model for Link Prediction}, proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings}, proceedings_a={BDTA}, year={2023}, month={5}, keywords={Graph learning Social networks Link prediction Web information systems}, doi={10.1007/978-3-031-33614-0_14} }- Kazi Zainab Khanam
Aditya Singhal
Vijay Mago
Year: 2023
NODDLE: Node2vec Based Deep Learning Model for Link Prediction
BDTA
Springer
DOI: 10.1007/978-3-031-33614-0_14
Abstract
Computing the probability of an edge’s existence in a graph network is known as link prediction. While traditional methods calculate the similarity between two given nodes in a static network, recent research has focused on evaluating networks that evolve dynamically. Although deep learning techniques and network representation learning algorithms, such as node2vec, show remarkable improvements in prediction accuracy, the Stochastic Gradient Descent (SGD) method of node2vec tends to fall into a mediocre local optimum value due to a shortage of prior network information, resulting in failure to capture the global structure of the network. To tackle this problem, we propose NODDLE (integration of NOde2vec anD Deep Learning mEthod), a deep learning model which incorporates the features extracted by node2vec and feeds them into a four layer hidden neural network. NODDLE takes advantage of adaptive learning optimizers such as Adam, Adamax, Adadelta, and Adagrad to improve the performance of link prediction. Experimental results show that this method yields better results than the traditional methods on various social network datasets.
