Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150188
Title: Representation learning on heterogenous information networks
Authors: Chen, Xiaoyu
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Chen, X. (2021). Representation learning on heterogenous information networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150188
Project: A3048-201
Abstract: In real world, most of the information networks are heterogeneous in nature, which contains different types of nodes and relationships. Representation learning or feature learning techniques are needed to extract features of these Heterogeneous Information Networks (HIN) and convert them to low dimensional vectors such that they can be used as input to machine learning models to perform machine learning tasks. Current graph embedding methods have the limitations on either considering singular types of nodes and relationships or losing important node features due to ignoring node contents or relationships between nodes. In this project, an advanced graph embedding technique, Metapath Aggregated Graph Neural Network (MAGNN), is studied. With the idea of metapath, which captures the relationships between node types, and graph neural network, a powerful graph embedding model based on deep learning, MAGNN aims to address these problems and generate node embedding with more structural and semantic information of HIN. Empirical studies with more benchmark datasets are conducted to investigate the effectiveness of MAGNN model. The results are useful for comparison with the state-of-the art baselines.
URI: https://hdl.handle.net/10356/150188
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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