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Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Representation Learning on Multi-layered Heterogeneous Network

Authors : Delvin Ce Zhang, Hady W. Lauw

Published in: Machine Learning and Knowledge Discovery in Databases. Research Track

Publisher: Springer International Publishing

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Abstract

Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along homogeneous nodes to explore latent structural similarities. Cross-layer proximity captures network semantics by extending heterogeneous neighborhood across layers. Through extensive experiments on four datasets, we demonstrate that our model achieves substantial gains in different real-world domains over state-of-the-art baselines.

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previous chapter Inductive Link Prediction with Interactive Structure Learning on Attributed Graph
next chapter Adaptive Node Embedding Propagation for Semi-supervised Classification
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Metadata
Title
Representation Learning on Multi-layered Heterogeneous Network
Authors
Delvin Ce Zhang
Hady W. Lauw
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases. Research Track

Print ISBN: 978-3-030-86519-1

Electronic ISBN: 978-3-030-86520-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-86520-7_25

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