Co-embedding attributed networks with external knowledge

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node repr...

Full description

Saved in:
Bibliographic Details
Main Authors: LO, Pei-chi, LIM, Ee-peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6118
https://ink.library.smu.edu.sg/context/sis_research/article/7121/viewcontent/BIGDATA_CO_EMBEDDING_ATTRIBUTED_NETWORKS_WITH_EXTERNAL_KNOWLEDGE.PDF
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7121
record_format dspace
spelling sg-smu-ink.sis_research-71212021-09-29T12:25:41Z Co-embedding attributed networks with external knowledge LO, Pei-chi LIM, Ee-peng Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of attribute semantics from different sources, we introduce a mixed model variant which has a twolayer encoder structure. Our experiments on three real-world datasets show that ECAN out-performs previous approaches in both node classification and link prediction tasks. 2020-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6118 https://ink.library.smu.edu.sg/context/sis_research/article/7121/viewcontent/BIGDATA_CO_EMBEDDING_ATTRIBUTED_NETWORKS_WITH_EXTERNAL_KNOWLEDGE.PDF http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Attributed Networks Network Embedding Knowledge Graph Computer Sciences
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Attributed Networks
Network Embedding
Knowledge Graph
Computer Sciences
spellingShingle Attributed Networks
Network Embedding
Knowledge Graph
Computer Sciences
LO, Pei-chi
LIM, Ee-peng
Co-embedding attributed networks with external knowledge
description Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of attribute semantics from different sources, we introduce a mixed model variant which has a twolayer encoder structure. Our experiments on three real-world datasets show that ECAN out-performs previous approaches in both node classification and link prediction tasks.
format text
author LO, Pei-chi
LIM, Ee-peng
author_facet LO, Pei-chi
LIM, Ee-peng
author_sort LO, Pei-chi
title Co-embedding attributed networks with external knowledge
title_short Co-embedding attributed networks with external knowledge
title_full Co-embedding attributed networks with external knowledge
title_fullStr Co-embedding attributed networks with external knowledge
title_full_unstemmed Co-embedding attributed networks with external knowledge
title_sort co-embedding attributed networks with external knowledge
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/6118
https://ink.library.smu.edu.sg/context/sis_research/article/7121/viewcontent/BIGDATA_CO_EMBEDDING_ATTRIBUTED_NETWORKS_WITH_EXTERNAL_KNOWLEDGE.PDF
_version_ 1770575832808423424