Abstract
Network Representation Learning (NRL) has been widely used to analyze networks by mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new NRL framework, called Event2vec, for HINs to consider both quantities and properties of relations during the representation learning process. Specifically, an event (i.e., a complete semantic unit) is used to represent the relation among multiple objects, and both event-driven first-order and second-order proximities are defined to measure the object relevance according to the quantities and properties of relations. We theoretically prove how event-driven proximities can be preserved in the embedding space by Event2vec, which utilizes event embeddings to facilitate learning the object embeddings. Experimental studies demonstrate its advantages over state-of-the-art algorithms on four real-world datasets and two network analysis tasks (i.e., link prediction and node classification).
Original language | English |
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Title of host publication | Neural Information Processing : 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part I |
Editors | Tom GEDEON, Kok Wai WONG, Minho LEE |
Publisher | Springer |
Pages | 327-339 |
Number of pages | 13 |
ISBN (Electronic) | 9783030367084 |
ISBN (Print) | 9783030367077 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 26th International Conference on Neural Information Processing - Sydney, Australia Duration: 12 Dec 2019 → 15 Dec 2019 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11953 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Name | Theoretical Computer Science and General Issues |
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Publisher | Springer |
ISSN (Print) | 2512-2010 |
ISSN (Electronic) | 2512-2029 |
Conference
Conference | 26th International Conference on Neural Information Processing |
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Abbreviated title | ICONIP 2019 |
Country/Territory | Australia |
City | Sydney |
Period | 12/12/19 → 15/12/19 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2019.
Funding
This work was supported by National Key R&D Program of China (Grant No. 2017YF-C0804003), National Natural Science Foundation of China (Grant No. 61976111), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. JCYJ20170817112421757 and JCYJ20180504165652917) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).
Keywords
- Heterogeneous information networks
- Network analysis
- Network embedding