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The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 2 (2023) · pp. 231–248

DOI
10.5614/itbj.ict.res.appl.2023.17.2.7
ISSN
2338-5499, 2337-5787
Published
2023-09-13
Publisher
DRI - ITB
Language
eng

Abstract

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved.

Keywords

Collaboration network of the authors

29 authors · 51 co-authorship links · 7 across different organisations

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  • Bandung Institute of Technology
  • Indonesia University of Education
  • HAN University of Applied Sciences
  • Parahyangan Catholic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dwi Hendratmo WidyantoroBandung Institute of Technology5
Bambang Riyanto TrilaksonoBandung Institute of Technology3
E. Aminudin AzizIndonesia University of Education3
Bayu HendradjayaBandung Institute of Technology3
Fransiskus AdikaraBandung Institute of Technology2
Muhammad Haris MaulanaBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Fazat Nur AzizahBandung Institute of Technology1
Guido P. BakemaHAN University of Applied Sciences1
Oerip S. SantosoBandung Institute of Technology1
Jan Wira Gotama PutraBandung Institute of Technology1
Ghoziyah Haitan RachmanBandung Institute of Technology1
Carmadi MachbubBandung Institute of Technology1
Wahyudi WahyudiBandung Institute of Technology1
Ary Setijadi PrihatmantoBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1
Bariza HaqiBandung Institute of Technology1
Fitra ArifiansyahBandung Institute of Technology1
Muhammad Zuhri Catur CandraBandung Institute of Technology1
Veronica Sri MoertiniParahyangan Catholic University1
Oerip S. SantosaAffiliation unknown1
Regina Yulia YasminBandung Institute of Technology1
Putri SaptawatiBandung Institute of Technology1
Mohamad Syafri TuloliBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">The Evaluation of DyHATR Performance for  Dynamic Heterogeneous Graphs</dc:title>
	<dc:creator>Al Ghifari, Nasy`an Taufiq</dc:creator>
	<dc:creator>Saptawati, Gusti Ayu Putri</dc:creator>
	<dc:creator>Khodra, Masayu Leylia</dc:creator>
	<dc:creator>Sitohang, Benhard</dc:creator>
	<dc:subject xml:lang="en-US">dynamic</dc:subject>
	<dc:subject xml:lang="en-US">graph mining</dc:subject>
	<dc:subject xml:lang="en-US">heterogeneous</dc:subject>
	<dc:subject xml:lang="en-US">link prediction</dc:subject>
	<dc:subject xml:lang="en-US">performance evaluation</dc:subject>
	<dc:description xml:lang="en-US">Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/20502</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.17.2.7</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 2 (2023); 231-248</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.17.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/20502/6314</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of ICT Research and Applications</dc:rights>
</oai_dc:dc>