2019
DOI: 10.1145/3333028
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Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Abstract: Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the futur… Show more

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Cited by 16 publications
(8 citation statements)
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References 41 publications
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“…Other recent algorithms such as DDNE [30], DANE [29], DynGem [18], Zhu et al [52], and Rahman et al [40] learn embeddings from a sequence of graph snapshots, which is not applicable to our setting of continuous interaction data. Recent models such as NP-GLM model [41], DGNN [33], and DyRep [44] learn embeddings from persistent links between nodes, which do not exist in interaction networks as the edges represent instantaneous interactions. Our proposed model, JODIE overcomes these shortcomings by generating dynamic user and item embeddings.…”
Section: Related Workmentioning
confidence: 99%
“…Other recent algorithms such as DDNE [30], DANE [29], DynGem [18], Zhu et al [52], and Rahman et al [40] learn embeddings from a sequence of graph snapshots, which is not applicable to our setting of continuous interaction data. Recent models such as NP-GLM model [41], DGNN [33], and DyRep [44] learn embeddings from persistent links between nodes, which do not exist in interaction networks as the edges represent instantaneous interactions. Our proposed model, JODIE overcomes these shortcomings by generating dynamic user and item embeddings.…”
Section: Related Workmentioning
confidence: 99%
“…Embedding (DHENE) HA-LSTM [17] multiple single Yes change2vec [1] multiple multiple Yes NP-GLM [26] multiple multiple Yes DHNE [38] multiple multiple Yes…”
Section: Dynamic Heterogeneous Networkmentioning
confidence: 99%
“…There exists four dynamic heterogeneous network embedding methods, Meta-DynaMix [21], change2vec [1], DHNE [38] and NP-GLM [26]. Here, we mainly focus on DHNE and NP-GLM method.…”
Section: Baselinesmentioning
confidence: 99%
See 1 more Smart Citation
“…Networks of interest can be social, biological, informational, or technological. Link prediction is the task of identifying links missing from a network ( Lü & Zhou, 2011 ; Martínez, Berzal & Cubero, 2017 ; Guimerà & Sales-Pardo, 2009 ; Al Hasan et al, 2006 ; Guimerà & Sales-Pardo, 2009 ; Clauset, Moore & Newman, 2008 ; Lü & Zhou, 2011 ; Cannistraci, Alanis-Lobato & Ravasi, 2013 ; Daminelli et al, 2015 ; Al Hasan et al, 2006 ; Wang, Satuluri & Parthasarathy, 2007 ; Zhang et al, 2020 ; Beigi, Tang & Liu, 2020 ; Sajadmanesh et al, 2019 ; Makarov et al, 2019 ), a problem with important applications, such as the reconstruction of networks from partial observations ( Guimerà & Sales-Pardo, 2009 ), recommendation of items in online shops and friends in social networks ( Al Hasan et al, 2006 ), and the prediction of interactions in biological networks ( Clauset, Moore & Newman, 2008 ).…”
Section: Introductionmentioning
confidence: 99%