Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
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"Foundations and Modeling of Dynamic..." refers background in this paper
...where LSTM is a normal LSTM [87] and Vp ∈ Rn is defined as Vp = δpi where δ is the Kronecker delta....
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38,211 citations
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...Available: http://arxiv.org/abs/1906.01529 [106] K. Lei, M. Qin, B. Bai, G. Zhang, and M. Yang, ‘‘GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks,’’ in Proc....
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...GCN-GAN [106] and DynGraphGAN [107] are two such models....
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...GCN-GAN use a stacked DGNN as a generator and a dense feed-forward networks as a discriminator [106] and DynGraphGAN use a shallow generator and a GCN [75] stacked with a CNN as a discriminator [107]....
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...These include: PATCHY-SAN, DyGGNN, RgGNN, StrGNN, EvolveGCN, JODIE, GC-LSTM, GCN-GAN, DynGraphGAN and DyREP. appearing and existing links disappearing....
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...Generative adversarial networks (GAN) [104] have proven to be very successful in the computer vision field [105]....
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33,771 citations
"Foundations and Modeling of Dynamic..." refers background in this paper
...Many models define rules for how links are established [29], [30]....
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...models such as preferential attachment [29], forest fire [30] and GraphRNN [31]....
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15,696 citations