DyRep: Learning Representations over Dynamic Graphs

By Rakshit S. Trivedi, Mehrdad Farajtabar, P. Biswal, Hongyuan Zha. 2019

678 citations recorded by the source corpus.

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Citations in the corpus

Learning to Simulate Complex Physics with Graph Networks (2020, background): “…et al., 2019; Sanchez-Gonzalez et al., 2019), as well as non-physical systems, such as multi-agent dynamics (Tacchetti et al., 2018; Sun et al., 2019), algorithm execution (Velikovi et al., 2020), and other dynamic graph settings (Trivedi et al., 2019; 2017; Yan et al., 2018; Manessi et al., 2020).”
Recurrent Event Network for Reasoning over Temporal Knowledge Graphs (2019, background): “Representation learning on dynamically evolving graph-structured data has emerged as an important machine learning task in a wide range of applications, such as social network analysis [16], question answering [25, 26], and event forecasting [22, 23].”
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks (2020, background): “DyREP (Trivedi et al. 2018) divides the dynamic graph network into two separate processes of global and local topological evolution, and proposes a two-time scale deep temporal point process to jointly model the two processes.”
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019, methodology): “Recent attempts to solve the extrapolation TKG reasoning problem are Know-Evolve (Trivedi et al., 2017) and its extension DyRep (Trivedi et al., 2019), which predict future events assuming ground truths of the preceding events are given at inference time.”
Incremental Training of Graph Neural Networks on Temporal Graphs under Distribution Shift (2020, background): “DyRep [27] extend this approach to better capture temporal dynamics.”
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning (2021, methodology): “For Know-evolve and DyRep, RE-NET extends them to the temporal reasoning task but does not release their codes.”
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021, methodology): “Know-E (Trivedi et al., 2017), DyRep (Trivedi et al., 2019) and JODIE (Kumar et al., 2019) use RNNs to propagate messages across interactions to update node representations.”
Diachronic Embedding for Temporal Knowledge Graph Completion (2019, background): “A related problem is the extrapolation problem where future interactions are to be predicted (see, e.g., [56, 33, 57]).”
Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020, background): “Moreover, only few of these approaches support the inductive setting of generalizing to new nodes not seen during training (46; 3; 59; 36).”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “Recently there has been growing interest towards time predicting decoders [217, 216, 261].”