Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey

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Time2Vec: Learning a Vector Representation of Time (2019, background): “Kazemi et al. [26] survey several such approaches for dynamic (knowledge) graphs.”
Diachronic Embedding for Temporal Knowledge Graph Completion (2019, background): “A comprehensive analysis of the existing models for interpolation and extrapolation can be found in (Kazemi et al. 2019).”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, methodology): “There are several surveys on dynamic network methods [2], [3], [8], [17]–[23].”
Knowledge Graphs (2020, unknown): “knowledge graph as constraints [82]; and contextual knowledge graph embeddings that provide numeric representations of nodes and edges that vary with time, place, and so on [67, 154].”
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019, background): “Knowledge graphs (KGs), which store real-world facts, are vital in various natural language processing applications (Bordes et al., 2013; Schlichtkrull et al., 2018; Kazemi et al., 2019).”
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs (2019, methodology): “We did not cover embedding based methods on graphs or knowledge graphs here, we refer the reader to some of the recent works [18, 19, 21, 16] 4 Methodology To provide intuition about each part of our algorithm we start with a vanilla solution to the problem.”