Continuous-Time Dynamic Network Embeddings

By G. Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim. 2018

646 citations recorded by the source corpus.

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, the majority of research in network representation learning has focused on static snapshots of the graph and has largely ignored the temporal dynamics of the network. In this work, we describe a general framework for incorporating temporal information into network embedding methods. The framework gives rise to methods for learning time-respecting embeddings from continuous-time dynamic networks. Overall, the experiments demonstrate the effectiveness of the proposed framework and dynamic network embedding approach as it achieves an average gain of 11.9% across all methods and graphs. The results indicate that modeling temporal dependencies in graphs is important for learning appropriate and meaningful network representations.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

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Citations in the corpus

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].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “The dataset used by Nguyen et al. [170] for their experiments had 899 nodes, and 33.7K edges along with timestamp in Unix time.”
Inductive Representation Learning on Temporal Graphs (2020, methodology): “ of graph snapshots (Li et al., 2018; Goyal et al., 2018; Rahman et al., 2018; Xu et al., 2019b), and some others work with temporally persistent node (edges) (Trivedi et al., 2018; Ma et al., 2018). Nguyen et al. (2018) proposes a node embedding method based on temporal random walk and reported sta”
Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020, background): “Our strong baselines are state-of-the-art approaches for continuous time dynamic graphs (CTDNE (47), Jodie (36), and TGAT (65)) as well as state-of-the-art models for static graphs (GAE (34), VGAE (34), DeepWalk (51), Node2Vec (23), GAT (61) and GraphSAGE (27)).”
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks (2021, background): “Network-embedding approaches may also be applied to temporal networks (Zhou et al., 2018; Du et al., 2018; Mahdavi et al., 2018; Singer et al., 2019; Nguyen et al., 2018).”
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs (2019, background): “data has emerged as an important problem in a wide range of applications, including social network analysis (Zhou et al., 2018; Trivedi et al., 2019), knowledge graph reasoning (Trivedi et al., 2017; Nguyen et al., 2018; Kazemi et al., 2019), event forecasting (Du et al., 2016), and recommender syst”
CGLB: Benchmark Tasks for Continual Graph Learning (2022, background): “Finally, the essential difference between CGL, dynamic graph learning [14, 48, 18, 60, 35, 66, 33, 13, 5], and few-shot graph learning [65, 16, 57, 43] is worth noting.”
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019, methodology): “(3) Temporalnetworkembeddingmodels: wecompare JODIE with CTDNE [33] which is the state-of-the-art in generating embeddings from temporal networks.”