Learning dynamic embeddings for temporal attributed networks

Xie Luodi, Hui Tian, Hong Shen

Research output: Contribution to journalArticlepeer-review

Abstract

Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.

Original languageEnglish
Article number111308
JournalKnowledge-Based Systems
Volume286
DOIs
Publication statusPublished - 28 Feb 2024
Externally publishedYes

Keywords

  • Bayesian inference
  • Dynamic link prediction
  • Node classification
  • Temporal network embedding
  • Variational inference

Fingerprint

Dive into the research topics of 'Learning dynamic embeddings for temporal attributed networks'. Together they form a unique fingerprint.

Cite this