Multi-View tensor factorization for dynamic attributed graph embedding with graph-aware features

Zhongjing Yu

doi:10.1016/j.patcog.2026.114935

Multi-View tensor factorization for dynamic attributed graph embedding with graph-aware features

2026-09-22

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DOI：10.1016/j.patcog.2026.114935

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• MV-TF: unsupervised multi-view tensor factorization for dynamic attributed graphs. • A lightweight graph-aware view bridges topology and attributes without supervision. • A sparse subspace constraint encourages community-structured node representations. • Experiments on five real-world datasets verify statistically significant gains.

Keywords:

Graph-aware feature tensor

Dynamic graph embeddings

Tensor decomposition

Multi-view learning

Subspace learning

Node classification

Multi-View tensor factorization for dynamic attributed graph embedding with graph-aware features

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