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Pattern recognition, 2023-04, Vol.136, p.109236, Article 109236
2023
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Autor(en) / Beteiligte
Titel
Compact network embedding for fast node classification
Ist Teil von
  • Pattern recognition, 2023-04, Vol.136, p.109236, Article 109236
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2023
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • •Discrete network embedding (DNE) is proposed for compact representation. DNE leverages hash code to represent node, and dramatically reduces computational and storage costs.•Deep discrete attributed network embedding (DDANE) is proposed to effectively leverage node attribute and network structure in attributed network. The proposed DDANE trains an improved graph convolutional network autoencoder to encode node attribute and network structure into latent discrete embedding.•Extensive experiments demonstrate the proposed methods exhibit lower storage and computational complexity than state-of-the-art network embedding methods, and achieve satisfactory accuracy. Network embedding has shown promising performance in real-world applications. The network embedding typically lies in a continuous vector space, where storage and computation costs are high, especially in large-scale applications. This paper proposes more compact representation to fulfill the gap. The proposed discrete network embedding (DNE) leverages hash code to represent node in Hamming space. The Hamming similarity between hash codes approximates the ground-truth similarity. The embedding and classifier are jointly learned to improve compactness and discrimination. The proposed multi-class classifier is further constrained to be discrete to expedite classification. In addition, this paper further extends DNE and proposes deep discrete attributed network embedding (DDANE) to learn compact deep embedding from more informative attributed network. From the perspective of generalized signal smoothing, the proposed DDANE trains an improved graph convolutional network autoencoder to effectively leverage node attribute and network structure. Extensive experiments on node classification demonstrate the proposed methods exhibit lower storage and computational complexity than state-of-the-art network embedding methods, and achieve satisfactory accuracy.
Sprache
Englisch
Identifikatoren
ISSN: 0031-3203
eISSN: 1873-5142
DOI: 10.1016/j.patcog.2022.109236
Titel-ID: cdi_crossref_primary_10_1016_j_patcog_2022_109236

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