Embedding Propagation over Heterogeneous Information Networks


Heterogeneous Information Networks (HINs) play a crucial role in modeling and analyzing multimedia systems and heterogeneous data. They provide a comprehensive understanding of entities and relationships within complex data structures. However, integrating HINs with machine learning tasks poses challenges that require specific models or vector space representation. This paper proposes an innovative embedding propagation graph method for HINs with textual data. By leveraging language models like BERT, our method propagates contextual text embeddings, combining the network’s topological information and the semantic information of textual objects, which are then propagated to non-textual objects within the network. The method facilitates the integration of machine learning techniques with various modeling approaches, enhancing analysis capabilities in multimedia and heterogeneous data domains. Through robust experimental evaluations on different datasets and in three application domains, our method demonstrates competitive performance, enabling direct comparison of entities and relationships within a unified latent space. This research highlights the potential of HINs for intelligent analysis and information retrieval in multimedia systems and heterogeneous data contexts.

Palavras-chave: embedding propagation, network embedding, heterogeneous information networks


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DO CARMO, Paulo Viviurka; MARCACINI, Ricardo. Embedding Propagation over Heterogeneous Information Networks. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 7-10. ISSN 2596-1683. DOI: https://doi.org/10.5753/webmedia_estendido.2023.233762.