Article: Design of a multimodal information visualisation and analysis model based on improved graph embedding network Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.6 pp.92 - 107 Abstract: This study adopts modality-specific feature extraction for text, visual, and audio inputs. Task predictions and modality representations are embedded into an adaptive graph, which is further augmented by introducing an attenuated higher-order common-neighbour similarity matrix within a heterogeneous graph neural network. This formulation is used to guide node aggregation and to support interpretability through explicit graph-based relational modelling. Based on these components, an attention-aware graph embedding model is constructed for downstream analysis. Across the Alibaba and IMDB datasets, the proposed method achieves average gains of 6.13% (Macro-F1) and 6.57% (Micro-F1) over graph embedding baselines. On IMDB, it further improves accuracy by 4.1%, F1-score by 5.9%, and reduces mean absolute error by 6.2%. These results suggest that the proposed graph-based fusion strategy can provide measurable gains on the considered benchmarks while enabling adaptive estimation of inter-modal interaction weights. Inderscience Publishers - linking academia, business and industry through research

Title: Design of a multimodal information visualisation and analysis model based on improved graph embedding network

Authors: YanHong Song

Addresses: School of Computer Science, Wuhan Vocational College of Software and Engineering (Wuhan Open University), Wuhan 430205, Hubei, China

Abstract: This study adopts modality-specific feature extraction for text, visual, and audio inputs. Task predictions and modality representations are embedded into an adaptive graph, which is further augmented by introducing an attenuated higher-order common-neighbour similarity matrix within a heterogeneous graph neural network. This formulation is used to guide node aggregation and to support interpretability through explicit graph-based relational modelling. Based on these components, an attention-aware graph embedding model is constructed for downstream analysis. Across the Alibaba and IMDB datasets, the proposed method achieves average gains of 6.13% (Macro-F1) and 6.57% (Micro-F1) over graph embedding baselines. On IMDB, it further improves accuracy by 4.1%, F1-score by 5.9%, and reduces mean absolute error by 6.2%. These results suggest that the proposed graph-based fusion strategy can provide measurable gains on the considered benchmarks while enabling adaptive estimation of inter-modal interaction weights.

Keywords: multimodal information; visual analytics; graph embedding networks; attention perception; adaptive graphs.

DOI: 10.1504/IJDMB.2026.153895

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.6, pp.92 - 107

Received: 26 Dec 2025
Accepted: 09 Mar 2026
Published online: 29 May 2026 *

Title: Design of a multimodal information visualisation and analysis model based on improved graph embedding network

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