Evaluating Heterogeneous Node Embedding Compositions Using Diversity Metrics
DOI:
https://doi.org/10.5753/jidm.2026.5727Keywords:
Heterogeneous Embedding, Graph Embedding, Heterogeneous Graph, Representation Knowledge, Graph Neural NetworksAbstract
This paper evaluates the impact of different embedding composition strategies on classification performance, analyzing local node features, neighboring node features, and metapaths. We conduct a comprehensive experimental evaluation using an authorial Person Relationships heterogeneous graph, incorporating diversity metrics to assess dataset balance and structural complexity. This approach provides deeper insights into their influence on model effectiveness and extends prior research by comparing new results against an established baseline. The experimental findings reaffirm the effectiveness of embedding compositions, with Aggregated Features + Metapaths achieving a Micro-F1 score of 94.04\%, demonstrating highly accurate results, validated by diversity metrics. This outcome highlights the importance of embedding compositions in heterogeneous graph representations, reinforcing its potential to improve predictive performance in real-world graph structures.
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References
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