Graph Embedding
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Deep Learning Approaches for Graph Similarity Computation: A Review
Abstract: Graph-structured data is increasingly prevalent in many real-world applications such as social networks, biological systems, molecular chemistry, and knowledge graphs. Measuring the similarity between graphs is a fundamental task in graph analytics, enabling applications including pattern recognition, recommendation systems, network alignment, and drug discovery. Traditional approaches for graph similarity computation, such as Graph Edit Distance and graph kernels, provide effective structural comparison but often suffer from high computational complexity and …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 2, 2026 Read article