Graph neural networks
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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
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Feature Engineering in Modern Predictive Modeling: Techniques, Automation Frameworks and Emerging Trends
Abstract: Feature engineering plays a crucial role in developing reliable and high-performing predictive models. It involves transforming raw and unprocessed data into meaningful features that help machine learning algorithms achieve better accuracy and overall performance. Feature engineering is helpful in identifying hidden patterns, removing noise, and increasing the signal-to-noise ratio in data sets. Machine learning techniques have progressed remarkably over the years, and the effectiveness of these models largely depends on …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 2, 2026 · pp. 32–40 Read article