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Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction.

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Titel: Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction.
Autoren: Dimitriou, Paraskevas1 (AUTHOR), Karyotis, Vasileios1 (AUTHOR) karyotis@ionio.gr
Quelle: Journal of Computational Science. Dec2025, Vol. 92, pN.PAG-N.PAG. 1p.
Schlagwörter: Machine learning, Evolutionary algorithms, Graph algorithms, Benchmark problems (Computer science), Communication network analysis, Feature extraction
Abstract: Link prediction is a fundamental task in network analysis, aiming at predicting missing or future connections between nodes in a network. With the growing availability of complex network data in fields like social networks, biological systems, the Internet, and scientific collaboration networks, accurate link prediction methods are becoming increasingly critical. Neighborhood or graph based link prediction algorithms are applied identically to different types of networks so that any differences in their structures are not exploited efficiently. Machine or deep learning based link prediction algorithms apply to each kind of network differently depending on the type of network, due to the unique characteristics of each domain, but frequently, most of them give poor results. In this paper, we propose a novel approach for link prediction, leveraging the power of machine learning and evolutionary algorithms. Our method utilizes local network information by encoding the network topology into link embeddings through a heuristic machine learning architecture. We introduce a novel tool to extract features from network structure effectively and combine them in an effective way through an evolutionary algorithm improving the discriminative power of link embeddings. We evaluate our method on eleven benchmark datasets and demonstrate its superior performance compared to a series (eleven in total) of effective and state-of-the-art algorithms. Our approach advances the state-of-the-art in link prediction yielding better results than other methods in all the networks we have applied it to. • Novel link prediction, leveraging on machine learning and evolutionary algorithms. • Local network information by encoding network topology into link embeddings. • Evaluation of HCSI over eleven datasets, demonstrating its superior performance. • Comparison with eleven state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
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Abstract:Link prediction is a fundamental task in network analysis, aiming at predicting missing or future connections between nodes in a network. With the growing availability of complex network data in fields like social networks, biological systems, the Internet, and scientific collaboration networks, accurate link prediction methods are becoming increasingly critical. Neighborhood or graph based link prediction algorithms are applied identically to different types of networks so that any differences in their structures are not exploited efficiently. Machine or deep learning based link prediction algorithms apply to each kind of network differently depending on the type of network, due to the unique characteristics of each domain, but frequently, most of them give poor results. In this paper, we propose a novel approach for link prediction, leveraging the power of machine learning and evolutionary algorithms. Our method utilizes local network information by encoding the network topology into link embeddings through a heuristic machine learning architecture. We introduce a novel tool to extract features from network structure effectively and combine them in an effective way through an evolutionary algorithm improving the discriminative power of link embeddings. We evaluate our method on eleven benchmark datasets and demonstrate its superior performance compared to a series (eleven in total) of effective and state-of-the-art algorithms. Our approach advances the state-of-the-art in link prediction yielding better results than other methods in all the networks we have applied it to. • Novel link prediction, leveraging on machine learning and evolutionary algorithms. • Local network information by encoding network topology into link embeddings. • Evaluation of HCSI over eleven datasets, demonstrating its superior performance. • Comparison with eleven state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
ISSN:18777503
DOI:10.1016/j.jocs.2025.102719