Supervised Machine Learning for Link Prediction Using Path-Based Similarity Features
| Autor: | Ranjan Kumar Behera, Satya Prakash Sahoo, Bibhudatta Sahoo, Anisha Kumari |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Feature vector Feature extraction 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Support vector machine SimRank Similarity (network science) Path (graph theory) 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence business computer Link analysis |
| Zdroj: | 2020 IEEE 17th India Council International Conference (INDICON). |
| Popis: | Link prediction is an emerging research domain, where new ties are predicted based on the structural properties of the network. In this paper, path-based similarity measures have been adopted for link prediction which is able to extract the global similarity instead of local neighborhood similarity. It has been observed that individual similarity measures may not be a adequate for future link analysis. In this paper, several path-based similarity measures like Katz index, Adamic Adar, simRank and Hitting time measures are hybridized for feature extraction in supervised machine learning models for link prediction. The major contribution in this paper is to leverage the path-based similarity measures to construct the feature vector for supervised machine learning algorithms to predict the future link. The effectiveness of machine learning models is compared with the individual global similarity measures using a few real-world network datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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