Link and Annotation Prediction Using Topology and Feature Structure in Large Scale Social Networks

Autor:	Fatih Erdogan Sevilgen, Mustafa Kirac, Burak Isikli
Rok vydání:	2014
Předmět:	business.industry Computer science Feature vector Big data Machine learning computer.software_genre Annotation Cheminformatics Large networks Graph isomorphism problem Graph (abstract data type) Artificial intelligence Data mining business Feature structure computer
Zdroj:	Communications in Computer and Information Science ISBN: 9783319069319 BDAS
DOI:	10.1007/978-3-319-06932-6_23
Popis:	Repeated patterns observed in graph and network structures can be utilized for predictive purposes in various domains including cheminformatics, bioinformatics, political sciences, and sociology. In large scale network structures like social networks, graph theoretical link and annotation prediction algorithms are usually not applicable due to graph isomorphism problem, unless some form of approximation is applied. We propose a non-graph theoretical alternative to link and annotation prediction in large networks by flattening network structures into feature vectors. We extract repeated sub-network pattern vectors for the nodes of a network, and utilize traditional machine learning algorithms for estimating missing or unknown annotations and links in the network. Our main contribution is a novel method for extracting features from large scale networks, and evaluation of the benefit each extraction method provides. We applied our methodology for suggesting new Twitter friends. In our experiments, we observed 11-27% improvement in prediction accuracy when compared to the simple methodology of suggesting friends of friends.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::17ef13d66419e7d1172d8cf3b83b2335 https://doi.org/10.1007/978-3-319-06932-6_23 Zobrazit plný text záznamu