HM-EIICT

Autor: Mykola Pechenizkiy, Akrati Saxena, George H. L. Fletcher
Přispěvatelé: Database Group, Data Mining, EAISI Health, EAISI Foundational
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Combinatorial Optimization, 44(4), 2853-2870. Springer
ISSN: 1382-6905
Popis: The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.
Databáze: OpenAIRE
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