TY - JOUR
T1 - HM-EIICT
T2 - Fairness-aware link prediction in complex networks using community information
AU - Saxena, Akrati
AU - Fletcher, George
AU - Pechenizkiy, Mykola
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Link analysis
KW - Link prediction
KW - Similarity-based indices
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=85113662593&partnerID=8YFLogxK
U2 - 10.1007/s10878-021-00788-0
DO - 10.1007/s10878-021-00788-0
M3 - Article
AN - SCOPUS:85113662593
SN - 1382-6905
VL - 44
SP - 2853
EP - 2870
JO - Journal of Combinatorial Optimization
JF - Journal of Combinatorial Optimization
IS - 4
ER -