CLP-ID: Community-based link prediction using information diffusion

Singh, Shashank Sheshar; Mishra, Shivansh; Kumar, Ajay

doi:10.1016/j.ins.2019.11.026

Cited by 44 publications

(10 citation statements)

References 30 publications

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“…Several researchers have studied community-based similarities. Singh et al [67] described information diffusion and the community structure that divided the network into clusters and denoted the algorithms as CLP-ID. Mahmoudi et al [89] outlined user community changes referred to as User Attribute-based Link Prediction (UALP).…”

Section: ) Community Similarity-based Methodsmentioning

confidence: 99%

Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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Link prediction is a technique to forecast future new or missing relationships between entities based on the current network information. Graph theory and network science are theoretical concepts that have influenced the link prediction research. Although previous reviews clearly outlined the link prediction research, it was focused on describing prediction approaches only. However, analysis of related studies identified other components that influence link prediction. This review aims to present a continued review and introduce the taxonomy of link prediction using three main components: the prediction approaches, prediction features, and prediction measurements. Each component has been detailed using its own taxonomy available at the present review. Furthermore, this review compares the prediction approaches and prediction features also benchmark algorithms and measurement methods of previous link prediction studies. In conclusion, the previous studies mostly focused on structural features and similarity-based approaches, while measuring the proposed methods using the Area Under the Curve (AUC) score. The proposed link prediction taxonomy can guide the researchers to generate new ideas and innovations that contribute to the link prediction research.

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Section: ) Community Similarity-based Methodsmentioning

confidence: 99%

Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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“…Li et al (Li et al 2019) proposed a link prediction framework that computes the Community Relationship Strength (CRS) and then uses it with similarity-based local indices to compute the final likelihood for a node-pair. Some other community-based link prediction methods include (Wang et al 2019;Singh et al 2020;Wu et al 2017;Jeon and Kim 2017a); however, none of them has focused on improving the inter-community link prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

HM-EIICT: Fairness-aware link prediction in complex networks using community information

2021

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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.

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“…After the COVID-19 pandemic, the necessity of studying information diffusion is tangible and well-understood. The applications of such studies vary in the wide range of predicting the spread of pathogens (Chinazzi et al, 2020;Ye et al, 2020), ideas (Rehman et al, 2020), or computer viruses (Chenquan et al, 2020), to diverse applications defined on networks, including link prediction (Singh, Mishra, Kumar, & Biswas, 2020). Information diffusion is a genuinely interdisciplinary topic to the extent that researchers from various disciplines, including computer science, social sciences, political sciences, and medical sciences, etc have been seriously pursuing it (Z.…”

Section: Introductionmentioning

confidence: 99%