Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm

Ahmad, Iftikhar; Akhtar, Muhammad Usman; Noor, Salma; Shahnaz, Ambreen

doi:10.1038/s41598-019-57304-y

Cited by 82 publications

(57 citation statements)

References 31 publications

(30 reference statements)

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“…In order to evaluate the performance of the proposed DICN method, this method and another 8 representative methods from the literature were implemented in Java and executed on a PC with an i5 2.3 GHz processor and 8 MB memory. The eight methods used for comparison are: Common Neighbours (CN) 8 , Preferential Attachment Index (PA) 10 , Jaccard Index (JC) 11 , Hub Promoted Index (HPI) 28 , Common Neighbours Degree Penalization (CNDP) 15 , Node-coupling Clustering (NCC) 17 , Parameterized Algorithm (CCPA) 16 and Significance of Higher-Order Path Index (SHOPI) 29 .…”

Section: Resultsmentioning

confidence: 99%

“…Although the set of (non-existing) links E N tends to have fewer common neighbours, on average, than the set E, there is a significant overlap between the two sets and, in some cases (say, around 8 common neighbours for the sets INF, EML, YST) the chance of an existing versus a non-existing link for that number In general, it appears that many links may exist between nodes that share no common neighbours at all, while, other nodes may share a large number of common neighbours without a direct link between them. Although it is true that various methods 14,16,17 have been proposed to improve the accuracy of link prediction based on the number of common neighbours, the key limitation is that they still rely mostly on common first-order neighbours.…”

Section: Motivationmentioning

confidence: 99%

“…In general, it appears that many links may exist between nodes that share no common neighbours at all, while, other nodes may share a large number of common neighbours without a direct link between them. Although it is true that various methods 14 , 16 , 17 have been proposed to improve the accuracy of link prediction based on the number of common neighbours, the key limitation is that they still rely mostly on common first-order neighbours.…”

Section: Motivationmentioning

confidence: 99%

“…( 6 ), where is the clustering coefficient of node . Parameterized Algorithm 16 : In this method, the number of common neighbours and the closeness of two nodes are both taken into account to estimate the similarity between a pair of nodes. The parameterized similarity score between and is calculated by Eq.…”

Section: Related Workmentioning

confidence: 99%

“…Although computing the number of common neighbours is highly time-efficient, this measure cannot capture the similarity between two nodes accurately. Different measures [14][15][16][17] have been proposed to improve the accuracy of this measure by combining the number of common neighbours with additional information. However, these measures also suffer from low accuracy.…”

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confidence: 99%

See 4 more Smart Citations

Similarity-based link prediction in social networks using latent relationships between the users

Zareie

Sakellariou

2020

Sci Rep

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Social network analysis has recently attracted lots of attention among researchers due to its wide applicability in capturing social interactions. Link prediction, related to the likelihood of having a link between two nodes of the network that are not connected, is a key problem in social network analysis. Many methods have been proposed to solve the problem. Among these methods, similarity-based methods exhibit good efficiency by considering the network structure and using as a fundamental criterion the number of common neighbours between two nodes to establish structural similarity. High structural similarity may suggest that a link between two nodes is likely to appear. However, as shown in the paper, the number of common neighbours may not be always sufficient to provide comprehensive information about structural similarity between a pair of nodes. To address this, a neighbourhood vector is first specified for each node. Then, a novel measure is proposed to determine the similarity of each pair of nodes based on the number of common neighbours and correlation between the neighbourhood vectors of the nodes Experimental results, on a range of different real-world networks, suggest that the proposed method results in higher accuracy than other state-of-the-art similarity-based methods for link prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Similarity-based link prediction in social networks using latent relationships between the users

Zareie

Sakellariou

2020

Sci Rep

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Gravitational search algorithm‐driven missing links prediction in social networks

Singh

2022

Concurrency and Computation

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The increased usage of online social networks in recent days attracts the attention of researchers. That makes analysis of social networks a significant concern. Link prediction is one of the problem of social networks, where links are predicted between users by analyzing the relationships between the nodes. As the size and usability of the social network grows, the accurate prediction of connections with limited public information is a challenge. This article introduces a novel approach for predicting missing links using a nature-inspired method, the gravitational search algorithm. We selected seven real-world networks and five widely used algorithms and also considered the sparsity of networks for the experimental evaluation of the proposed approach. The proposed GSA-driven algorithm has shown a better or comparable AUC result than the other standard algorithms on these networks.

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MEM‐FET: Essential protein prediction using membership feature and machine learning approach

Payra,

Saha,

Ghosh

2023

Proteins

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Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day‐to‐day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM‐FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM‐FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM‐FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

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Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm

Cited by 82 publications

References 31 publications

Similarity-based link prediction in social networks using latent relationships between the users

Similarity-based link prediction in social networks using latent relationships between the users

Gravitational search algorithm‐driven missing links prediction in social networks

MEM‐FET: Essential protein prediction using membership feature and machine learning approach

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