Link prediction in complex networks based on Significance of Higher-Order Path Index (SHOPI)

Kumar, Ajay; Mishra, Shivansh; Singh, Shashank Sheshar; Singh, Kuldeep; Biswas, Bhaskar

doi:10.1016/j.physa.2019.123790

Cited by 32 publications

(7 citation statements)

References 39 publications

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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%

“…( 7 ), where is a tunable parameter and is the shortest distance between nodes and . Higher-Order Path Index 29 : Based on the common neighbours, the significance of paths between two nodes is taken into account to propose an iterative method. Summing up the significance of the paths between two nodes determines the likelihood of link formation between them.…”

Section: Related Workmentioning

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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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: Related Workmentioning

confidence: 99%

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

Zareie

Sakellariou

2020

Sci Rep

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“…Kumar et al [26] have introduced a new method to predict links based on level-2 node clustering coefficients. Their method presents level-2 common nodes and clustering coefficients to gather information about clusters from the seed node pair's level-2 familiar neighbors.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Sajjadi

Esmaeili

Ghobaei‐Arani

et al. 2023

Preprint

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In recent years, researchers from academic and industrial fields have become increasingly interested in social network data to extract meaningful information. This information is used in applications such as link prediction between people groups, community detection, protein module identification, etc. Therefore, the clustering technique has emerged as a solution to finding similarities between social network members. Recently, in most graph clustering solutions, the structural similarity of nodes is combined with their attribute similarity. The results of these solutions indicate that the graph's topological structure is more important. Since most social networks are sparse, these solutions often suffer from insufficient use of node features. This paper proposes a hybrid clustering approach for link prediction in heterogeneous information networks (HINs). In our approach, an adjacency vector is determined for each node until, in this vector, the weight of the direct edge or the weight of the shortest communication path among every pair of nodes is considered. A similarity metric is presented that calculates similarity using the direct edge weight between two nodes and the correlation between their adjacency vectors. Finally, we evaluated the effectiveness of our proposed method using DBLP and Political blogs datasets under entropy, density, purity, and execution time metrics. The simulation results demonstrate that while maintaining the cluster density significantly reduces the entropy and the execution time compared with the other methods.

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“…The model that is formed in the path equation will bring up the path coefficients whose significance needs to be tested next. The significance of the existing path coefficients will affect the path significance of the path diagram of the resulting model (Kumar et al, 2020). The significance test using the t-student test with the null hypothesis is that there is no significant effect of exogenous variables on endogenous variables.…”

Section: Path Coefficient Significancementioning

confidence: 99%

Analysis of Resource Ownership of Small Medium Industry and Their Effect on Natural Disaster Preparedness in the Beach Area (Study on SMEs Food Industry Branch)

Adfar¹,

Tope²,

Lutfi³

2022

IJRISS

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Palu Bay is one of the bays in a seismically active area in Indonesia which is traversed by the Palu-Koro fault segment. In the aftermath of a major natural disaster in the Region on September 28, 2018 Palu City, Donggala Regency and Sigi Regency were areas that were most affected by this disaster, so it was necessary to identify the level of community preparedness, especially for SMEs in dealing with natural disasters in the future and how the impact of resource ownership will be that they have whether the resource significantly affects SMEs preparedness to face natural disasters. This research was conducted three years after the natural disaster in the Palu Bay Area. The assessment was carried out through a questionnaire involving 260 SMIs that were chosen randomly and from the four variables of SMEs resources studied, that the resources that had an effect on disaster preparedness were the capital variable, which was 0.07, the entrepreneurial variable was 0.35, the labor variable was -0.03, and the Information Technology variable is not significant. While the level of preparedness of SMEs in dealing with natural disasters in the future, 17.31% of respondents are stated to be "not ready", 60.90% "somewhat ready" and 23.8% "ready" while none of the respondents were "very prepared". This lack of readiness of SMEs clearly requires the attention of all stakeholders in fostering the resilience of SMEs.

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Link prediction in complex networks based on Significance of Higher-Order Path Index (SHOPI)

Cited by 32 publications

References 39 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

A Hybrid Clustering Approach for link prediction in Heterogeneous Information Networks

Analysis of Resource Ownership of Small Medium Industry and Their Effect on Natural Disaster Preparedness in the Beach Area (Study on SMEs Food Industry Branch)

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