Extended resource allocation index for link prediction of complex network

Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

doi:10.1016/j.physa.2017.02.078

Cited by 42 publications

(19 citation statements)

References 31 publications

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“…Rafiee et al [59] proposed a similarity-based prediction method based on common neighbors degree penalization that generates similarity scores based on the similarity of common neighbors' topology characteristics and the average network cluster coefficient. Later, S. Liu et al [111] proposed an extended resource allocation index (ERA), which adds longer paths to the RA index. The ERA index measures similarity based on a parameter adjusting the number of resources transferred by longer paths in different networks and the number of resources exchanged by common neighbors and non-common neighbors between two endpoints [111].…”

Section: ) Local 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: ) Local Similarity-based Methodsmentioning

confidence: 99%

Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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“…In practice, L is generally set to 100 for large-scale networks, such as Ref. [ 4 , 32 ]. Thus, in order to compare the results more intuitively and clearly in multiple datasets, we set L =100 in this paper.…”

Section: Metrics and Baselinesmentioning

confidence: 99%

“…Recently, more and more complex systems have been analyzed through theories of network science [ 1 , 2 , 3 , 4 , 5 ]. As an important hot topic of complex networks, link prediction [ 6 ] aims to predict the likelihood that a link exists between two nodes of complex networks.…”

Section: Introductionmentioning

confidence: 99%

“…These local indices perform very well in many types of networks, but they need more topology information to improve the prediction accuracy in some networks. Considering longer paths, Local Path (LP) index [ 14 ] and Extended Resource Allocation (ERA) index [ 4 ] are proposed by adding the paths with length 3 to the CN index and RA index, respectively. Furthermore, many global indices are proposed by considering all the topological information between two endpoints, such as Katz index [ 15 ], SimRank [ 16 ], Average Commute Time (ACT, also called Mean Commute Time) [ 17 , 18 ] and Cosine Similarity Time (Cos+) [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

Li¹,

Liu²,

Chen³

et al. 2019

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Recently, a number of similarity-based methods have been proposed for link prediction of complex networks. Among these indices, the resource-allocation-based prediction methods perform very well considering the amount of resources in the information transmission process between nodes. However, they ignore the information channels and their information capacity in information transmission process between two endpoints. Motivated by the Cannikin Law, the definition of information capacity is proposed to quantify the information transmission capability between any two nodes. Then, based on the information capacity, a potential information capacity (PIC) index is proposed for link prediction. Empirical study on 15 datasets has shown that the PIC index we proposed can achieve a good performance, compared with eight mainstream baselines.

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“…Zhu and Xia (2015) introduced the Neighbor Set Information (NSI) method, which from a perspective of information theory, uses both the common neighbors and the links across the two neighbor sets of analyzed nodes to improve the prediction accuracy. Liu et al (2017) proposed the Extended Resource Allocation (ERA) method, which is based on RA but considers a parameter to adjust the amount of resources transferred, i.e. the allocation process is performed by sending more than unit of resources between a pair of disconnected nodes.…”

Section: Common Neighbors (Cn) Common Neighbors Is the Simplest Locamentioning

confidence: 99%

Mining user behavior in location-based social networks

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Extended resource allocation index for link prediction of complex network

Cited by 42 publications

References 31 publications

Taxonomy of Link Prediction for Social Network Analysis: A Review

Taxonomy of Link Prediction for Social Network Analysis: A Review

A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

Mining user behavior in location-based social networks

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