Enhancing link prediction via network reconstruction

Wu, Mei‐Hwan; Wu, Shunyao; Zhang, Qi; Xue, Chuanyu; Kan, Hongsheng; Shao, Fengjing

doi:10.1016/j.physa.2019.122346

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…Therefore, although He et al. (2015) reported considerable improvement, later experiments ( Wu et al., 2019 ; Zhang et al., 2020 ) indicated that the method in He et al. (2015) does not work well because the position of a predictor is irrelevant to its quality.…”

Section: Ensemble Learningmentioning

confidence: 99%

“…(2015) does not work well because the position of a predictor is irrelevant to its quality. In contrast, if the order is relevant to the predictors’ qualities (e.g., according to their precisions trained by the target network), OWA will bring in remarkable improvement compared with individual predictors ( Wu et al., 2019 ). As some link prediction algorithms scale worse than

, Duan et al.…”

Section: Ensemble Learningmentioning

confidence: 99%

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Progresses and challenges in link prediction

Zhou

2021

iScience

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Summary Link prediction is a paradigmatic problem in network science, which aims at estimating the existence likelihoods of nonobserved links, based on known topology. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about local similarity indices, link predictability, network embedding, matrix completion, ensemble learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.

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Section: Ensemble Learningmentioning

confidence: 99%

, Duan et al.…”

Section: Ensemble Learningmentioning

confidence: 99%

Progresses and challenges in link prediction

Zhou

2021

iScience

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“…Previously, W. Wang et al [104] proposed a kernel framework and reconstructed the network using a different kernel that can obtain global and local network information through kernel mapping. Furthermore, M. Wu et al [73] proposed a new serial ensemble strategy by using network reconstruction of nine local indices aggregated with the Ordered Weighted Averaging (OWA) operator.…”

Section: ) Network Reconstructionmentioning

confidence: 99%

“…Then, the accuracy is defined as the ratio of the predicted classification results and the correct classification results [70]. Precision is used to evaluate the algorithm performance [73] and to measure the ratio of the selected items relevant to their number [64], [69], [104], [111]. Furthermore, recall is a division between the number of true positive results and the number of positive results returned [70].…”

Section: Figure 5 Detailed Taxonomy Of Prediction Measurements In LImentioning

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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“…As stated in He and Liu [ 23 ], ordered weighted averaging (OWA) operator is such a method which can effectively reduce the variance of similarity-based link prediction algorithm and improve the stability of link predictor. In order to select and organize these indices more reasonably, Wu et al employed a typical metric precision to evaluate the performance of each index and filtered the indices with poor performance [ 24 ]. Simulations prove that the integrated learning and fusion algorithm are effective in solving the problem of link prediction, with the satisfactory effect [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks

Yuan

Ren

Zeng

et al. 2020

Entropy

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Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.

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Enhancing link prediction via network reconstruction

Cited by 8 publications

References 23 publications

Progresses and challenges in link prediction

Progresses and challenges in link prediction

Taxonomy of Link Prediction for Social Network Analysis: A Review

Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks

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