Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge fe...

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Autores principales:	Chuanting Zhang, Ke-Ke Shang, Jingping Qiao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/22f70b50c8954428a7b69aa02e6f2d39
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spelling	oai:doaj.org-article:22f70b50c8954428a7b69aa02e6f2d392021-11-29T00:56:17ZAdaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction1099-052610.1155/2021/1277579https://doaj.org/article/22f70b50c8954428a7b69aa02e6f2d392021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1277579https://doaj.org/toc/1099-0526Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.Chuanting ZhangKe-Ke ShangJingping QiaoHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Chuanting Zhang Ke-Ke Shang Jingping Qiao Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
description	Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.
format	article
author	Chuanting Zhang Ke-Ke Shang Jingping Qiao
author_facet	Chuanting Zhang Ke-Ke Shang Jingping Qiao
author_sort	Chuanting Zhang
title	Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
title_short	Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
title_full	Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
title_fullStr	Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
title_full_unstemmed	Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction
title_sort	adaptive similarity function with structural features of network embedding for missing link prediction
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/22f70b50c8954428a7b69aa02e6f2d39
work_keys_str_mv	AT chuantingzhang adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction AT kekeshang adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction AT jingpingqiao adaptivesimilarityfunctionwithstructuralfeaturesofnetworkembeddingformissinglinkprediction
_version_	1718407693825212416

Adaptive Similarity Function with Structural Features of Network Embedding for Missing Link Prediction

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