Study on a New Method of Link-Based Link Prediction in the Context of Big Data

Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” cur...

Full description

Saved in:
Bibliographic Details
Main Authors: Chen Jicheng, Chen Hongchang, Li Hanchao
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Applied Bionics and Biomechanics
Online Access:http://dx.doi.org/10.1155/2021/1654134
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832563462374424576
author Chen Jicheng
Chen Hongchang
Li Hanchao
author_facet Chen Jicheng
Chen Hongchang
Li Hanchao
author_sort Chen Jicheng
collection DOAJ
description Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature’s unsupervised approaches.
format Article
id doaj-art-e809b41bcd4844ef9598ec459665ea72
institution Kabale University
issn 1754-2103
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Applied Bionics and Biomechanics
spelling doaj-art-e809b41bcd4844ef9598ec459665ea722025-02-03T01:20:13ZengWileyApplied Bionics and Biomechanics1754-21032021-01-01202110.1155/2021/1654134Study on a New Method of Link-Based Link Prediction in the Context of Big DataChen Jicheng0Chen Hongchang1Li Hanchao2National Digital Switching System Engineering and Technology Research CenterNational Digital Switching System Engineering and Technology Research CenterNational Digital Switching System Engineering and Technology Research CenterLink prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature’s unsupervised approaches.http://dx.doi.org/10.1155/2021/1654134
spellingShingle Chen Jicheng
Chen Hongchang
Li Hanchao
Study on a New Method of Link-Based Link Prediction in the Context of Big Data
Applied Bionics and Biomechanics
title Study on a New Method of Link-Based Link Prediction in the Context of Big Data
title_full Study on a New Method of Link-Based Link Prediction in the Context of Big Data
title_fullStr Study on a New Method of Link-Based Link Prediction in the Context of Big Data
title_full_unstemmed Study on a New Method of Link-Based Link Prediction in the Context of Big Data
title_short Study on a New Method of Link-Based Link Prediction in the Context of Big Data
title_sort study on a new method of link based link prediction in the context of big data
url http://dx.doi.org/10.1155/2021/1654134
work_keys_str_mv AT chenjicheng studyonanewmethodoflinkbasedlinkpredictioninthecontextofbigdata
AT chenhongchang studyonanewmethodoflinkbasedlinkpredictioninthecontextofbigdata
AT lihanchao studyonanewmethodoflinkbasedlinkpredictioninthecontextofbigdata