Projection-based link prediction in a bipartite network

Gao, Man; Chen, Ling; Li, Bin; Li, Yun; Liu, Wei; Xu, Yongfen

doi:10.1016/j.ins.2016.10.015

Cited by 55 publications

(24 citation statements)

References 32 publications

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“…2012; Barra and Agliari, 2012; Arnaboldi et al ., 2017; Gao et al . 2017). Verbatim transcripts of class 3 stress that the effect of beneficial or harmful effects of structural holes in ego‐networks on direct or indirect performance (in different contexts) is heterogeneous because structural holes are characterized by a collaboration paradox.…”

Section: Resultsmentioning

confidence: 99%

Research on Structural Holes: An Assessment on Measurement Issues

Saglietto

Cézanne

David

2020

Journal of Economic Surveys

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Social capital theory generally analyses network structures by focusing on the connections between players. However, it has been suggested by several authors that the absence of relations in a network or ‘structural holes’ is meaningful. The aim of our paper is to provide a global survey and appraisal of the academic research on structural holes and discuss its main measurements. We adopt a bibliometric approach and identify a typology of practices, developments, and issues related to structural holes. We highlight a strand of work on operationalization of structural holes, and discuss the various measures they propose. We provide numerical examples emphasizing the respective advantages and limitations of the studied measures. Based on our results, we propose a guide to using existing measures of structural holes according to the type (simple or complex) of network.

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Section: Resultsmentioning

confidence: 99%

Research on Structural Holes: An Assessment on Measurement Issues

Saglietto

Cézanne

David

2020

Journal of Economic Surveys

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“…By considering stability similarity also could improve the stability recommendation. Furthermore, M. Gao et al [103] proposed a projected graph in the bipartite network to map into a unipartite network aiming to define the concept of a CNP and potential link prediction (PLP).…”

Section: ) Network Formatsmentioning

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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“…Therefore, bipartite networks need to be converted into single-mode networks in order to apply these methods. Also, there are other methods for link prediction in bipartite networks [30–35].…”

Section: Link Prediction In Bipartite Networkmentioning

confidence: 99%

Hotel recommendation system by bipartite networks and link prediction

Kaya

2019

Journal of Information Science

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With the rapid growth of the Internet in recent years, online social media has become very important for people. People often use social media tools to communicate and share their ideas or experiences regardless of time and place. One of the areas where the use of these tools is widespread is tourism. It is one of the hardest tasks to find a suitable hotel for travellers. There are many websites where accommodation services of the tourism enterprises are evaluated. People share their experiences about the hotels they stayed through these websites. Positive or negative comments are an effective factor in hotel selection. The purpose of this study is to construct a hotel recommendation system based on user’s location using online hotel reviews. The reviews crawled from TripAdvisor.com were filtered according to their total scores, and a dataset was obtained consisting of users and reviews of their liked hotels. For the recommendation task, first, three different bipartite networks consisting of users and hotels were modelled, which were global, country and city based. Then, in these networks, it was recommended hotels to users for their next choice with link prediction methods, using the common hotels where the users prefer to stay in the past. The most successful results were obtained in the city-based network. With this study, it was tried to reduce the time spent reading the online reviews and finding the suitable hotel. To the best of our knowledge, this is the first study on recommending a hotel using online reviews by bipartite networks.

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Projection-based link prediction in a bipartite network

Cited by 55 publications

References 32 publications

Research on Structural Holes: An Assessment on Measurement Issues

Research on Structural Holes: An Assessment on Measurement Issues

Taxonomy of Link Prediction for Social Network Analysis: A Review

Hotel recommendation system by bipartite networks and link prediction

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