A Mobile User Preference Prediction Method Based on Trust and Link Prediction

2014 ◽  
Vol 35 (12) ◽  
pp. 2972-2977
Author(s):  
Hua Geng ◽  
Xiang-wu Meng ◽  
Yan-cui Shi
Keyword(s):  
Link Prediction ◽  
Prediction Method ◽  
Mobile User ◽  
User Preference

scholarly journals A Prediction Method of Mobile User Preference Based on the Influence between Users

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yancui Shi ◽  
Jianhua Cao ◽  
Congcong Xiong ◽  
Xiankun Zhang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Prediction Method ◽  
Mobile User ◽  
User Preferences ◽  
User Preference ◽  
Context Information ◽  
Mobile Social Network ◽  
Data Set ◽  
Interaction Behavior

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

A novel link prediction method for supervising transitivity process

2018 ◽  
Vol 48 (11) ◽  
pp. 4305-4316
Author(s):  
Cheng Jiang ◽  
Wei Chen ◽  
Jun Zhang
Keyword(s):  
Link Prediction ◽  
Prediction Method

scholarly journals GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i464-i473
Author(s):  
Kapil Devkota ◽  
James M Murphy ◽  
Lenore J Cowen
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Global Network ◽  
Local Network ◽  
Supplementary Information ◽  
Network Data ◽  
Ppi Network ◽  
Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade

2021 ◽  
Vol 13 (21) ◽  
pp. 11681
Author(s):  
Qiaoran Yang ◽  
Zhiliang Dong ◽  
Yichi Zhang ◽  
Man Li ◽  
Ziyi Liang ◽  
...  
Keyword(s):  
International Trade ◽  
Link Prediction ◽  
Prediction Method ◽  
Uneven Distribution ◽  
Trade Network ◽  
Research Results ◽  
Trade Relations ◽  
China And India ◽  
Potential Relationship ◽  
The World

Nickel ore sand and its concentrate are the main sources of raw nickel materials in various countries. Due to its uneven distribution throughout the world, the international trade of nickel ore sand is also unstable. Looking for potential links in the changing international nickel ore trade can help governments find potential partners, make strategic preparations in advance, and quickly find new partners when original trade relationships break down. In this paper, we build an international nickel ore trade network using a link prediction method to find potential trade relations between countries. The results show that China and Italy, China and Denmark, China and Indonesia, and China and India are most likely to establish trade relations within five years. Finally, according to the research results, suggestions regarding the international nickel ore trade are proposed.

scholarly journals A Scalable Similarity-Popularity Link Prediction Method

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Said Kerrache ◽  
Ruwayda Alharbi ◽  
Hafida Benhidour
Keyword(s):  
Link Prediction ◽  
Prediction Method
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