Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation

2020 ◽  
Vol 79 (19-20) ◽  
pp. 13197-13215
Author(s):  
Lingling Zhang ◽  
Minghui Zhao ◽  
Daozhen Zhao
Keyword(s):  
Bipartite Graph ◽  
Link Prediction ◽  
Prediction Method ◽  
Music Recommendation ◽  
Graph Link

scholarly journals Genre-based Link Prediction in Bipartite Graph for Music Recommendation

2016 ◽  
Vol 91 ◽  
pp. 959-965 ◽  
Author(s):  
Daozhen Zhao ◽  
Lingling Zhang ◽  
Weiqi Zhao
Keyword(s):  
Bipartite Graph ◽  
Link Prediction ◽  
Music Recommendation

Complex-InversE: Improving bilinear knowledge graph embeddings by mapping into complex space

2021 ◽  
pp. 1-11
Author(s):  
Yukun Cao ◽  
Zeyu Miao
Keyword(s):  
Link Prediction ◽  
Complex Space ◽  
Prediction Method ◽  
Knowledge Graph ◽  
The Public ◽  
Low Dimensional ◽  
Link Information ◽  
Prediction Problems ◽  
Benchmark Evaluation ◽  
Graph Link

Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE.

scholarly journals A Similarity-Inclusive Link Prediction Based Recommender System Approach

2019 ◽  
Vol 25 (6) ◽  
pp. 62-69 ◽  
Author(s):  
Zuhal Kurt ◽  
Kemal Ozkan ◽  
Alper Bilge ◽  
Omer Nezih Gerek
Keyword(s):  
Recommender Systems ◽  
Bipartite Graph ◽  
Recommender System ◽  
Link Prediction ◽  
Prediction Method ◽  
Research Field ◽  
Complex Domain ◽  
Proposed Model ◽  
Prediction Techniques ◽  
Item Item

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.

Analysis of Link Prediction Method for Link Recovery in Mobile Ad-hoc Network

2017 ◽  
Vol 7 (5) ◽  
pp. 849-854
Author(s):  
◽  
Jatinder Kaur ◽  
Danvir Mandal ◽  
Rajneesh Talwar ◽  
◽  
...  
Keyword(s):  
Ad Hoc Network ◽  
Link Prediction ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Prediction Method ◽  
Mobile Ad Hoc

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 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

Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation

2019 ◽  
Vol 18 (01) ◽  
pp. 311-338 ◽  
Author(s):  
Lingling Zhang ◽  
Jing Li ◽  
Qiuliu Zhang ◽  
Fan Meng ◽  
Weili Teng
Keyword(s):  
Bipartite Graph ◽  
Link Prediction ◽  
Domain Knowledge ◽  
Attenuation Factor ◽  
Prediction Algorithm ◽  
Bipartite Network ◽  
Topological Similarity ◽  
Product Recommendation ◽  
Knowledge Based ◽  
Context Knowledge

In this paper, we propose domain knowledge-based link prediction algorithm in customer-product bipartite network to improve effectiveness of product recommendation in retail. The domain knowledge is classified into product domain knowledge and time context knowledge, which play an important part in link prediction. We take both of them into consideration in recommendation and form a unified domain knowledge-based link prediction framework. We capture product semantic similarity by ontology-based analysis and time attenuation factor from time context knowledge, then incorporate them into network topological similarity to form a new linkage measure. To evaluate the algorithm, we use a real retail transaction dataset from Food Mart. Experimental results demonstrate that the usage of domain knowledge in link prediction achieved significantly better performance.

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.

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