A New Social Recommender System Based on Link Prediction Across Heterogeneous Networks

Slokom, Manel; Ayachi, Raouia

doi:10.1007/978-3-319-59424-8_31

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Besides, users can obtain information from related objects or spread information. Predicting the possible links between objects and/or information, based on the known information [10], would enable us to better understand the evolution of social networks [11,12], also help business planners to make decisions, carry out precise services based on user connections, and achieve greater business value [4,5,9,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Shi

Liu

et al. 2021

Wireless Communications and Mobile Computing

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Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

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Section: Introductionmentioning

confidence: 99%

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Shi

Liu

et al. 2021

Wireless Communications and Mobile Computing

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“…This study is of great importance, not only in revealing the evolution of social networks, but also benefiting network management, such as promoting useful links or prohibiting harmful interactions. For instance, a recommendation system [7], as a typical application of temporal links prediction, is designed for individuals to make friends or purchase goods via efficient predicted results, which brings considerable benefits for corporations.…”

Section: Introductionmentioning

confidence: 99%

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

2020

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Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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“…Link prediction [1]- [3] is a common task which aims at finding the possible and unobservable associations (or links) between two objects by learning the known networkstructured data of the graph. It is very demanding in many real-world scenarios, such as social network [4], recommender systems [5], protein function prediction [6], and many other industry applications. There are many studies [7], [8] have been proposed for dealing with this task, which target to predict interactive relationships between different node objects by capturing the structural information of graph nodes.…”

Section: Introductionmentioning

confidence: 99%

MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

et al. 2019

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Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.

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A New Social Recommender System Based on Link Prediction Across Heterogeneous Networks

Cited by 4 publications

References 13 publications

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

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