research-article

Inferring tag co-occurrence relationship across heterogeneous social networks

Authors:

Ming ZouAuthors Info & Claims

Volume 66, Issue C

Pages 512 - 524

https://doi.org/10.1016/j.asoc.2017.07.029

Published: 01 May 2018 Publication History

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Highlights

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Study the problem of the tag co-occurrence relationship prediction in Flickr heterogeneous social network.

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Present a novel data structure to capture the correlation between images and tags in the Flickr network.

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Leverage a weight path-based feature space to illustrate structural information in co-occurrence prediction.

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Denote a new similarity measure to capture the subtle similarity semantics between peer objects in heterogeneous networks.

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Our method can predict co-occurrence relationship with high accuracy compared with the state-of-the-art link prediction methods.

Abstract

Predicting the occurrence of links or interactions between objects in a network is a fundamental problem in network analysis. In this work, we address a novel problem about tag co-occurrence relationship prediction across heterogeneous networks. Although tag co-occurrence has recently become a hot research topic, many studies mainly focus on how to produce the personalized recommendation leveraging the tag co-occurrence relationship and most of them are considered in a homogeneous network. So far, few studies pay attention to how to predict tag co-occurrence relationship across heterogeneous networks. In order to solve this novel problem mentioned previously, we propose a novel three-step prediction approach. First, image-tag bins are generated by utilizing the TF-IDF like method, which help reduce the search space. And then, weight path-based topological features are systematically extracted from the network. At last, a supervised model is used to learn the best weights associated with different topological features in deciding the co-occurrence relationships. Experiments are performed on real-world dataset, the Flickr network, with comprehensive measurements. Experimental results demonstrate that weight path-based heterogeneous topological features have substantial advantages over commonly used link prediction approaches in predicting co-occurrence relations in Flickr networks.

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Cited By

Zhang YWang XYamasaki T(2023)Which account will you follow? Recommending influential accounts on social mediaMultimedia Tools and Applications10.1007/s11042-023-14538-382:22(34053-34074)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11042-023-14538-3
Deep SHu XKoutris P(2022)Ranked enumeration of join queries with projectionsProceedings of the VLDB Endowment10.14778/3510397.351040115:5(1024-1037)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.14778/3510397.3510401
Guo LZhang DSilva T(2020)Measure User Intimacy by Mining Maximum Information Transmission PathsComplexity10.1155/2020/23764512020Online publication date: 21-Mar-2020
https://dl.acm.org/doi/10.1155/2020/2376451

Index Terms

Inferring tag co-occurrence relationship across heterogeneous social networks
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 66, Issue C

May 2018

564 pages

ISSN:1568-4946

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2018

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Cited By

Zhang YWang XYamasaki T(2023)Which account will you follow? Recommending influential accounts on social mediaMultimedia Tools and Applications10.1007/s11042-023-14538-382:22(34053-34074)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s11042-023-14538-3
Deep SHu XKoutris P(2022)Ranked enumeration of join queries with projectionsProceedings of the VLDB Endowment10.14778/3510397.351040115:5(1024-1037)Online publication date: 18-May-2022
https://dl.acm.org/doi/10.14778/3510397.3510401
Guo LZhang DSilva T(2020)Measure User Intimacy by Mining Maximum Information Transmission PathsComplexity10.1155/2020/23764512020Online publication date: 21-Mar-2020
https://dl.acm.org/doi/10.1155/2020/2376451

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