Kang, Y., Pu, B., Kou, Y. et al. (6 more authors) (2024) A deep graph network with multiple similarity for user clustering in human–computer interaction. ACM Transactions on Multimedia Computing, Communications and Applications, 20 (2). pp. 1-20. ISSN 1551-6857
Abstract
User counterparts, such as user attributes in social networks or user interests, are the keys to more natural Human–Computer Interaction (HCI). In addition, users’ attributes and social structures help us understand the complex interactions in HCI. Most previous studies have been based on supervised learning to improve the performance of HCI. However, in the real world, owing to signal malfunctions in user devices, large amounts of abnormal information, unlabeled data, and unsupervised approaches (e.g., the clustering method) based on mining user attributes are particularly crucial. This paper focuses on improving the clustering performance of users’ attributes in HCI and proposes a deep graph embedding network with feature and structure similarity (called DGENFS) to cluster users’ attributes in HCI applications based on feature and structure similarity. The DGENFS model consists of a Feature Graph Autoencoder (FGA) module, a Structure Graph Attention Network (SGAT) module, and a Dual Self-supervision (DSS) module. First, we design an attributed graph clustering method to divide users into clusters by making full use of their attributes. To take full advantage of the information of human feature space, a k-neighbor graph is generated as a feature graph based on the similarity between human features. Then, the FGA and SGAT modules are utilized to extract the representations of human features and topological space, respectively. Next, an attention mechanism is further developed to learn the importance weights of different representations to effectively integrate human features and social structures. Finally, to learn cluster-friendly features, the DSS module unifies and integrates the features learned from the FGA and SGAT modules. DSS explores the high-confidence cluster assignment as a soft label to guide the optimization of the entire network. Extensive experiments are conducted on five real-world data sets on user attribute clustering. The experimental results demonstrate that the proposed DGENFS model achieves the most advanced performance compared with nine competitive baselines.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in ACM Transactions on Multimedia Computing, Communications and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Attributed graph clustering; cluster-friendly features; deep graph embedding; self-supervision module |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 May 2022 09:30 |
Last Modified: | 12 Jul 2024 15:39 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3549954 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186847 |