Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 4; p. 1402
• Main Authors: Huang, Ru, Chen, Zijian, He, Jianhua, Chu, Xiaoli
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11-02-2022; MDPI
• Subjects: Cluster Analysis; Clustering; community detection; Content analysis; Dynamic characteristics; Forecasts and trends; graph representation learning; Graph representations; Graphical representations; Heuristic; Learning; Machine Learning; Markov chains; Network analysis; Neural Networks, Computer; relation assessment; Semantics; Social networks; Teaching methods; User generated content; user-generated contents; relation assessment; graph representation learning; community detection; user-generated contents
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22041402

Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non-textual contents, such as images and videos themselves, while ignoring the interrelationship between each user post's contents. In this paper, we propose a novel framework named community-aware dynamic heterogeneous graph embedding (CDHNE) for relationship assessment, capable of mining heterogeneous information, latent community structure and dynamic characteristics from user-generated contents (UGC), which aims to solve complex non-euclidean structured problems. Specifically, we introduce the Markov-chain-based metapath to extract heterogeneous contents and semantics in UGC. A edge-centric attention mechanism is elaborated for localized feature aggregation. Thereafter, we obtain the node representations from micro perspective and apply it to the discovery of global structure by a clustering technique. In order to uncover the temporal evolutionary patterns, we devise an encoder-decoder structure, containing multiple recurrent memory units, which helps to capture the dynamics for relation assessment efficiently and effectively. Extensive experiments on four real-world datasets are conducted in this work, which demonstrate that CDHNE outperforms other baselines due to the comprehensive node representation, while also exhibiting the superiority of CDHNE in relation assessment. The proposed model is presented as a method of breaking down the barriers between traditional UGC analysis and their abstract network analysis.