文献类型：期刊文献

英文题名：Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning

作者：Huang, Ru[1];Chen, Zijian[1];He, Jianhua[2];Chu, Xiaoli[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England;[3]Univ Sheffield, Dept Elect & Elect Engn, Sheffield S10 3JD, S Yorkshire, England

年份：2022

卷号：22

期号：4

外文期刊名：SENSORS

收录：;EI(收录号：20220711619090);WOS:【SCI-EXPANDED(收录号:WOS:000765175100001)】；

基金：This work was supported in part by National Natural Science Foundation of China under Grant 61673178 and 61922063; in part by Natural Science Foundation of Shanghai under Grant 20ZR1413800; in part by European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 824019 and 101022280.

语种：英文

外文关键词：user-generated contents; relation assessment; community detection; graph representation learning

摘要：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.