Joint Subgraph-to-Subgraph Transitions: Generalizing Triadic Closure for Powerful and Interpretable Graph Modeling

Hibshman, Justus; González, Daniel; Sikdar, Satyaki; Weninger, Tim

doi:10.1145/3437963.3441817

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PC-GNN can recognize both fake review and fake reviewer at the same time. Among these methods, PC-GNN, AO-GNN, and DeepFD are with better generalization ability (Du et al, 2020 ; Betlei et al, 2021 ; Hibshman et al, 2021 ). Some detailed parameter settings are unspecified and some datasets are not publicly available such as Xianyu Graph, Alibaba Review Graph, and Alibaba Group, thus limiting the repetition of these methods to some extent.…”

Section: Graph Learning For Fake Review Detectionmentioning

confidence: 99%

Graph Learning for Fake Review Detection

Ren

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Fake reviews have become prevalent on various social networks such as e-commerce and social media platforms. As fake reviews cause a heavily negative influence on the public, timely detection and response are of great significance. To this end, effective fake review detection has become an emerging research area that attracts increasing attention from various disciplines like network science, computational social science, and data science. An important line of research in fake review detection is to utilize graph learning methods, which incorporate both the attribute features of reviews and their relationships into the detection process. To further compare these graph learning methods in this paper, we conduct a detailed survey on fake review detection. The survey presents a comprehensive taxonomy and covers advancements in three high-level categories, including fake review detection, fake reviewer detection, and fake review analysis. Different kinds of fake reviews and their corresponding examples are also summarized. Furthermore, we discuss the graph learning methods, including supervised and unsupervised learning approaches for fake review detection. Specifically, we outline the unsupervised learning approach that includes generation-based and contrast-based methods, respectively. In view of the existing problems in the current methods and data, we further discuss some challenges and open issues in this field, including the imperfect data, explainability, model efficiency, and lightweight models.

show abstract

Section: Graph Learning For Fake Review Detectionmentioning

confidence: 99%