Cross Network Representation Matching with Outliers

Hou, Mengshu; Ren, Jing; Febrinanto, Falih Gozi; Shehzad, Ahsan; Xia, Feng

doi:10.1109/icdmw53433.2021.00124

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…L encoder = L recon + αL en reg (7) where α is a hyper-parameter. After minimizing the loss L encoder and updating all weights and biases, we can obtain C and define the strength of edge between v i and v j as follows.…”

Section: Link Strength Learningmentioning

confidence: 99%

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Fairness-Aware Predictive Graph Learning in Social Networks

Wang

Febrinanto

et al. 2022

Mathematics

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Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data.

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Section: Link Strength Learningmentioning

confidence: 99%

“…To learn graph data representation, every relationship in the graph should be converted into a feature space that can be further processed to address the downstream tasks [6,7]. However, by merely learning graph-structured data, the embedding space learned by the model may be historically biased.…”

Section: Introductionmentioning

confidence: 99%

Fairness-Aware Predictive Graph Learning in Social Networks

Wang

Febrinanto

et al. 2022

Mathematics

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“…Research on fake reviews have grown noticeably in the last 5 years, and Figure 1 presents the research status by showing the number of papers with keyword “fake reviews” on SCI, EI, and DBLP, respectively. The anonymity characteristic of the Internet makes it challenging for the websites to handle fake reviews, and the high volume of freelancers and botnets worsen this situation (Wang et al, 2019 ; Wen et al, 2020 ; Hou et al, 2021 ; Li et al, 2021b ). Moreover, fake reviewers adopt camouflage techniques to hide their identities (Hooi et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Graph Learning for Fake Review Detection

Ren

et al. 2022

Front. Artif. Intell.

Self Cite

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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.

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Cross Network Representation Matching with Outliers

Cited by 2 publications

References 32 publications

Fairness-Aware Predictive Graph Learning in Social Networks

Fairness-Aware Predictive Graph Learning in Social Networks

Graph Learning for Fake Review Detection

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