A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Wu, Bingzhe; Li, Jintang; Yu, Junchi; Bian, Yatao; Zhang, Hengtong; Chen, CHaochao; Hou, Chengbin; Fu, Guoji; Chen, Liang; Xu, Tingyang; Yu, Rong; Zheng, Xiaolin; Huang, Junzhou; He, Ran; Sun, GUangyu; Cui, Peng; Zheng, Zibin; Liu, Zhe; Zhao, Peilin

doi:10.48550/arxiv.2205.10014

Cited by 6 publications

(6 citation statements)

References 88 publications

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“…The works [71] and [72] deal with privacy, fairness and explainability in the context of graph neural networks (GNNs). The authors mention in [71] a positive interaction between explainability and privacy and that there is also a connection with trust.…”

Section: Related Workmentioning

confidence: 99%

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Privacy explanations – A means to end-user trust

Brunotte

Specht

Chazette

et al. 2023

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

“…The authors mention in [71] a positive interaction between explainability and privacy and that there is also a connection with trust. However, [72] refers to possible problems in terms of privacy, when providing explanations for whitebox GNNs, since the model parameters are accessible.…”

Section: Related Workmentioning

confidence: 99%

Privacy explanations – A means to end-user trust

Brunotte

Specht

Chazette

et al. 2023

Journal of Systems and Software

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“…Interpretation in graph learning is in need as explaining predictions made by GNNs helps collect insights from graph-structured data [59,73]. Recently, an increasing number of approaches have been proposed to provide interpretation for GNNs.…”

Section: Related Workmentioning

confidence: 99%

Rumor Detection with Diverse Counterfactual Evidence

Zhang

Shi

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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The growth in social media has exacerbated the threat of fake news to individuals and communities. This draws increasing attention to developing efficient and timely rumor detection methods. The prevailing approaches resort to graph neural networks (GNNs) to exploit the post-propagation patterns of the rumor-spreading process. However, these methods lack inherent interpretation of rumor detection due to the black-box nature of GNNs. Moreover, these methods suffer from less robust results as they employ all the propagation patterns for rumor detection. In this paper, we address the above issues with the proposed Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our intuition is to exploit the diverse counterfactual evidence of an event graph to serve as multi-view interpretations, which are further aggregated for robust rumor detection results. Specifically, our method first designs a subgraph generation strategy to efficiently generate different subgraphs of the event graph. We constrain the removal of these subgraphs to cause the change in rumor detection results. Thus, these subgraphs naturally serve as counterfactual evidence for rumor detection. To achieve multi-view interpretation, we design a diversity loss inspired by Determinantal Point Processes (DPP) to encourage diversity among the counterfactual evidence. A GNN-based rumor detection model further aggregates the diverse counterfactual evidence discovered by the proposed DCE-RD to achieve interpretable and robust rumor detection results. Extensive experiments on two real-world datasets show the superior performance of our method.

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“…et al, 2008). Their growing use in critical sectors such as healthcare and fraud detection has escalated the need for explainability in their decision-making processes (Zhang et al, 2022a;Wu et al, 2022;Li et al, 2022). To meet this demand, a variety of explanation methods have been recently developed to interpret the behavior of GNN models.…”

Section: Preprint (A) Explanation Process (B) Ood Problemmentioning

confidence: 99%

hECA: the cell-centric assembly of a cell atlas

Chen

Luo

Gao

et al. 2021

Preprint

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The significance of building atlases of human cells as references for future biological and medical studies of human in health or disease has been well recognized. Comparing to the rapidly accumulation of single-cell data, there has been fewer published work on the information structure to assemble cell atlases, or on methods for using reference atlases once they are ready. Most existing cell atlas work organize single-cell gene expression data as a collection of individual files, allowing users to download selected data sheets, or to annotate query cells using models pretrained with the collected data. These features are useful as the basic use of cell atlases. More comprehensive uses of global cell atlases can be developed once data of cells from multiple organs across different studies can be assembled into one orchestrated data repository rather than a collection of data files. For this purpose, we presented a unified giant table or uGT to store and organize single-cell data from multiple studies into a single huge data repository, and a unified hierarchical annotation framework or uHAF to annotate cells from uncoordinated studies. Based on these technologies, we developed a system that enables users to design complex rules to recruit from the atlas cells that meet certain conditions, such as with desired expression range of a gene or multiple genes and with required organ, tissue or developmental origins, across multiple datasets that were otherwise unconnected. The conditions can be expressed as sophisticated logic criteria to pinpoint specific cells that cannot be easily spotted in traditional in vivo or in vitro cell sorting or in traditional searching in published data. We call this technology as in data cell sorting from cell atlases. With the increasing coverage of the cell atlas, this in data experiment paradigm will facilitate scientists to conduct investigations in the data space beyond the restrictions in traditional in vivo and in vitro experiments. In the current work, we collected scRNA-seq data of more than 1 million human cells from scattered studies and assembled them as a human Ensemble Cell Atlas or hECA using the proposed information structure, and provided comprehensive tools for in data experiments based on the atlas. Case examples on agile construction of atlases of particular cell types and on studying the side effects of targeted immune therapy drugs showed that in data cell sorting is an efficient and effective way for comprehensive discoveries. hECA provides a powerful platform for assembling massive scattered single-cell data into a unified atlas, and can serve as a prototype for building future cell atlases.

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A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Cited by 6 publications

References 88 publications

Privacy explanations – A means to end-user trust

Privacy explanations – A means to end-user trust

Rumor Detection with Diverse Counterfactual Evidence

hECA: the cell-centric assembly of a cell atlas

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