Towards human-like perception: Learning structural causal model in heterogeneous graph

Lin, Tianqianjin; Song, Kaisong; Jiang, Zhuoren; Kang, Yangyang; Yuan, Weikang; Li, Xurui; Sun, Changlong; Huang, Cui; Liu, Xiaozhong

doi:10.1016/j.ipm.2023.103600

Information Processing & Management

2024

DOI: 10.1016/j.ipm.2023.103600

|View full text |Cite

Towards human-like perception: Learning structural causal model in heterogeneous graph

Tianqianjin Lin,

Kaisong Song,

Zhuoren Jiang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Social Risks in the Era of Generative AI

Liu,

Lin,

Jiang

et al. 2024

Proceedings of the Association for Information Science and Tech

View full text Add to dashboard Cite

Generative AI (GAI) technologies have demonstrated human‐level performance on a vast spectrum of tasks. However, recent studies have also delved into the potential threats and vulnerabilities posed by GAI, particularly as they become increasingly prevalent in sensitive domains such as elections and education. Their use in politics raises concerns about manipulation and misinformation. Further exploration is imperative to comprehend the social risks associated with GAI across diverse societal contexts. In this panel, we aim to dissect the impact and risks posed by GAI on our social fabric, examining both technological and societal perspectives. Additionally, we will present our latest investigations, including the manipulation of ideologies using large language models (LLMs), the potential risk of AI self‐consciousness, the application of Explainable AI (XAI) to identify patterns of misinformation and mitigate their dissemination, as well as the influence of GAI on the quality of public discourse. These insights will serve as catalysts for stimulating discussions among the audience on this crucial subject matter, and contribute to fostering a deeper understanding of the importance of responsible development and deployment of GAI technologies.

show abstract

Social Risks in the Era of Generative AI

Liu,

Lin,

Jiang

et al. 2024

Proceedings of the Association for Information Science and Tech

View full text Add to dashboard Cite

show abstract

Unraveling and Mitigating Endogenous Task-oriented Spurious Correlations in Ego-graphs via Automated Counterfactual Contrastive Learning

Lin,

Kang,

Jiang

et al. 2025

Expert Systems with Applications

View full text Add to dashboard Cite

Mg-SubAgg: Multi-granularity Subgraph Aggregation with topology for GNN

Zhang,

Ye,

Zhang

et al. 2024

Information Sciences

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Towards human-like perception: Learning structural causal model in heterogeneous graph

Cited by 4 publications

References 52 publications

Social Risks in the Era of Generative AI

Social Risks in the Era of Generative AI

Unraveling and Mitigating Endogenous Task-oriented Spurious Correlations in Ego-graphs via Automated Counterfactual Contrastive Learning

Mg-SubAgg: Multi-granularity Subgraph Aggregation with topology for GNN

Contact Info

Product

Resources

About