Robust graph learning on the web: Challenges, methods, and applications

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2026

Abstract

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web contexts. Next, we categorize current robust graph learning approaches, spanning data-level preprocessing to model-level adaptation and generalization, and discuss representative models in detail. We then showcase real-world case studies illustrating how robustness challenges emerge and how targeted methods can mitigate them in the web system. This tutorial offers researchers, engineers, and platform developers actionable strategies to safeguard graph-based AI in dynamic, high-impact web environments.

Keywords

Robust learning, Graph learning, AI on the Web

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

WWW Companion '26: Companion Proceedings of the ACM Web Conference 2026, Dubai, UAE June 29 - July 3

First Page

62

Last Page

65

Identifier

10.1145/3774905.3793923

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3774905.3793923

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