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Tianxiang Zhou

dblp:09/1447 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Security and privacy of machine learning · 100%
Artificial intelligence
1 paper
Graph learning · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning
graph neural network		 0.812024
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function · WSDM 2024
Security and privacy of machine learning › adversarial attack
graph adversarial attack		 0.812024
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function · WSDM 2024
Security and privacy of machine learning › adversarial attack
black-box attack		 0.212024
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function · WSDM 2024
Geometric modeling and processing
solid modeling		 0.012004
A hierarchically structured and constraint-based data model for intuitive and precise solid modeling in a virtual reality environment · Comput. Aided Des. 2004
Immersive interaction
virtual reality		 0.012004
A hierarchically structured and constraint-based data model for intuitive and precise solid modeling in a virtual reality environment · Comput. Aided Des. 2004

Methods — techniques the papers use, named apart from their topics

influence functions · 0.8influence function · 0.8
YearPublicationVenuePosition
2024 Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function
abstract
Graph neural network (GNN), the mainstream method to learn on graph data, is vulnerable to graph evasion attacks, where an attacker slightly perturbing the graph structure can fool trained GNN models. Existing work has at least one of the following drawbacks: 1) limited to directly attack two-layer GNNs; 2) inefficient; and 3) impractical, as they need to know full or part of GNN model parameters.
Binghui Wang, Minhua Lin, Tianxiang Zhou, Pan Zhou 0001, Ang Li 0005, Hai Li 0001, Yiran Chen 0001
 WSDM3
2004 A hierarchically structured and constraint-based data model for intuitive and precise solid modeling in a virtual reality environment
Weiyin Ma, Yongmin Zhong, Shiu Kit Tso, Tianxiang Zhou
 Comput. Aided Des.4