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Jiaming Mu

dblp:283/4445 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2021
—ORCID · unresolved

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

Security and privacy · 2 · 1 first-author · 2 since 2021

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
2 papers
Security and privacy of machine learning · 100%
Artificial intelligence
1 paper
Graph learning · 100%
Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Security and privacy of machine learning
adversarial attack		 0.512021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021
Security and privacy of machine learning › adversarial attack
black-box attack		 0.512021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021
Security and privacy of machine learning › adversarial attack
graph adversarial attack		 0.512021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021
Security and privacy of machine learning › graph neural network security
graph classification attack		 0.512021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021
Security and privacy of machine learning › adversarial attack › black-box attack
hard-label attack		 0.512021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021
Security and privacy of machine learning
poisoning attack		 0.512021
Data Poisoning Attacks to Deep Learning Based Recommender Systems · NDSS 2021
Machine learning › Graph learning
graph neural network		 0.112021
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks · CCS 2021

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

poisoning attack · 1.0deep learning · 1.0black-box attack · 1.0adversarial attack · 1.0
YearPublicationVenuePosition
2021 A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
abstract
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph structure related tasks such as node classification and graph classification. However, GNNs are vulnerable to adversarial attacks. Existing works mainly focus on attacking GNNs for node classification; nevertheless, the attacks against GNNs for graph classification have not been well explored.
Jiaming Mu, Binghui Wang, Qi Li 0002, Kun Sun 0001, Mingwei Xu 0001, Zhuotao Liu
 CCS1
2021 Data Poisoning Attacks to Deep Learning Based Recommender Systems
Hai Huang 0014, Jiaming Mu, Neil Zhenqiang Gong, Qi Li 0002, Bin Liu 0045
 NDSS2