Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Zenghua Xia

dblp:245/6045 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0002-4606-6606ORCID · reported

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

Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-author · 1 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.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Network and information security
1 paper
Web and mobile security · 87% Privacy and data protection · 13%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › retrieval models
generative retrieval
1.012026
CAT-ID2: Category-Tree Integrated Document Identifier Learning for Generative Retrieval In E-commerce · WSDM 2026
Web and mobile security › online social network abuse
malicious account detection
0.412019
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study · KDD 2019
Web and mobile security
online social network security
0.412019
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study · KDD 2019
Information retrieval
e-commerce search
0.312026
CAT-ID2: Category-Tree Integrated Document Identifier Learning for Generative Retrieval In E-commerce · WSDM 2026

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

malicious account detection · 0.4
YearPublicationVenuePosition
2026 CAT-ID2: Category-Tree Integrated Document Identifier Learning for Generative Retrieval In E-commerce
Yiqing Wu, Zenghua Xia, Fuzhen Zhuang, Zhao Zhang 0011, Fei Jiang 0009, Wei Lin 0022
WSDM5
2019 Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study
abstract
Malicious accounts are one of the biggest threats to the security and privacy of online social networks (OSNs). In this work, we study a new type of OSN, called privacy-centric mobile social network (PC-MSN), such as KakaoTalk and LINE, which has attracted billions of users recently. The design of PC-MSN is inspired to protect their users' privacy from strangers: (1) a stranger is not easy to send a friend request to a user who does not want to make friends with strangers; and (2) strangers cannot view a user's post. Such a design mitigates the security issue of malicious accounts. At the same time, it also brings the battleground between attackers and defenders to an earlier stage, i.e., making friendship, than the one studied in previous works. Also, previous defense proposals mostly rely on certain assumptions on the attacker, which may not be robust in the new PC-MSNs. As a result, previous malicious accounts detection approaches are less effective on a PC-MSN.
Zenghua Xia, Chang Liu 0021, Neil Zhenqiang Gong, Qi Li 0002, Yong Cui 0001, Dawn Song
KDD1