Luyue Wang

dblp:380/0909 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
0009-0004-8944-0682ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 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
Data mining · 75% Graph data management · 25%

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

TopicWeightPapersLastEvidence papers
Graph data management
attributed graph
0.812024
Bootstrap Deep Metric for Seed Expansion in Attributed Networks · SIGIR 2024
Data mining › structured data mining › graph mining
community detection
0.812024
Bootstrap Deep Metric for Seed Expansion in Attributed Networks · SIGIR 2024
Data mining › structured data mining
graph mining
0.812024
Bootstrap Deep Metric for Seed Expansion in Attributed Networks · SIGIR 2024
Data mining › structured data mining › graph mining › community detection
seed expansion
0.812024
Bootstrap Deep Metric for Seed Expansion in Attributed Networks · SIGIR 2024

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

deep metric learning · 0.8bootstrap learning · 0.8
YearPublicationVenuePosition
2025 Representative negative sampling for graph positive-unlabeled learning
Luyue Wang, Xinyuan Feng, Chunquan Liang
Neurocomputing1
2025 Graph positive-unlabeled learning via Bootstrapping Label Disambiguation
Chunquan Liang, Luyue Wang, Xinyuan Feng, Yuying Cheng, Shirui Pan, Hongming Zhang 0002
Neural Networks2
2024 Bootstrap Deep Metric for Seed Expansion in Attributed Networks
abstract
Seed expansion tasks play an important role in various network applications such as recommendation systems, social network analysis, and bioinformatics. Given a network and a small group of examples as seeds, these tasks involve identifying additional members of interest from the same community. While most existing expansion methods focus on defining a fixed metric function based on the network structure alone, they often overlook the rich content associated with nodes in attributed networks.
Chunquan Liang, Qiankun Chen, Xinyuan Feng, Luyue Wang, Hongming Zhang 0002
SIGIR5