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Zhihe Qu

dblp:232/4747 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-6911-4933ORCID · reported

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

Databases, data management, data science and information retrieval · 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.

Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%
Databases, data mining, and information retrieval
1 paper
Web and social media mining · 100%

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

TopicWeightPapersLastEvidence papers
Web and social media mining
social network analysis
0.412019
LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks · IEEE Trans. Knowl. Data Eng. 2019
Graph algorithms and graph theory › graph clustering
community detection
0.412019
LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks · IEEE Trans. Knowl. Data Eng. 2019
Graph algorithms and graph theory › network analysis › network diffusion
label propagation
0.412019
LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks · IEEE Trans. Knowl. Data Eng. 2019
Graph algorithms and graph theory › graph clustering › community detection
overlapping community detection
0.412019
LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks · IEEE Trans. Knowl. Data Eng. 2019

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

neighbor node influence · 0.8label propagation · 0.8historical label preference · 0.8
YearPublicationVenuePosition
2019 LPANNI: Overlapping Community Detection Using Label Propagation in Large-Scale Complex Networks
abstract
Overlapping community structure is a significant feature of large-scale complex networks. Some existing community detection algorithms cannot be applied to large-scale complex networks due to their high time or space complexity. Label propagation algorithms were proposed for detecting communities in large-scale networks because of their linear time complexity, however most of which can only detect non-overlapping communities, or the results are inaccurate and unstable. Aimed at the defects, we proposed an improved overlapping community detection algorithm, LPANNI (Label Propagation Algorithm with Neighbor Node Influence), which detects overlapping community structures by adopting fixed label propagation sequence based on the ascending order of node importance and label update strategy based on neighbor node influence and historical label preferred strategy. Extensive experimental results in both real networks and synthetic networks show that, LPANNI can significantly improve the accuracy and stability of community detection algorithms based on label propagation in large-scale complex networks. Meanwhile, LPANNI can detect overlapping community structures in large-scale complex networks under linear time complexity.
Meilian Lu, Zhenglin Zhang, Zhihe Qu
IEEE Trans. Knowl. Data Eng.3