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Fengmin Tang

dblp:251/3074 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0002-0222-154XORCID · corroborated

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

Artificial intelligence and machine learning · 3 · 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.

Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Graph algorithms and graph theory
random graph models		 0.412020
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application · ICML 2020
Computational social science and digital humanities › network science
network evolution		 0.112020
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application · ICML 2020

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

simulation study · 0.9maximum likelihood estimation · 0.9
YearPublicationVenuePosition
2023 TDCA: improved optimization algorithm with degree distribution and communication traffic for the deployment of software components based on AUTOSAR architecture
Yanheng Liu 0001, Jingyi Jin, Fengmin Tang
 Soft Comput.7
2021 Lane departure warning algorithm based on probability statistics of driving habits
Jiaxin Si, Xuelong Yin, Zhenhai Gao, Young Shik Moon, Jinfeng Gong, Fengmin Tang
 Soft Comput.7
2020 The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application
abstract
This paper is concerned with statistical estimation of two preferential attachment models: the Buckley-Osthus model and the block preferential attachment model. We prove that the maximum likelihood estimates for both models are consistent. We perform simulation studies to corroborate our theoretical findings. We also apply both models to study the evolution of a real-world network. A list of open problems are presented.
Wenpin Tang, Fengmin Tang
 ICML3