Efficient personalized community detection via genetic evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8178
- @InProceedings{Gao:2019:GECCO,
-
author = "Zheng Gao and Chun Guo and Xiaozhong Liu",
-
title = "Efficient personalized community detection via genetic
evolution",
-
booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
-
year = "2019",
-
editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
-
isbn13 = "978-1-4503-6111-8",
-
pages = "383--391",
-
address = "Prague, Czech Republic",
-
DOI = "doi:10.1145/3321707.3321711",
-
publisher = "ACM",
-
publisher_address = "New York, NY, USA",
-
month = "13-17 " # jul,
-
organisation = "SIGEVO",
-
keywords = "genetic algorithms, genetic programming, Personalized
community detection, Graph mining, Network analysis",
-
size = "9 pages",
-
abstract = "Personalized community detection aims to generate
communities associated with user need on graphs, which
benefits many downstream tasks such as node
recommendation and link prediction for users, etc. It
is of great importance but lack of enough attention in
previous studies which are on topics of
user-independent, semi-supervised, or top-K
user-centric community detection. Meanwhile, most of
their models are time consuming due to the complex
graph structure. Different from these topics,
personalized community detection requires to provide
higher-resolution partition on nodes that are more
relevant to user need while coarser manner partition on
the remaining less relevant nodes. In this paper, to
solve this task in an efficient way, we propose a
genetic model including an off-line and an on-line
step. In the offline step, the user-independent
community structure is encoded as a binary tree. And
subsequently an online genetic pruning step is applied
to partition the tree into communities. To accelerate
the speed, we also deploy a distributed version of our
model to run under parallel environment. Extensive
experiments on multiple datasets show that our model
outperforms the state-of-arts with significantly
reduced running time.",
-
notes = "Also known as \cite{3321711} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Zheng Gao
Chun Guo
Xiaozhong Liu
Citations