Local search of communities in large graphs
By Wanyun Cui, Yanghua Xiao, Haixun Wang, Wei Wang. SIGMOD Conference, 2014
313 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/5a255880a9fa5e746282b5ee4b778047c81a00b0
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In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Local community search [166] assumes one query node at a time and expands the search space around it.”
- A survey of community search over big graphs (2019, methodology): “For simple graphs, we can divide them into two groups, where the first group [15,46,175] focuses on undirected graphs while the second group [66] only considers directed graphs.”
- Local Higher-Order Graph Clustering (2017, methodology): “ere are several methods for nding clusters containing a seed node beyond the personalized PageRank method considered here, including other graph diusions [8, 26], local spectral methods [32, 33], local modularity maximization [10], ow-based algorithms [36], and minimum degree maximization [11, 4”
- Effective Community Search for Large Attributed Graphs (2016, methodology): “Non-attributed [22, 11] [26, 5, 4, 15, 18] Attributed [34, 21, 20, 29, 23] ACQ (This paper)”