FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks.
It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2015-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4474961?pdf=render |
id |
doaj-46106fae169e4009bf2f5128ea8f10c2 |
---|---|
record_format |
Article |
spelling |
doaj-46106fae169e4009bf2f5128ea8f10c22020-11-24T21:24:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01106e013008610.1371/journal.pone.0130086FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks.Jing YangLimin ChenJianpei ZhangIt is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.http://europepmc.org/articles/PMC4474961?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jing Yang Limin Chen Jianpei Zhang |
spellingShingle |
Jing Yang Limin Chen Jianpei Zhang FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. PLoS ONE |
author_facet |
Jing Yang Limin Chen Jianpei Zhang |
author_sort |
Jing Yang |
title |
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. |
title_short |
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. |
title_full |
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. |
title_fullStr |
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. |
title_full_unstemmed |
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. |
title_sort |
fctclus: a fast clustering algorithm for heterogeneous information networks. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2015-01-01 |
description |
It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification. |
url |
http://europepmc.org/articles/PMC4474961?pdf=render |
work_keys_str_mv |
AT jingyang fctclusafastclusteringalgorithmforheterogeneousinformationnetworks AT liminchen fctclusafastclusteringalgorithmforheterogeneousinformationnetworks AT jianpeizhang fctclusafastclusteringalgorithmforheterogeneousinformationnetworks |
_version_ |
1725988043152687104 |