The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
각 노드가 여러 유형의 속성으로 특징지어지는 다중 속성 그래프는 현실 세계 어디에서나 볼 수 있습니다. 노드 커뮤니티의 탐지 및 특성화는 다양한 애플리케이션에 중요한 영향을 미칠 수 있습니다. 이전 연구에서 이 작업을 시도했지만 여러 속성을 가진 그래프 구조를 통합하는 데 어려움이 있고 그래프에 노이즈가 존재하기 때문에 여전히 어려운 작업입니다. 따라서 본 연구에서는 속성 값 클러스터와 커뮤니티와 속성-값 클러스터 간의 강력한 상관 관계에 중점을 두었습니다. 제안된 연구에서 채택된 그래프 클러스터링 방법론은 다음과 같습니다. C면역 감지, A속성-값 클러스터링 및 파생 R커뮤니티와 속성-값 클러스터(간단히 CAR) 간의 관계. 이러한 개념을 바탕으로 제안된 다중 속성 그래프 클러스터링은 CAR 클러스터링으로 모델링됩니다. CAR 클러스터링을 달성하기 위해 협력 방식으로 CAR을 감지할 수 있는 NMF(Non-Negative Matrix Factorization)를 기반으로 CARNMF라는 새로운 알고리즘이 개발되었습니다. 실제 데이터세트를 사용한 실험에서 얻은 결과는 CARNMF가 기존의 비교 방법보다 커뮤니티와 속성-값 클러스터를 더 정확하게 감지할 수 있음을 보여줍니다. 또한 CARNMF를 사용하여 얻은 클러스터링 결과는 CARNMF가 커뮤니티와 속성-값 클러스터 간의 상관 관계를 통해 의미 있는 의미 설명이 포함된 유익한 커뮤니티를 성공적으로 감지할 수 있음을 나타냅니다.
Hiroyoshi ITO
University of Tsukuba
Takahiro KOMAMIZU
Nagoya University
Toshiyuki AMAGASA
University of Tsukuba
Hiroyuki KITAGAWA
University of Tsukuba
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Hiroyoshi ITO, Takahiro KOMAMIZU, Toshiyuki AMAGASA, Hiroyuki KITAGAWA, "Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 810-820, April 2019, doi: 10.1587/transinf.2018DAP0022.
Abstract: Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018DAP0022/_p
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@ARTICLE{e102-d_4_810,
author={Hiroyoshi ITO, Takahiro KOMAMIZU, Toshiyuki AMAGASA, Hiroyuki KITAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs},
year={2019},
volume={E102-D},
number={4},
pages={810-820},
abstract={Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.},
keywords={},
doi={10.1587/transinf.2018DAP0022},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs
T2 - IEICE TRANSACTIONS on Information
SP - 810
EP - 820
AU - Hiroyoshi ITO
AU - Takahiro KOMAMIZU
AU - Toshiyuki AMAGASA
AU - Hiroyuki KITAGAWA
PY - 2019
DO - 10.1587/transinf.2018DAP0022
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.
ER -