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
서명된 네트워크는 현실 세계 어디에나 존재합니다. 서명된 네트워크에서 커뮤니티 감지 문제를 연구하는 것은 매우 중요합니다. 일반적으로 서명된 네트워크의 노드 행동은 합리적이며, 이는 커뮤니티 형성 과정을 모델링하는 데 사용할 수 있는 게임 이론의 플레이어와 일치합니다. 서명되지 않은 네트워크와 달리 서명된 네트워크에는 긍정적인 가장자리와 부정적인 가장자리가 모두 포함되어 각각 친구와 적의 관계를 나타냅니다. 커뮤니티 형성 과정에서 노드는 일반적으로 친구와 동일한 커뮤니티에 있고 적과 다른 커뮤니티에 속하도록 선택합니다. 이 아이디어를 바탕으로 우리는 서명된 네트워크에서 커뮤니티 탐지 문제를 해결하기 위한 게임 이론 모델을 제안했습니다. 노드를 플레이어로 삼아 커뮤니티 내부와 외부의 양수 에지와 음수 에지 수를 기반으로 이득 함수를 구축하고 내쉬 균형점의 존재를 증명합니다. 이와 같이 게임이 내쉬 균형 상태에 도달하면 모든 노드에 대한 최적의 전략 공간은 최종 커뮤니티 분할의 결과입니다. 우리 방법의 성능을 체계적으로 조사하기 위해 합성 네트워크와 실제 네트워크 모두에 대한 정교한 실험이 수행됩니다. 실험 결과는 우리의 방법이 기존의 다른 알고리즘보다 더 정확할 뿐만 아니라 잡음에 더 강하다는 것을 보여줍니다.
Shuaihui WANG
University of PLA
Guyu HU
Army Engineering University of PLA
Zhisong PAN
Army Engineering University of PLA
Jin ZHANG
University of PLA,Army Military Transportation University
Dong LI
University of PLA
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Shuaihui WANG, Guyu HU, Zhisong PAN, Jin ZHANG, Dong LI, "A Game-Theoretic Approach for Community Detection in Signed Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 6, pp. 796-807, June 2019, doi: 10.1587/transfun.E102.A.796.
Abstract: Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.796/_p
부
@ARTICLE{e102-a_6_796,
author={Shuaihui WANG, Guyu HU, Zhisong PAN, Jin ZHANG, Dong LI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Game-Theoretic Approach for Community Detection in Signed Networks},
year={2019},
volume={E102-A},
number={6},
pages={796-807},
abstract={Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.},
keywords={},
doi={10.1587/transfun.E102.A.796},
ISSN={1745-1337},
month={June},}
부
TY - JOUR
TI - A Game-Theoretic Approach for Community Detection in Signed Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 796
EP - 807
AU - Shuaihui WANG
AU - Guyu HU
AU - Zhisong PAN
AU - Jin ZHANG
AU - Dong LI
PY - 2019
DO - 10.1587/transfun.E102.A.796
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2019
AB - Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.
ER -