Distance dynamics based overlapping semantic community detection for node‐attributed networks-Reference-Cited by-同舟云学术

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Distance dynamics based overlapping semantic community detection for node‐attributed networks

Published:2020-04-14 Issue:2 Volume:37 Page:678-700
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Sun Heli¹²³^ORCID,Jia Xiaolin¹,Huang Ruodan²,Wang Pei¹,Wang Chenyu¹,Huang Jianbin⁴

Affiliation:

1. School of Computer Science and Technology Xi'an Jiaotong University Xi'an China

2. Shenzhen Research School Xi'an Jiaotong University Shenzhen China

3. School of Journalism and New Media Xi'an Jiaotong University Xi'an China

4. School of Computer Science and Technology Xidian University Xi'an China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12324

Reference44 articles.

1. DuHongxia SunHeli HuangJianbin SunZhongbin HeLiang ChengHong. Mining cohesive clusters with interpretations in labeled graphs. Paper presented at: Proceedings of the Pacific‐Asia Conference on Knowledge Discovery and Data Mining;2017:774‐785; Springer Cham.

2. MoserFlavia ColakRecep RafieyArash EsterMartin. Mining cohesive patterns from graphs with feature vectors. Paper presented at: Proceedings of the 2009 SIAM International Conference on Data Mining;2009:593‐604.

3. XuZhiqiang KeYiping WangYi ChengHong ChengJames. A model‐based approach to attributed graph clustering. Paper presented at: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data;2012:505‐516.

4. ZhouYang ChengHong YuJeffrey Xu. Clustering large attributed graphs: an efficient incremental approach. Paper presented at: Proceedings of the IEEE International Conference on Data Mining;2010:689‐698.

5. Graph clustering based on structural/attribute similarities

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