An improved multi-view attention network inspired by coupled P system for node classification.
An improved multi-view attention network inspired by coupled P system for node classification.
复制标题
受节点分类耦合 P 系统启发的改进多视图注意网络
DOI：
10.1371/journal.pone.0267565
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
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作者：
Most of the existing graph embedding methods are used to describe the single view network and solve the single relation in the network. However, the real world is made up of networks with multiple views of complex relationships, and the existing methods can no longer meet the needs of people. To solve this problem, we propose a novel multi-view attention network inspired by coupled P system(MVAN-CP) to deal with node classification. More specifically, we design a multi-view attention network to extract abundant information from multiple views in the network and obtain a learning representation for each view. To enable the views to collaborate, we further apply attention mechanism to facilitate the view fusion process. Taking advantage of the maximum parallelism of P system, the process of learning and fusion will be realized in the coupled P system, which greatly improves the computational efficiency. Experiments on real network data sets indicate that our model is effective.
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DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
影响因子：
3.8
作者：
Ceterchi, Rodica;Orellana-Martin, David;Zhang, Gexiang
通讯作者：
Zhang, Gexiang
影响因子：
1.1
作者：
Martín-Vide, C;Paun, G;Rodríguez-Patón, A
通讯作者：
Rodríguez-Patón, A
利用模糊类组织P系统进行电力系统故障诊断
影响因子：
6.5
作者：
Peng, Hong;Wang, Jun;Riscos-Nunez, Agustin
通讯作者：
Riscos-Nunez, Agustin
影响因子：
--
作者：
Scarselli, Franco;Gori, Marco;Monfardini, Gabriele
通讯作者：
Monfardini, Gabriele