Graph meets probabilistic generation model: A new perspective for graph disentanglement
文献类型:期刊论文
作者 | Peng, Zouzhang1,2; Zheng, Shuai1,2; Zhu, Zhenfeng1,2; Liu, Zhizhe1,2; Cheng, Jian3; Dong, Honghui4; Zhao, Yao1,2 |
刊名 | PATTERN RECOGNITION |
出版日期 | 2024-04-01 |
卷号 | 148页码:11 |
ISSN号 | 0031-3203 |
关键词 | Graph representation learning Graph disentanglement Probabilistic generation model |
DOI | 10.1016/j.patcog.2023.110153 |
通讯作者 | Peng, Zouzhang(pengzouzhang@bjtu.edu.cn) ; Zheng, Shuai(zs1997@bjtu.edu.cn) ; Zhu, Zhenfeng(zhfzhu@bjtu.edu.cn) |
英文摘要 | Different from the existing graph disentanglement neural networks, we interpret the graph entanglement under a probabilistic generation framework in this paper. With this foundation, a Mixed Probabilistic Generation Model induced Graph Disentanglement Network (MPGD) is proposed. Considering the disentangled components corresponding to different factors as obeying specific distributions, a generalized probabilistic aggregation scheme among components is deduced theoretically. As a key part of the mixed probabilistic generative model, we provide a solution for estimating the mixture probabilities using self-attention and an in-depth analysis of its close connection with the classical EM parameter estimation method. Meanwhile, a way of probabilistic aggregation is formulated to obtain the node representation in embedding space. In addition, the prior mixture probabilities are formulated as an auxiliary factor-aware representation to facilitate the twin branch prediction. A variety of experiments show that MPGD achieves more competitive performance than some existing state-of-the-art methods while having ideal disentangling effects. The code implementation is available in https://github.com/GiorgioPeng/MPGD. |
资助项目 | National Natural Science Foundation of China[2018AAA0102101] ; [61976018] |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | ELSEVIER SCI LTD |
WOS记录号 | WOS:001128636200001 |
资助机构 | National Natural Science Foundation of China |
源URL | [http://ir.ia.ac.cn/handle/173211/54946] |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Peng, Zouzhang; Zheng, Shuai; Zhu, Zhenfeng |
作者单位 | 1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China 2.Network Technol, Beijing Key Lab Adv Informat Sci, Beijing 100044, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China |
推荐引用方式 GB/T 7714 | Peng, Zouzhang,Zheng, Shuai,Zhu, Zhenfeng,et al. Graph meets probabilistic generation model: A new perspective for graph disentanglement[J]. PATTERN RECOGNITION,2024,148:11. |
APA | Peng, Zouzhang.,Zheng, Shuai.,Zhu, Zhenfeng.,Liu, Zhizhe.,Cheng, Jian.,...&Zhao, Yao.(2024).Graph meets probabilistic generation model: A new perspective for graph disentanglement.PATTERN RECOGNITION,148,11. |
MLA | Peng, Zouzhang,et al."Graph meets probabilistic generation model: A new perspective for graph disentanglement".PATTERN RECOGNITION 148(2024):11. |
入库方式: OAI收割
来源:自动化研究所
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