1. (Traditional) Generative Models for Graphs 图生成模型问题的研究动机: 我们此前的学习过程中,都假设图是已知的;但我们也会想通过graph generative model人工生成与真实图类似的synthetic graph,这可以让我们: ①了解图的形成过程。 ②预测图的演化。 ③生成新的图实例。 ④异常检测:检测一个图是否异常。 本...
-Graph Generative Model(GAM)除了可以直接生成新的药物分子外, -GAM还可以对已有的药物分子进行拓展和补全,进一步提升其某些性质. -除了具有极大经济价值的药物研发, GAM其实还有助于我们理解图本身的结构和性质. 这就类似于GAN,同为生成模型, 它们都可以更好的实现对data分布的描述. 毕竟你都可以生成数据了,必然...
The Small-World Model Small-World模型考虑如何在保持高聚类系数的同时保证较短的路径长度。 其首先根据某些规则生成一个网络,如环,然后对每条边都采取一定的概率连接到其他点,当概率为1时就是ER模型 Kronecker Graph Model Kronecker图模型考虑生成自相关的图,即给定一个邻接矩阵,对其做kronecker积生成更大的图 如果...
-Graph Generative Model(GAM)除了可以直接生成新的药物分子外, -GAM还可以对已有的药物分子进行拓展和补全,进一步提升其某些性质. -除了具有极大经济价值的药物研发, GAM其实还有助于我们理解图本身的结构和性质. 这就类似于GAN,同为生成模型, 它们都可以更好的实现对data分布的描述. 毕竟你都可以生成数据了,必然...
Thegraph-structuredconditioningofasequencemodelaffordsseveralbenefits,ludingfavorable computationalefficiency,inductivebias,andrepresentationalflexibility.Weaccomplishthefirsttwo byleveragingawell-evidencedfindinginproteinscience,namelythatlong-rangedependenciesin ...
algorithms to explore the protein sequence and structure space.Here, we explore an alternative, top-down framework for protein design that directly learns aconditional generative model for protein sequences given a specif ication of the target structure, whichis represented as a graph over the ...
A Deep Generative Model for Graph Layoutdoi:10.1109/TVCG.2019.2934396Kwan-Liu MaOh-Hyun Kwon
We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on...
13 A Data-Driven Graph Generative Model for Temporal Interaction Networks link:https://scholar.google.com.sg/scholar_url?url=https://par.nsf.gov/servlets/purl/10272483&hl=zh-TW&sa=X&ei=HCmOYrzrJ8nFywSFg47QCw&scisig=AAGBfm08x5PFAPPWh_nl6CoUzkqZBeJ3pg&oi=scholarr Abstract 本文提出了...
Scalable and Privacy-enhanced Graph Generative Model for Graph Neural Networks As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems. Unf...