提出分阶段生成异构图的新方法，提升复杂关系建模能力。
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling
- 分两阶段生成：先建骨架图再填充节点特征
- 在IMDB和DBLP数据集上生成图的类型分布与特征匹配度高
- 适合需要真实异构图数据的研究者使用
异构图广泛存在于社交网络、推荐系统和生物网络等领域。与同质图不同，异构图包含多种类型的节点和边，分别代表不同实体与关系。由于需同时建模节点类型分布及每类节点的特征分布，生成真实异构图极具挑战。本文提出一种两级分层结构：第一阶段基于扩散模型生成带有节点类型的骨架图；第二阶段采用编码器-采样器结构为节点分配类型特异性特征，特征从节点特征池中采样，并通过判别器指导生成器训练。在IMDB和DBLP子集上的大量实验验证了该方法的有效性，并揭示了各组件的必要性。
原文摘要 · Abstract (English)
Heterogeneous graphs are present in various domains, such as social networks, recommendation systems, and biological networks. Unlike homogeneous graphs, heterogeneous graphs consist of multiple types of nodes and edges, each representing different entities and relationships. Generating realistic heterogeneous graphs that capture the complex interactions among diverse entities is a difficult task due to several reasons. The generator has to model both the node type distribution along with the feature distribution for each node type. In this paper, we look into solving challenges in heterogeneous graph generation, by employing a two phase hierarchical structure, wherein the first phase creates a skeleton graph with node types using a prior diffusion based model and in the second phase, we use an encoder and a sampler structure as generator to assign node type specific features to the nodes. A discriminator is used to guide training of the generator and feature vectors are sampled from a node feature pool. We conduct extensive experiments with subsets of IMDB and DBLP datasets to show the effectiveness of our method and also the need for various architecture components.
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