提出BiGG-E模型，高效生成带权图并建模拓扑与权重联合分布。
Scalable Generative Modeling of Weighted Graphs
- 基于自回归机制，联合建模图结构与边权分布
- 支持n节点m边图在O((n+m)log n)时间内生成
- 适用于生物、化学等需复杂图生成的领域
加权图广泛存在于生物学、化学和社会科学中，推动了基于深度神经网络的抽象加权图生成模型的发展。然而，当前大多数深度生成模型要么仅针对无权图设计，难以扩展到加权结构，要么在引入边权时未考虑其与图拓扑的联合分布。此外，学习加权图分布需处理边与边权之间的复杂非局部依赖关系。我们提出一种自回归模型BiGG-E，作为BiGG模型的非平凡扩展，在保持稀疏性利用的同时，学习加权图的联合分布，可在O((n + m)log n)时间内生成含n个节点和m条边的加权图。模拟研究及多种基准数据集实验表明，BiGG-E在准确捕捉加权图分布方面表现最佳，同时具备可扩展性和计算高效性。
原文摘要 · Abstract (English)
Weighted graphs are ubiquitous throughout biology, chemistry, and the social sciences, motivating the development of generative models for abstract weighted graph data using deep neural networks. However, most current deep generative models are either designed for unweighted graphs and are not easily extended to weighted topologies or incorporate edge weights without consideration of a joint distribution with topology. Furthermore, learning a distribution over weighted graphs must account for complex nonlocal dependencies between both the edges of the graph and corresponding weights of each edge. We develop an autoregressive model BiGG-E, a nontrivial extension of the BiGG model, that learns a joint distribution over weighted graphs while still exploiting sparsity to generate a weighted graph with $n$ nodes and $m$ edges in $O((n + m)\log n)$ time. Simulation studies and experiments on a variety of benchmark datasets demonstrate that BiGG-E best captures distributions over weighted graphs while remaining scalable and computationally efficient.
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