Generative Modeling of Graphs via Joint Diffusion of Node and Edge
  Attributes
Nimrod Berman Eitan Kosman Dotan Di Castro Omri Azencot
Graph generation is integral to various engineering and scientific
disciplines. Nevertheless, existing methodologies tend to overlook the
generation of edge attributes. However, we identify critical applications where
edge attributes are essential, making prior methods potentially unsuitable in
such contexts. Moreover, while trivial adaptations are available, empirical
investigations reveal their limited efficacy as they do not properly model the
interplay among graph components. To address this, we propose a joint
score-based model of nodes and edges for graph generation that considers all
graph components. Our approach offers two key novelties: (i) node and edge
attributes are combined in an attention module that generates samples based on
the two ingredients; and (ii) node, edge and adjacency information are mutually
dependent during the graph diffusion process. We evaluate our method on
challenging benchmarks involving real-world and synthetic datasets in which
edge features are crucial. Additionally, we introduce a new synthetic dataset
that incorporates edge values. Furthermore, we propose a novel application that
greatly benefits from the method due to its nature: the generation of traffic
scenes represented as graphs. Our method outperforms other graph generation
methods, demonstrating a significant advantage in edge-related measures.