TD-GEN: Graph Generation With Tree Decomposition
원문 초록
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation. The framework includes a permutation invariant tree generation model which forms the backbone of graph generation. Tree nodes are supernodes, each representing a cluster of nodes in the graph. Graph nodes and edges are incrementally generated inside the clusters by traversing the tree supernodes, respecting the structure of the tree decomposition, and following node sharing decisions between the clusters. Finally, we discuss the shortcomings of standard evaluation criteria based on statistical properties of the generated graphs as performance measures. We propose to compare the performance of models based on likelihood. Empirical results on a variety of standard graph generation datasets demonstrate the superior performance of our method.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Open World Scene Graph Generation using Vision Language Models
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding
Directed Graph Grammars for Sequence-based Learning
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study
GCAL: Adapting Graph Models to Evolving Domain Shifts
토론
이 논문과 연결된 포럼 글이 아직 없습니다.