Can GAN Learn Topological Features of a Graph?
This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generative adversarial networks (GANs) can successfully capture topological features of any arbitrary graph, and rank edge sets by different stages according to their contribution to topology reconstruction. Moreover, in addition to acting as an indicator of graph reconstruction, we find that these stages can also preserve important topological features in a graph.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph ReconstructionSimilar Papers 제목 키워드 기반
Learning Graph Topological Features via GAN
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input g…
Graph Topological Features via GAN
Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via …
Enhancing Graph Representation Learning with Localized Topological Features
Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. …
Graph LearningGraph Representation LearningLink PredictionNode Classification+1Flexible Mesh Segmentation via Reeb Graph Representation of Geometrical and Topological Features
This paper presents a new mesh segmentation method that integrates geometrical and topological features through a flexible Reeb graph representation. The algorithm consists of three phases: construction of the Reeb graph…
Computational EfficiencySegmentationExploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Graph emb…
Graph EmbeddingGraph Mining