Low-dimensional statistical manifold embedding of directed graphs
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable space. Furthermore, we analyze the connection between the geometrical properties of such embedding and their efficient learning procedure. Extensive experiments show that our proposed embedding is better in preserving the global geodesic information of graphs, as well as outperforming existing embedding models on directed graphs in a variety of evaluation metrics, in an unsupervised setting.
Code (1)
Similar Papers 제목 키워드 기반
Estimating Vector Fields on Manifolds and the Embedding of Directed Graphs
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed w…
Graph EmbeddingDirected Graph Embeddings in Pseudo-Riemannian Manifolds
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed graphs can be effectively represented b…
Graph Representation LearningLink PredictionRepresentation LearningDirected Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design…
Graph EmbeddingGradient-Based Spectral Embeddings of Random Dot Product Graphs
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilitie…
Graph Representation LearningNetwork EmbeddingRepresentation LearningGraph Learning via Spectral Densification
Graph learning plays important role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, data clustering, and visualization, etc. For the …
BIG-bench Machine LearningClusteringDimensionality ReductionGraph Learning