EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection

by   Quan Li, et al.

Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.


Network2Vec Learning Node Representation Based on Space Mapping in Networks

Complex networks represented as node adjacency matrices constrains the a...

Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network

Recently network embedding has gained increasing attention due to its ad...

Understanding graph embedding methods and their applications

Graph analytics can lead to better quantitative understanding and contro...

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

Modern machine learning techniques commonly rely on complex, high-dimens...

A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information

We propose a general framework for visualizing any intermediate embeddin...

Visual Comparison of Language Model Adaptation

Neural language models are widely used; however, their model parameters ...

The Utility of Decorrelating Colour Spaces in Vector Quantised Variational Autoencoders

Vector quantised variational autoencoders (VQ-VAE) are characterised by ...

Please sign up or login with your details

Forgot password? Click here to reset