# EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection

arXiv: https://arxiv.org/abs/1808.09074 · HTML: https://mlsanity.com/paper/1808.09074/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 18 of 18)

- [AttentionViz: A Global View of Transformer Attention](https://mlsanity.com/paper/2305.03210/) (2023)
- [WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings](https://mlsanity.com/paper/2306.09328/) (2023)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces](https://mlsanity.com/paper/2202.02641/) (2022)
- [Visual Comparison of Language Model Adaptation](https://mlsanity.com/paper/2208.08176/) (2022)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embedding](https://mlsanity.com/paper/2106.12839/) (2021)
- [VideoMap: Supporting Video Editing Exploration, Brainstorming, and Prototyping in the Latent Space](https://mlsanity.com/paper/2211.12492/) (2022)
- [VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations](https://mlsanity.com/paper/2104.02797/) (2021)
- [EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding](https://mlsanity.com/paper/2308.01329/) (2023)
- [k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis](https://mlsanity.com/paper/2312.04024/) (2023)
- [Inspecting the Process of Bank Credit Rating via Visual Analytics](https://mlsanity.com/paper/2108.03011/) (2021)
- [GenLens: A Systematic Evaluation of Visual GenAI Model Outputs](https://mlsanity.com/paper/2402.03700/) (2024)
- [KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models](https://mlsanity.com/paper/2603.29689/) (2026)
- [GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks](https://mlsanity.com/paper/2406.04548/) (2024)
- [Simbanex: Similarity-based Exploration of IEEE VIS Publications](https://mlsanity.com/paper/2409.00478/) (2024)
- [TopoAlign: Topology-Aware Visual Representation Alignment](https://mlsanity.com/paper/2605.25541/) (2026)
- [LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks](https://mlsanity.com/paper/2607.21941/) (2026)

## Abstract (arXiv)

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.