# Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction

arXiv: https://arxiv.org/abs/2303.09590 · HTML: https://mlsanity.com/paper/2303.09590/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 109 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning](https://mlsanity.com/paper/1905.03911/) (2019)
- [DimReader: Axis lines that explain non-linear projections](https://mlsanity.com/paper/1710.00992/) (2017)
- [A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration](https://mlsanity.com/paper/1811.12199/) (2018)
- [Interactive Dimensionality Reduction for Comparative Analysis](https://mlsanity.com/paper/2106.15481/) (2021)
- [Measuring dependence powerfully and equitably](https://mlsanity.com/paper/1505.02213/) (2015)
- [A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions](https://mlsanity.com/paper/2002.10998/) (2020)
- [What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization](https://mlsanity.com/paper/1710.04328/) (2017)
- [Multivariate Data Explanation by Jumping Emerging Patterns Visualization](https://mlsanity.com/paper/2106.11112/) (2021)
- [The State of the Art in Multilayer Network Visualization](https://mlsanity.com/paper/1902.06815/) (2019)
- [A Visual Analytics Framework for Contrastive Network Analysis](https://mlsanity.com/paper/2008.00151/) (2020)
- [Network Comparison with Interpretable Contrastive Network Representation Learning](https://mlsanity.com/paper/2005.12419/) (2020)
- [Visual Neural Decomposition to Explain Multivariate Data Sets](https://mlsanity.com/paper/2009.05502/) (2020)
- [A Visual Analytics System for Water Distribution System Optimization](https://mlsanity.com/paper/2108.12540/) (2021)
- [Interactive Visual Pattern Search on Graph Data via Graph Representation Learning](https://mlsanity.com/paper/2202.09459/) (2022)

## Built on by (most cited first, 3 of 3)

- [GhostUMAP2: Measuring and Analyzing (r,d)-Stability of UMAP](https://mlsanity.com/paper/2507.17174/) (2025)
- [Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis](https://mlsanity.com/paper/2512.03296/) (2025)
- [A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes](https://mlsanity.com/paper/2401.03700/) (2024)

## Abstract (arXiv)

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.