# Visualization Tasks for Unlabeled Graphs

arXiv: https://arxiv.org/abs/2504.14115 · HTML: https://mlsanity.com/paper/2504.14115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 75 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [GraphWorld: Fake Graphs Bring Real Insights for GNNs](https://mlsanity.com/paper/2203.00112/) (2022)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs](https://mlsanity.com/paper/2107.01495/) (2021)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [Surfacing Visualization Mirages](https://mlsanity.com/paper/2001.02316/) (2020)
- [An information-theoretic, all-scales approach to comparing networks](https://mlsanity.com/paper/1804.03665/) (2018)
- [Challenges of Generating Structurally Diverse Graphs](https://mlsanity.com/paper/2409.18859/) (2024)
- [Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs](https://mlsanity.com/paper/1804.11242/) (2018)
- [Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition](https://mlsanity.com/paper/1510.08542/) (2015)
- [Friendship Paradox Redux: Your Friends Are More Interesting Than You](https://mlsanity.com/paper/1304.3480/) (2013)
- [Topology comparison of Twitter diffusion networks effectively reveals misleading information](https://mlsanity.com/paper/1905.03043/) (2019)
- [Group-Level Graph Visualization Taxonomy](https://mlsanity.com/paper/1403.7421/) (2014)
- [On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists](https://mlsanity.com/paper/1911.05031/) (2019)
- [Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations](https://mlsanity.com/paper/2408.01272/) (2024)
- [The Census-Stub Graph Invariant Descriptor](https://mlsanity.com/paper/2412.04582/) (2024)

## Abstract (arXiv)

We investigate tasks that can be accomplished with unlabeled graphs, which are graphs with nodes that do not have persistent or semantically meaningful labels attached. New visualization techniques to represent unlabeled graphs have been proposed, but more understanding of unlabeled graph tasks is required before these techniques can be adequately evaluated. Some network visualization tasks apply to both labeled and unlabeled graphs, but many do not translate between these contexts. We propose a data abstraction model that distinguishes the Unlabeled context from the increasingly semantically rich Labeled, Attributed, and Augmented contexts. We filter tasks collected and gleaned from the literature according to our data abstraction and analyze the surfaced tasks, leading to a taxonomy of abstract tasks for unlabeled graphs. Our task taxonomy is organized according to the Target data under consideration, the Action intended by the user, and the Scope of the data at play. We show the descriptive power of this task abstraction by connecting to concrete examples from previous frameworks, and connecting these abstractions to real-world problems. To showcase the evaluative power of the taxonomy, we perform a preliminary assessment across 6 different network visualization idioms for each task. For each combination of task and visual encoding, we consider the effort required from viewers, the likelihood of task success, and how both factors vary between small-scale and large-scale graphs. Supplemental materials are available at osf.io/e23mr.