# Local Intrinsic Dimensionality for Dynamic Graph Embeddings

arXiv: https://arxiv.org/abs/2411.16145 · HTML: https://mlsanity.com/paper/2411.16145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [dynnode2vec: Scalable Dynamic Network Embedding](https://mlsanity.com/paper/1812.02356/) (2018)
- [Local Intrinsic Dimensionality Measures for Graphs, with Applications to Graph Embeddings](https://mlsanity.com/paper/2208.11986/) (2022)
- [Hub-aware Random Walk Graph Embedding Methods for Classification](https://mlsanity.com/paper/2209.07603/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding](https://mlsanity.com/paper/2608.18762/) (2026)

## Abstract (arXiv)

The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent research efforts indicate that LID measures defined for graphs can improve graph representational learning methods based on random walks. In this paper, we discuss how NC-LID, a LID measure designed for static graphs, can be adapted for dynamic networks. Focusing on dynnode2vec as the most representative dynamic graph embedding method based on random walks, we examine correlations between NC-LID and the intrinsic quality of 10 real-world dynamic network embeddings. The obtained results show that NC-LID can be used as a good indicator of nodes whose embedding vectors do not tend to preserve temporal graph structure well. Thus, our empirical findings constitute the first step towards LID-aware dynamic graph embedding methods.