# Seedless Graph Matching via Tail of Degree Distribution for Correlated Erdos-Renyi Graphs

arXiv: https://arxiv.org/abs/1907.06334 · HTML: https://mlsanity.com/paper/1907.06334/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Exact Matching of Random Graphs with Constant Correlation](https://mlsanity.com/paper/2110.05000/) (2021)
- [Random Graph Matching with Improved Noise Robustness](https://mlsanity.com/paper/2101.11783/) (2021)
- [Matching recovery threshold for correlated random graphs](https://mlsanity.com/paper/2205.14650/) (2022)
- [Detection threshold for correlated ErdHos-R'enyi graphs via densest subgraphs](https://mlsanity.com/paper/2203.14573/) (2022)
- [A polynomial-time iterative algorithm for random graph matching with non-vanishing correlation](https://mlsanity.com/paper/2306.00266/) (2023)
- [A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation](https://mlsanity.com/paper/2212.13677/) (2022)
- [The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime](https://mlsanity.com/paper/2402.15095/) (2024)
- [Efficiently matching random inhomogeneous graphs via degree profiles](https://mlsanity.com/paper/2310.10441/) (2023)
- [Robust Random Graph Matching in Dense Graphs via an Approximate Message Passing Type Algorithm](https://mlsanity.com/paper/2412.16457/) (2024)

## Abstract (arXiv)

The network alignment (or graph matching) problem refers to recovering the node-to-node correspondence between two correlated networks. In this paper, we propose a network alignment algorithm which works without using a seed set of pre-matched node pairs or any other auxiliary information (e.g., node or edge labels) as an input. The algorithm assigns structurally innovative features to nodes based on the tail of empirical degree distribution of their neighbor nodes. Then, it matches the nodes according to these features. We evaluate the performance of proposed algorithm on both synthetic and real networks. For synthetic networks, we generate Erdos-Renyi graphs in the regions of Theta(log(n)/n) and Theta(log2(n)/n), where a previous work theoretically showed that recovering is feasible in sparse Erdos-Renyi graphs if and only if the probability of having an edge between a pair of nodes in one of the graphs and also between the corresponding nodes in the other graph is in the order of Omega(log(n)/n), where n is the number of nodes. Experiments on both real and synthetic networks show that it outperforms previous works in terms of probability of correct matching.