# A Two-Stage AI-Powered Motif Mining Method for Efficient Power System Topological Analysis

arXiv: https://arxiv.org/abs/2412.05957 · HTML: https://mlsanity.com/paper/2412.05957/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2024.

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

- [Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution](https://mlsanity.com/paper/2608.03878/) (2026)

## Abstract (arXiv)

Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power system is extremely computationally challenging and even infeasible, which undermines the value of motif analysis in practice. In this paper, we introduce a two-stage AI-powered motif mining method to enable efficient and wide-range motif analysis in power systems. In the first stage, a representation learning method with specially designed network structure and loss function is proposed to achieve ordered embedding for the power system topology, simplifying the subgraph isomorphic problem into a vector comparison problem. In the second stage, under the guidance of the ordered embedding space, a greedy-search-based motif growing algorithm is introduced to quickly obtain the motifs without traversal searching. A case study based on a power system database including 61 circuit models demonstrates the effectiveness of the proposed method.