# Fast Butterfly-Core Community Search For Large Labeled Graphs

arXiv: https://arxiv.org/abs/2401.10642 · HTML: https://mlsanity.com/paper/2401.10642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2312.06337/) (2023)
- [Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs](https://mlsanity.com/paper/1406.2075/) (2014)
- [A Survey of Community Search Over Big Graphs](https://mlsanity.com/paper/1904.12539/) (2019)
- [CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation](https://mlsanity.com/paper/2312.01758/) (2023)
- [Butterfly-Core Community Search over Labeled Graphs](https://mlsanity.com/paper/2105.08628/) (2021)

## Abstract (arXiv)

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search methods need help identifying cross-group communities and suffer from efficiency issues, making them unsuitable for large graphs. This paper presents a fast community search model based on the Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate the importance of vertices within communities, allowing leader vertices to be rapidly updated to maintain cross-group cohesion. Moreover, we devised a more efficient method for updating vertex distances, which minimizes vertex visits and enhances operational efficiency. Extensive experiments on several real-world temporal graphs demonstrate the effectiveness and efficiency of this solution.