Summary of Scalable and Adaptive Spectral Embedding For Attributed Graph Clustering, by Yunhui Liu et al.
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
by Yunhui Liu, Tieke He, Qing Wu, Tao Zheng, Jianhua Zhao
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Scalable and Adaptive Spectral Embedding (SASE), a novel attributed graph clustering method that avoids parameter learning. SASE consists of three components: node features smoothing via k-order simple graph convolution, scalable spectral clustering using random Fourier features, and adaptive order selection. This approach not only captures global cluster structures but also exhibits linear time and space complexity relative to the graph size. Empirical results demonstrate the superiority of SASE, achieving a 6.9% improvement in ACC and a 5.87x speedup compared to the runner-up, S3GC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about grouping nodes in a special kind of graph into clusters. It’s hard to do this when the graph is very big because it takes too much computer time and memory. The authors created a new way called Scalable and Adaptive Spectral Embedding (SASE) that can handle large graphs without using up too many resources. SASE works by smoothing out node features, clustering nodes, and choosing the right order to do things in. It’s really good at finding clusters and is much faster than other methods. |
Keywords
» Artificial intelligence » Clustering » Embedding » Spectral clustering
