Summary of Heteromile: a Multi-level Graph Representation Learning Framework For Heterogeneous Graphs, by Yue Zhang et al.
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
by Yue Zhang, Yuntian He, Saket Gurukar, Srinivasan Parthasarathy
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Multi-Level Embedding framework of nodes on a heterogeneous graph, called HeteroMILE, tackles the issue of learning embeddings in large-scale graphs. Current solutions struggle with scalability due to high computational complexity. To overcome this limitation, HeteroMILE coarsens the original graph into a smaller size while preserving its backbone structure, reducing processing time and computational cost. The framework uses heterogeneous graph convolution neural networks for refinement, leading to improved quality of embeddings. Experimental results demonstrate a significant speedup (approximately 20x) and enhanced performance in link prediction and node classification tasks on various popular datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HeteroMILE is a new way to learn about connections between different things, like people or objects, using big networks. Right now, it’s hard to do this for really large networks because they take too long to process. HeteroMILE makes it faster and better by breaking down the network into smaller parts that are easier to work with, and then refining those parts back together again. |
Keywords
* Artificial intelligence * Classification * Embedding
