Summary of Lspi: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identification, by Yufei Zhao et al.
LSPI: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identification
by Yufei Zhao, Shiduo Wang, Hua Duan
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel heterogeneous graph neural network (HGNN) algorithm, called LSPI, for classification and aggregation tasks on large-scale heterogeneous information networks (HINs). Existing HGNNs rely heavily on meta-paths to capture semantic information in HINs, but neglect the properties of these meta-paths. The authors study three popular datasets and find significant differences in neighbor connections across various meta-paths, which can lead to noise interference issues. To address this, LSPI divides meta-paths into large and small neighbor paths using a path discriminator. It then selects nodes with high similarity from topology and feature perspectives for filtered large neighbor paths and passes them through different graph convolution components. The algorithm aggregates features under subgraphs and fuses them using subgraph-level attention to generate final node embeddings. Experimental results demonstrate the superiority of LSPI, and the authors provide suggestions on retaining nodes in large neighbor paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to analyze big networks that contain different types of information (like people, places, and things). Existing methods focus on finding patterns in these networks, but don’t consider how those patterns are connected. The authors studied three real-world datasets and found that some connections between nodes are much more important than others. They created a new algorithm called LSPI to take this into account. LSPI looks at the network and divides it into smaller parts based on the importance of each connection. It then combines information from these smaller parts to create a complete picture of the network. The authors tested their algorithm and found that it works better than other methods. |
Keywords
* Artificial intelligence * Attention * Classification * Graph neural network
