Summary of Co-neighbor Encoding Schema: a Light-cost Structure Encoding Method For Dynamic Link Prediction, by Ke Cheng et al.
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction
by Ke Cheng, Linzhi Peng, Junchen Ye, Leilei Sun, Bowen Du
First submitted to arxiv on: 30 Jul 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 In this paper, researchers propose a novel approach to structure encoding in temporal graphs, addressing the issue of high computational costs caused by repeated feature computation due to graph evolution. The Co-Neighbor Encoding Schema (CNES) stores information in memory to avoid redundant calculations and uses a hashtable-based memory for efficient structure feature construction and updating. Additionally, CNES introduces Temporal-Diverse Memory to generate long-term and short-term structure encoding for neighbors with different structural information. The proposed method is evaluated on thirteen public datasets, demonstrating its effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in understanding graphs that change over time. Right now, it takes a lot of computer power to figure out the important features of these changing graphs. To fix this, scientists created a new way to store information so that computers don’t have to keep re-calculating everything. They also came up with a new way to use memory efficiently to make things faster. This helps when learning about patterns in dynamic graphs. The method was tested on many real-world datasets and showed great results. |
