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Summary of Motif-driven Subgraph Structure Learning For Graph Classification, by Zhiyao Zhou et al.


Motif-driven Subgraph Structure Learning for Graph Classification

by Zhiyao Zhou, Sheng Zhou, Bochao Mao, Jiawei Chen, Qingyun Sun, Yan Feng, Chun Chen, Can Wang

First submitted to arxiv on: 13 Jun 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
A novel approach to improve graph structure and boost performance in downstream tasks is explored, focusing on graph-level tasks such as graph classification. Despite numerous methods proposed, most research has concentrated on node-level tasks, leaving a gap in understanding how to apply node-level Graph Structure Learning (GSL) to graph classification. The authors propose Motif-driven Subgraph Structure Learning for Graph Classification (MOSGSL), which incorporates a subgraph structure learning module and a motif-driven structure guidance module to capture key subgraph-level structural patterns. Experimental results demonstrate significant improvements over baselines, showcasing the flexibility and generalizability of MOSGSL.
Low GrooveSquid.com (original content) Low Difficulty Summary
Graphs are special kinds of data that have connections between pieces of information. A new way to make these graphs better is being explored. Right now, most people are only trying to make graphs for small things like individual objects or nodes on a graph. But what about bigger tasks, like classifying entire graphs? That’s the challenge being addressed in this research. The authors created a special method called MOSGSL that can help improve graph classification by selecting important parts of the graph and learning how to connect them correctly.

Keywords

» Artificial intelligence  » Classification