Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

• URL: http://arxiv.org/abs/2402.05011v3
• Date: Tue, 18 Jun 2024 12:38:51 GMT
• Title: Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
• Authors: Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You,
• Abstract summary: Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart. Existing methods often fall short of accurately replicating the original graph for certain datasets. In this paper, we make the first attempt toward textitlossless graph condensation by bridging the previously neglected supervision signals.
• Score: 26.303436980548174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost for training GNNs. Nevertheless, existing methods often fall short of accurately replicating the original graph for certain datasets, thereby failing to achieve the objective of lossless condensation. To understand this phenomenon, we investigate the potential reasons and reveal that the previous state-of-the-art trajectory matching method provides biased and restricted supervision signals from the original graph when optimizing the condensed one. This significantly limits both the scale and efficacy of the condensed graph. In this paper, we make the first attempt toward \textit{lossless graph condensation} by bridging the previously neglected supervision signals. Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching. Moreover, we design a loss function to further extract knowledge from the expert trajectories. Theoretical analysis justifies the design of our method and extensive experiments verify its superiority across different datasets. Code is released at https://github.com/NUS-HPC-AI-Lab/GEOM.

Related papers

• EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction [0.0]
  We consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.
  arXiv  Detail & Related papers  (2024-07-18T22:29:24Z)
• Simple Graph Condensation [30.85754566420301]
  Graph condensation involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on a large-scale original graph. We introduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer. SimGC achieves a significant speedup of up to 10 times compared to existing graph condensation methods.
  arXiv  Detail & Related papers  (2024-03-22T05:04:48Z)
• Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching [50.30124426442228]
  Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. We propose a novel graph method named textbfCraftextbfTing textbfRationatextbf (textbfCTRL) which offers an optimized starting point closer to the original dataset's feature distribution.
  arXiv  Detail & Related papers  (2024-02-07T14:49:10Z)
• Graph Condensation for Inductive Node Representation Learning [59.76374128436873]
  We propose mapping-aware graph condensation (MCond) MCond integrates new nodes into the synthetic graph for inductive representation learning. On the Reddit dataset, MCond achieves up to 121.5x inference speedup and 55.9x reduction in storage requirements.
  arXiv  Detail & Related papers  (2023-07-29T12:11:14Z)
• Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data [91.27527985415007]
  Existing graph condensation methods rely on the joint optimization of nodes and structures in the condensed graph. We advocate a new Structure-Free Graph Condensation paradigm, named SFGC, to distill a large-scale graph into a small-scale graph node set.
  arXiv  Detail & Related papers  (2023-06-05T07:53:52Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• Scaling R-GCN Training with Graph Summarization [71.06855946732296]
  Training of Relation Graph Convolutional Networks (R-GCN) does not scale well with the size of the graph. In this work, we experiment with the use of graph summarization techniques to compress the graph. We obtain reasonable results on the AIFB, MUTAG and AM datasets.
  arXiv  Detail & Related papers  (2022-03-05T00:28:43Z)
• Graph Condensation for Graph Neural Networks [34.4899280207043]
  We study the problem of graph condensation for graph neural networks (GNNs) We aim to condense the large, original graph into a small, synthetic and highly-informative graph. We are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer.
  arXiv  Detail & Related papers  (2021-10-14T17:42:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.