Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference

• URL: http://arxiv.org/abs/2411.14035v1
• Date: Thu, 21 Nov 2024 11:39:09 GMT
• Title: Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
• Authors: Yunhui Liu, Xinyi Gao, Tieke He, Jianhua Zhao, Hongzhi Yin,
• Abstract summary: We introduce HG2M and HG2M+ to combine both HGNN's superior performance and relational's efficient inference. HG2M directly trains students with node features as input and soft labels from teacher HGNNs as targets. HG2Ms demonstrate a 379.24$times$ speedup in inference over HGNNs on the large-scale IGB-3M-19 dataset.
• Score: 53.38082028252104
• License:
• Abstract: Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semantics inherent in heterogeneous graph structures. However, the neighborhood-fetching latency incurred by structure dependency in HGNNs makes it challenging to deploy for latency-constrained applications that require fast inference. Inspired by recent GNN-to-MLP knowledge distillation frameworks, we introduce HG2M and HG2M+ to combine both HGNN's superior performance and MLP's efficient inference. HG2M directly trains student MLPs with node features as input and soft labels from teacher HGNNs as targets, and HG2M+ further distills reliable and heterogeneous semantic knowledge into student MLPs through reliable node distillation and reliable meta-path distillation. Experiments conducted on six heterogeneous graph datasets show that despite lacking structural dependencies, HG2Ms can still achieve competitive or even better performance than HGNNs and significantly outperform vanilla MLPs. Moreover, HG2Ms demonstrate a 379.24$\times$ speedup in inference over HGNNs on the large-scale IGB-3M-19 dataset, showcasing their ability for latency-sensitive deployments.

Related papers

• Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation [56.912354708167534]
  Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs) GNNto-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student. This paper proposes a simple yet effective Hardness-aware GNN-to-MLP Distillation (HGMD) framework.
  arXiv  Detail & Related papers  (2024-07-20T06:13:00Z)
• A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation [58.813991312803246]
  We propose a Teacher-Free Graph Self-Distillation (TGS) framework that does not require any teacher model or GNNs during both training and inference. TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.
  arXiv  Detail & Related papers  (2024-03-06T05:52:13Z)
• LightHGNN: Distilling Hypergraph Neural Networks into MLPs for $100\times$ Faster Inference [19.383356275847444]
  Hypergraph Networks (HGNNs) have recently attracted much attention and exhibited satisfactory performance due to their superiority in high-order correlation modeling. In this paper, we propose to bridge the gap between the HGNNs and inference-efficient Multi-Layer Perceptron (MLPs) to eliminate the hypergraph dependency of HGNNs.
  arXiv  Detail & Related papers  (2024-02-06T14:40:26Z)
• Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework [36.160251860788314]
  We propose an efficient Full-Frequency GNN-to-MLP (FFG2M) distillation framework. We factorize the knowledge learned by GNNs into low- and high-frequency components in the spectral domain. We identify a potential information drowning problem for existing GNN-to-MLP distillation.
  arXiv  Detail & Related papers  (2023-05-18T06:57:06Z)
• Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs [71.93227401463199]
  This paper pinpoints the major source of GNNs' performance gain to their intrinsic capability, by introducing an intermediate model class dubbed as P(ropagational)MLP. We observe that PMLPs consistently perform on par with (or even exceed) their GNN counterparts, while being much more efficient in training.
  arXiv  Detail & Related papers  (2022-12-18T08:17:32Z)
• Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification [42.840122801915996]
  We propose a Graph Self-Distillation on Neighborhood (GSDN) framework to reduce the gap between GNNs and Neurals. GSDN infers 75XX faster than existing GNNs and 16X-25X faster than other inference acceleration methods.
  arXiv  Detail & Related papers  (2022-10-05T08:35:34Z)
• NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs [41.85649409565574]
  Graph Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data. Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features. In this paper, we propose to learn NOise-robust Structure-awares On Graphs (NOSMOG) to overcome the challenges.
  arXiv  Detail & Related papers  (2022-08-22T01:47:07Z)
• Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily [58.76759997223951]
  We propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs. We also propose a Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets.
  arXiv  Detail & Related papers  (2022-03-19T14:26:43Z)
• On Graph Neural Networks versus Graph-Augmented MLPs [51.23890789522705]
  Graph-Augmented Multi-Layer Perceptrons (GA-MLPs) first augments node features with certain multi-hop operators on the graph. We prove a separation in expressive power between GA-MLPs and GNNs that grows exponentially in depth.
  arXiv  Detail & Related papers  (2020-10-28T17:59:59Z)

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.