Understanding and Tackling Over-Dilution in Graph Neural Networks

• URL: http://arxiv.org/abs/2508.16829v1
• Date: Fri, 22 Aug 2025 22:55:23 GMT
• Title: Understanding and Tackling Over-Dilution in Graph Neural Networks
• Authors: Junhyun Lee, Veronika Thost, Bumsoo Kim, Jaewoo Kang, Tengfei Ma,
• Abstract summary: Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs.<n>MPNNs struggle with unintended behaviors, such as over-smoothing and over-squashing, due to irregular data structures.<n>In this paper, we delve into the limitations of MPNNs, focusing on aspects that have previously been overlooked.
• Score: 32.15766560861491
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs, but they struggle with unintended behaviors, such as over-smoothing and over-squashing, due to irregular data structures. The observation and formulation of these limitations have become foundational in constructing more informative graph representations. In this paper, we delve into the limitations of MPNNs, focusing on aspects that have previously been overlooked. Our observations reveal that even within a single layer, the information specific to an individual node can become significantly diluted. To delve into this phenomenon in depth, we present the concept of Over-dilution and formulate it with two dilution factors: intra-node dilution for attribute-level and inter-node dilution for node-level representations. We also introduce a transformer-based solution that alleviates over-dilution and complements existing node embedding methods like MPNNs. Our findings provide new insights and contribute to the development of informative representations. The implementation and supplementary materials are publicly available at https://github.com/LeeJunHyun/NATR.

Related papers

• Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey [0.0]
  Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their effectiveness is often constrained by two critical challenges. Oversquashing, where the excessive compression of information from distant nodes results in significant information loss, and oversmoothing, where repeated message-passing iterations homogenize node representations, obscuring meaningful distinctions. In this survey, we examine graph rewiring techniques, a class of methods designed to address these structural bottlenecks by modifying graph topology to enhance information diffusion.
  arXiv  Detail & Related papers  (2024-11-26T13:38:12Z)
• Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks [0.0]
  We deploy decoupled GNNs to overcome the issue of capturing nonlinear network information. For node representation learning, we develop a GNN architecture with two modules for aggregating node feature information. The findings demonstrate that decoupled training along with the global context enhanced representation of the nodes is superior to the state-of-the-art models in terms of AUC.
  arXiv  Detail & Related papers  (2024-09-04T21:54:07Z)
• Probabilistic Graph Rewiring via Virtual Nodes [21.273828055299408]
  Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning.<n>MPNNs face challenges such as under-reaching and over-squashing, where limited receptive fields and structural bottlenecks hinder information flow in the graph.<n>Here, we propose implicitly rewired message-passing neural networks (IPR-MPNNs), a novel approach that integrates implicit probabilistic graph rewiring into MPNNs.
  arXiv  Detail & Related papers  (2024-05-27T16:11:49Z)
• Degree-based stratification of nodes in Graph Neural Networks [66.17149106033126]
  We modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods.
  arXiv  Detail & Related papers  (2023-12-16T14:09:23Z)
• DINE: Dimensional Interpretability of Node Embeddings [3.3040172566302206]
  Graph representation learning methods, such as node embeddings, are powerful approaches to map nodes into a latent vector space. We develop new metrics that measure the global interpretability of embedding vectors. We then introduce DINE, a novel approach that can retrofit existing node embeddings by making them more interpretable without sacrificing their task performance.
  arXiv  Detail & Related papers  (2023-10-02T12:47:42Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module [65.81781176362848]
  Graph Neural Networks (GNNs) can learn from graph-structured data through neighborhood information aggregation. As the number of layers increases, node representations become indistinguishable, which is known as over-smoothing. We propose a textbfPosterior-Sampling-based, Node-distinguish Residual module (PSNR).
  arXiv  Detail & Related papers  (2023-05-09T12:03:42Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Graph Neural Networks with Learnable Structural and Positional Representations [83.24058411666483]
  A major issue with arbitrary graphs is the absence of canonical positional information of nodes. We introduce Positional nodes (PE) of nodes, and inject it into the input layer, like in Transformers. We observe a performance increase for molecular datasets, from 2.87% up to 64.14% when considering learnable PE for both GNN classes.
  arXiv  Detail & Related papers  (2021-10-15T05:59:15Z)
• Node2Seq: Towards Trainable Convolutions in Graph Neural Networks [59.378148590027735]
  We propose a graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores.
  arXiv  Detail & Related papers  (2021-01-06T03:05:37Z)
• NCGNN: Node-level Capsule Graph Neural Network [45.23653314235767]
  Node-level Capsule Graph Neural Network (NCGNN) represents nodes as groups of capsules. novel dynamic routing procedure is developed to adaptively select appropriate capsules for aggregation. NCGNN can well address the over-smoothing issue and outperforms the state of the arts by producing better node embeddings for classification.
  arXiv  Detail & Related papers  (2020-12-07T06:46:17Z)

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.