NodeMixup: Tackling Under-Reaching for Graph Neural Networks
- URL: http://arxiv.org/abs/2312.13032v2
- Date: Thu, 21 Dec 2023 03:02:35 GMT
- Title: NodeMixup: Tackling Under-Reaching for Graph Neural Networks
- Authors: Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, Long Jin
- Abstract summary: Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem.
Due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the emphunder-reaching issue.
To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup.
- Score: 27.393295683072406
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have become mainstream methods for solving the
semi-supervised node classification problem. However, due to the uneven
location distribution of labeled nodes in the graph, labeled nodes are only
accessible to a small portion of unlabeled nodes, leading to the
\emph{under-reaching} issue. In this study, we firstly reveal under-reaching by
conducting an empirical investigation on various well-known graphs. Then, we
demonstrate that under-reaching results in unsatisfactory distribution
alignment between labeled and unlabeled nodes through systematic experimental
analysis, significantly degrading GNNs' performance. To tackle under-reaching
for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The
fundamental idea is to (1) increase the reachability of labeled nodes by
labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the
neighbor connections of intra-class node pairs to improve performance gains of
mixup, and (3) use neighbor label distribution similarity incorporating node
degrees to determine sampling weights for node mixup. Extensive experiments
demonstrate the efficacy of NodeMixup in assisting GNNs in handling
under-reaching. The source code is available at
\url{https://github.com/WeigangLu/NodeMixup}.
Related papers
- NodeFormer: A Scalable Graph Structure Learning Transformer for Node
Classification [70.51126383984555]
We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes.
The efficient computation is enabled by a kernerlized Gumbel-Softmax operator.
Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
arXiv Detail & Related papers (2023-06-14T09:21:15Z) - GraFN: Semi-Supervised Node Classification on Graph with Few Labels via
Non-Parametric Distribution Assignment [5.879936787990759]
We propose a novel semi-supervised method for graphs, GraFN, to ensure nodes that belong to the same class to be grouped together.
GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph.
We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs.
arXiv Detail & Related papers (2022-04-04T08:22:30Z) - 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) - Graph Neural Networks with Feature and Structure Aware Random Walk [7.143879014059894]
We show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models.
We develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes.
arXiv Detail & Related papers (2021-11-19T08:54:21Z) - Graph Pointer Neural Networks [11.656981519694218]
We present Graph Pointer Neural Networks (GPNN) to tackle the challenges mentioned above.
We leverage a pointer network to select the most relevant nodes from a large amount of multi-hop neighborhoods.
The GPNN significantly improves the classification performance of state-of-the-art methods.
arXiv Detail & Related papers (2021-10-03T10:18:25Z) - 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) - Label-Consistency based Graph Neural Networks for Semi-supervised Node
Classification [47.753422069515366]
Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification.
In this paper, we propose label-consistency based graph neural network(LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs.
Experiments on benchmark datasets demonstrate the proposed LC-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.
arXiv Detail & Related papers (2020-07-27T11:17:46Z) - Towards Deeper Graph Neural Networks with Differentiable Group
Normalization [61.20639338417576]
Graph neural networks (GNNs) learn the representation of a node by aggregating its neighbors.
Over-smoothing is one of the key issues which limit the performance of GNNs as the number of layers increases.
We introduce two over-smoothing metrics and a novel technique, i.e., differentiable group normalization (DGN)
arXiv Detail & Related papers (2020-06-12T07:18:02Z) - Bilinear Graph Neural Network with Neighbor Interactions [106.80781016591577]
Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data.
We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes.
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
arXiv Detail & Related papers (2020-02-10T06:43:38Z) - Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification.
For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes.
Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
arXiv Detail & Related papers (2020-01-17T02:52:30Z)
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.