Geodesic Graph Neural Network for Efficient Graph Representation
Learning
- URL: http://arxiv.org/abs/2210.02636v1
- Date: Thu, 6 Oct 2022 02:02:35 GMT
- Title: Geodesic Graph Neural Network for Efficient Graph Representation
Learning
- Authors: Lecheng Kong, Yixin Chen, Muhan Zhang
- Abstract summary: We propose an efficient GNN framework called Geodesic GNN (GDGNN)
It injects conditional relationships between nodes into the model without labeling.
Conditioned on the geodesic representations, GDGNN is able to generate node, link, and graph representations that carry much richer structural information than plain GNNs.
- Score: 34.047527874184134
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, Graph Neural Networks (GNNs) have been applied to graph learning
tasks and achieved state-of-the-art results. However, many competitive methods
employ preprocessing on the target nodes, such as subgraph extraction and
customized labeling, to capture some information that is hard to be learned by
normal GNNs. Such operations are time-consuming and do not scale to large
graphs. In this paper, we propose an efficient GNN framework called Geodesic
GNN (GDGNN). It injects conditional relationships between nodes into the model
without labeling. Specifically, we view the shortest paths between two nodes as
the spatial graph context of the neighborhood around them. The GNN embeddings
of nodes on the shortest paths are used to generate geodesic representations.
Conditioned on the geodesic representations, GDGNN is able to generate node,
link, and graph representations that carry much richer structural information
than plain GNNs. We theoretically prove that GDGNN is more powerful than plain
GNNs, and present experimental results to show that GDGNN achieves highly
competitive performance with state-of-the-art GNN models on link prediction and
graph classification tasks while taking significantly less time.
Related papers
- Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks [13.655670509818144]
We propose a novel Graph structure Prompt Learning method (GPL) to enhance the training of Graph networks (GNNs)
GPL employs task-independent graph structure losses to encourage GNNs to learn intrinsic graph characteristics while simultaneously solving downstream tasks.
In experiments on eleven real-world datasets, after being trained by neural prediction, GNNs significantly outperform their original performance on node classification, graph classification, and edge tasks.
arXiv Detail & Related papers (2024-07-16T03:59:18Z) - Training Graph Neural Networks on Growing Stochastic Graphs [114.75710379125412]
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data.
We propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
arXiv Detail & Related papers (2022-10-27T16:00:45Z) - Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link
Prediction [23.545059901853815]
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graphstructured data.
We propose Neighborhood Overlap-aware Graph Neural Networks (Neo-GNNs) that learn useful structural features from an adjacency overlapped neighborhoods for link prediction.
arXiv Detail & Related papers (2022-06-09T01:43:49Z) - Dual GNNs: Graph Neural Network Learning with Limited Supervision [33.770877823910176]
We propose a novel Dual GNN learning framework to address this challenge task.
By integrating the two modules in a dual GNN learning framework, we perform joint learning in an end-to-end fashion.
arXiv Detail & Related papers (2021-06-29T23:52:25Z) - Identity-aware Graph Neural Networks [63.6952975763946]
We develop a class of message passing Graph Neural Networks (ID-GNNs) with greater expressive power than the 1-WL test.
ID-GNN extends existing GNN architectures by inductively considering nodes' identities during message passing.
We show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks.
arXiv Detail & Related papers (2021-01-25T18:59:01Z) - Distance Encoding: Design Provably More Powerful Neural Networks for
Graph Representation Learning [63.97983530843762]
Graph Neural Networks (GNNs) have achieved great success in graph representation learning.
GNNs generate identical representations for graph substructures that may in fact be very different.
More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure.
We propose Distance Depiction (DE) as a new class of graph representation learning.
arXiv Detail & Related papers (2020-08-31T23:15:40Z) - GPT-GNN: Generative Pre-Training of Graph Neural Networks [93.35945182085948]
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.
We present the GPT-GNN framework to initialize GNNs by generative pre-training.
We show that GPT-GNN significantly outperforms state-of-the-art GNN models without pre-training by up to 9.1% across various downstream tasks.
arXiv Detail & Related papers (2020-06-27T20:12:33Z) - Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs [95.63153473559865]
Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
Most existing GNN models in practice are shallow and essentially feature-centric.
We show empirically and analytically that the existing shallow GNNs cannot preserve graph structures well.
We propose Eigen-GNN, a plug-in module to boost GNNs ability in preserving graph structures.
arXiv Detail & Related papers (2020-06-08T02:47:38Z) - XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information.
GNNs are mostly treated as black-boxes and lack human intelligible explanations.
We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
arXiv Detail & Related papers (2020-06-03T23:52:43Z)
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.