A Simple and Scalable Graph Neural Network for Large Directed Graphs
- URL: http://arxiv.org/abs/2306.08274v2
- Date: Wed, 6 Dec 2023 06:38:01 GMT
- Title: A Simple and Scalable Graph Neural Network for Large Directed Graphs
- Authors: Seiji Maekawa, Yuya Sasaki, Makoto Onizuka
- Abstract summary: We investigate various combinations of node representations and edge direction awareness within an input graph.
In response, we propose a simple yet holistic classification method A2DUG.
We demonstrate that A2DUG stably performs well on various datasets and improves the accuracy up to 11.29 compared with the state-of-the-art methods.
- Score: 11.792826520370774
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Node classification is one of the hottest tasks in graph analysis. Though
existing studies have explored various node representations in directed and
undirected graphs, they have overlooked the distinctions of their capabilities
to capture the information of graphs. To tackle the limitation, we investigate
various combinations of node representations (aggregated features vs. adjacency
lists) and edge direction awareness within an input graph (directed vs.
undirected). We address the first empirical study to benchmark the performance
of various GNNs that use either combination of node representations and edge
direction awareness. Our experiments demonstrate that no single combination
stably achieves state-of-the-art results across datasets, which indicates that
we need to select appropriate combinations depending on the dataset
characteristics. In response, we propose a simple yet holistic classification
method A2DUG which leverages all combinations of node representations in
directed and undirected graphs. We demonstrate that A2DUG stably performs well
on various datasets and improves the accuracy up to 11.29 compared with the
state-of-the-art methods. To spur the development of new methods, we publicly
release our complete codebase under the MIT license.
Related papers
- GRAN is superior to GraphRNN: node orderings, kernel- and graph
embeddings-based metrics for graph generators [0.6816499294108261]
We study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space.
We compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.
arXiv Detail & Related papers (2023-07-13T12:07:39Z) - Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN.
We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
arXiv Detail & Related papers (2023-05-18T07:27:18Z) - NESS: Node Embeddings from Static SubGraphs [0.0]
We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting.
NESS is based on two key ideas: i) Partitioning the training graph to multiple static, sparse subgraphs with non-overlapping edges using random edge split during data pre-processing.
We demonstrate that NESS gives a better node representation for link prediction tasks compared to current autoencoding methods that use either the whole graph or subgraphs.
arXiv Detail & Related papers (2023-03-15T22:14:28Z) - CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph
Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning.
We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning.
We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z) - SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations.
We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path.
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
arXiv Detail & Related papers (2021-12-12T14:18:18Z) - A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
We propose a robust framework for adversarial graph embedding, named AGE.
AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution.
Based on this framework, we propose three models to handle three types of graph data.
arXiv Detail & Related papers (2021-05-22T07:05:48Z) - Accurate Learning of Graph Representations with Graph Multiset Pooling [45.72542969364438]
We propose a Graph Multiset Transformer (GMT) that captures the interaction between nodes according to their structural dependencies.
Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks.
arXiv Detail & Related papers (2021-02-23T07:45:58Z) - Scalable Graph Neural Networks for Heterogeneous Graphs [12.44278942365518]
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data.
Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks.
In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities.
arXiv Detail & Related papers (2020-11-19T06:03:35Z) - 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) - Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects.
To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks.
Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
arXiv Detail & Related papers (2020-07-08T19:48:19Z)
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.