Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings

• URL: http://arxiv.org/abs/2312.05905v2
• Date: Thu, 2 May 2024 12:18:43 GMT
• Title: Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
• Authors: Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez,
• Abstract summary: We present a novel edge-level ego-network encoding for learning on graphs. It can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features. We show theoretically that such encoding is more expressive than node-based sub-graph MP-GNNs.
• Score: 3.8711489380602804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats. The proposed encoding is sufficient to distinguish Strongly Regular Graphs, a family of challenging 3-WL equivalent graphs. We show theoretically that such encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical evaluation on four benchmarks with 10 graph datasets, our results match or improve previous baselines on expressivity, graph classification, graph regression, and proximity tasks -- while reducing memory usage by 18.1x in certain real-world settings.

Related papers

• A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening [18.688057947275112]
  Subgraph Graph Neural Networks (Subgraph GNNs) enhance the expressivity of message-passing GNNs by representing graphs as sets of subgraphs. Previous approaches suggested processing only subsets of subgraphs, selected either randomly or via learnable sampling. This paper introduces a new Subgraph GNNs framework to address these issues.
  arXiv  Detail & Related papers  (2024-06-13T16:29:06Z)
• Spectral Greedy Coresets for Graph Neural Networks [61.24300262316091]
  The ubiquity of large-scale graphs in node-classification tasks hinders the real-world applications of Graph Neural Networks (GNNs) This paper studies graph coresets for GNNs and avoids the interdependence issue by selecting ego-graphs based on their spectral embeddings. Our spectral greedy graph coreset (SGGC) scales to graphs with millions of nodes, obviates the need for model pre-training, and applies to low-homophily graphs.
  arXiv  Detail & Related papers  (2024-05-27T17:52:12Z)
• UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification [6.977634174845066]
  A universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix.
  arXiv  Detail & Related papers  (2023-08-03T09:32:50Z)
• 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)
• SoftEdge: Regularizing Graph Classification with Random Soft Edges [18.165965620873745]
  Graph data augmentation plays a vital role in regularizing Graph Neural Networks (GNNs) Simple edge and node manipulations can create graphs with an identical structure or indistinguishable structures to message passing GNNs but of conflict labels. We propose SoftEdge, which assigns random weights to a portion of the edges of a given graph to construct dynamic neighborhoods over the graph.
  arXiv  Detail & Related papers  (2022-04-21T20:12:36Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings [51.82434518719011]
  GNNAutoScale (GAS) is a framework for scaling arbitrary message-passing GNNs to large graphs. Gas prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations. Gas reaches state-of-the-art performance on large-scale graphs.
  arXiv  Detail & Related papers  (2021-06-10T09:26:56Z)
• 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)
• GraphCrop: Subgraph Cropping for Graph Classification [36.33477716380905]
  We develop the textbfGraphCrop (Subgraph Cropping) data augmentation method to simulate the real-world noise of sub-structure omission. By preserving the valid structure contexts for graph classification, we encourage GNNs to understand the content of graph structures in a global sense.
  arXiv  Detail & Related papers  (2020-09-22T14:05:41Z)
• 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)
• Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College [80.67842220664231]
  We propose a novel pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph. In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-10T14:48:48Z)

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.