Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

• URL: http://arxiv.org/abs/2412.00241v2
• Date: Tue, 10 Dec 2024 08:27:38 GMT
• Title: Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
• Authors: H. Çağrı Bilgi, Lydia Y. Chen, Kubilay Atasu,
• Abstract summary: MEGA-GNN is a unified framework for message passing on multigraphs. We show that MEGA-GNN is not only permutation equivariant but also universal given a strict total ordering on the edges. Experiments show that MEGA-GNN significantly outperforms state-of-the-art solutions by up to 13% on Anti-Money Laundering datasets.
• Score: 5.193718340934995
• License:
• Abstract: Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their application to multigraphs, where parallel edges exist between the same pair of nodes, remains under-explored. Standard GNNs, designed for simple graphs, compute node representations by combining all connected edges at once, without distinguishing between edges from different neighbors. There are some GNN architectures proposed specifically for multigraphs, yet these architectures perform only node-level aggregation in their message passing layers, which limits their expressive power. Furthermore, these approaches either lack permutation equivariance when a strict total edge ordering is absent, or fail to preserve the topological structure of the multigraph. To address all these shortcomings, we propose MEGA-GNN, a unified framework for message passing on multigraphs that can effectively perform diverse graph learning tasks. Our approach introduces a two-stage aggregation process in the message passing layers: first, parallel edges are aggregated, followed by a node-level aggregation of messages from distinct neighbors. We show that MEGA-GNN is not only permutation equivariant but also universal given a strict total ordering on the edges. Experiments show that MEGA-GNN significantly outperforms state-of-the-art solutions by up to 13\% on Anti-Money Laundering datasets and is on par with their accuracy on real-world phishing classification datasets in terms of minority class F1 score.

Related papers

• Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs [0.0]
  One of the most important problems in graph clustering is to find densest overlapping subgraphs (DOS) In this paper, we propose a solution to the DOS problem via Agglomerativedyion (DOSAGE) algorithm as a novel approach to enhance the process of generating the densest overlapping subgraphs. Experiments on standard benchmarks show that the DOSAGE algorithm significantly outperforms the HGNNs and six other methods on the node classification task.
  arXiv  Detail & Related papers  (2024-09-16T14:56:10Z)
• 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)
• 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)
• Refined Edge Usage of Graph Neural Networks for Edge Prediction [51.06557652109059]
  We propose a novel edge prediction paradigm named Edge-aware Message PassIng neuRal nEtworks (EMPIRE) We first introduce an edge splitting technique to specify use of each edge where each edge is solely used as either the topology or the supervision. In order to emphasize the differences between pairs connected by supervision edges and pairs unconnected, we further weight the messages to highlight the relative ones that can reflect the differences.
  arXiv  Detail & Related papers  (2022-12-25T23:19:56Z)
• ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting [32.69196871253339]
  We propose a novel Edge Splitting GNN (ES-GNN) framework to adaptively distinguish between graph edges either relevant or irrelevant to learning tasks. We show that our ES-GNN can be regarded as a solution to a disentangled graph denoising problem.
  arXiv  Detail & Related papers  (2022-05-27T01:29:03Z)
• 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)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization [70.8567058758375]
  VQ-GNN is a universal framework to scale up any convolution-based GNNs using Vector Quantization (VQ) without compromising the performance. Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
  arXiv  Detail & Related papers  (2021-10-27T11:48:50Z)
• Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing [88.30867628592112]
  Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. We show that existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. In order to preserve node proximities, we augment the existing GNNs with node representations.
  arXiv  Detail & Related papers  (2020-09-05T16:46:56Z)

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.