Cooperative Graph Neural Networks

• URL: http://arxiv.org/abs/2310.01267v2
• Date: Sun, 9 Jun 2024 01:42:46 GMT
• Title: Cooperative Graph Neural Networks
• Authors: Ben Finkelshtein, Xingyue Huang, Michael Bronstein, İsmail İlkan Ceylan,
• Abstract summary: A class of graph neural networks follow a standard message-passing paradigm. We propose a novel framework for training graph neural networks. Our approach offers a more flexible and dynamic message-passing paradigm.
• Score: 7.2459816681395095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standard message-passing paradigm: at every layer, each node state is updated based on an aggregate of messages from its neighborhood. In this work, we propose a novel framework for training graph neural networks, where every node is viewed as a player that can choose to either 'listen', 'broadcast', 'listen and broadcast', or to 'isolate'. The standard message propagation scheme can then be viewed as a special case of this framework where every node 'listens and broadcasts' to all neighbors. Our approach offers a more flexible and dynamic message-passing paradigm, where each node can determine its own strategy based on their state, effectively exploring the graph topology while learning. We provide a theoretical analysis of the new message-passing scheme which is further supported by an extensive empirical analysis on a synthetic dataset and on real-world datasets.

Related papers

• Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning [4.085624738017079]
  We introduce the concept of textitmessage detouring to hierarchically characterize cycle representation throughout the entire graph. Message detouring can significantly outperform current counterpart approaches on various benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-12T22:06:37Z)
• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Shortest Path Networks for Graph Property Prediction [13.986963122264632]
  Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood. We propose shortest path message passing neural networks, where the node representations of a graph are propagated to each node in the shortest path neighborhoods. Our framework generalizes message passing neural networks, resulting in provably more expressive models.
  arXiv  Detail & Related papers  (2022-06-02T12:04:29Z)
• 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)
• Node-wise Localization of Graph Neural Networks [52.04194209002702]
  Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. We propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. We conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.
  arXiv  Detail & Related papers  (2021-10-27T10:02:03Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Natural Graph Networks [80.77570956520482]
  We show that the more general concept of naturality is sufficient for a graph network to be well-defined. We define global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks.
  arXiv  Detail & Related papers  (2020-07-16T14:19:06Z)
• Convolutional Kernel Networks for Graph-Structured Data [37.13712126432493]
  We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps. Our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks.
  arXiv  Detail & Related papers  (2020-03-11T09:44:03Z)
• Self-Supervised Graph Representation Learning via Global Context Prediction [31.07584920486755]
  This paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. We randomly select pairs of nodes in a graph and train a well-designed neural net to predict the contextual position of one node relative to the other. Our underlying hypothesis is that the representations learned from such within-graph context would capture the global topology of the graph and finely characterize the similarity and differentiation between nodes.
  arXiv  Detail & Related papers  (2020-03-03T15:46:01Z)

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.