Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks

• URL: http://arxiv.org/abs/2403.17040v1
• Date: Mon, 25 Mar 2024 12:15:10 GMT
• Title: Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
• Authors: Huifeng Yin, Mingkun Xu, Jing Pei, Lei Deng,
• Abstract summary: Spiking neural networks (SNNs) have emerged as a promising alternative to traditional neural networks for graph learning tasks. We propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning.
• Score: 5.627287101959473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.

Related papers

• Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies [15.037300421748107]
  spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of neurons. This work examines the unique properties and benefits of spiking dynamics in enhancing graph representation learning. We propose a spike-based graph neural network model that incorporates spiking dynamics, enhanced by a novel spatial-temporal feature normalization (STFN) technique.
  arXiv  Detail & Related papers  (2024-07-30T02:53:26Z)
• 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)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• Knowledge Enhanced Graph Neural Networks for Graph Completion [0.0]
  Knowledge Enhanced Graph Neural Networks (KeGNN) is a neuro-symbolic framework for graph completion. KeGNN consists of a graph neural network as a base upon which knowledge enhancement layers are stacked. We instantiate KeGNN in conjunction with two state-of-the-art graph neural networks, Graph Convolutional Networks and Graph Attention Networks.
  arXiv  Detail & Related papers  (2023-03-27T07:53:43Z)
• Automatic Relation-aware Graph Network Proliferation [182.30735195376792]
  We propose Automatic Relation-aware Graph Network Proliferation (ARGNP) for efficiently searching GNNs. These operations can extract hierarchical node/relational information and provide anisotropic guidance for message passing on a graph. Experiments on six datasets for four graph learning tasks demonstrate that GNNs produced by our method are superior to the current state-of-the-art hand-crafted and search-based GNNs.
  arXiv  Detail & Related papers  (2022-05-31T10:38:04Z)
• Spiking Graph Convolutional Networks [19.36064180392385]
  SpikingGCN is an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. We show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis.
  arXiv  Detail & Related papers  (2022-05-05T16:44:36Z)
• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning [9.88508686848173]
  Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning.
  arXiv  Detail & Related papers  (2021-06-30T11:20:16Z)
• Analyzing the Performance of Graph Neural Networks with Pipe Parallelism [2.269587850533721]
  We focus on Graph Neural Networks (GNNs) that have found great success in tasks such as node or edge classification and link prediction. New approaches for processing larger networks are needed to advance graph techniques. We study how GNNs could be parallelized using existing tools and frameworks that are known to be successful in the deep learning community.
  arXiv  Detail & Related papers  (2020-12-20T04:20:38Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

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.