Dynamic Spiking Graph Neural Networks
(2401.05373)Abstract
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexity and large memory overheads. Current work often uses SNNs instead of Recurrent Neural Networks (RNNs) by using binary features instead of continuous ones for efficient training, which would overlooks graph structure information and leads to the loss of details during propagation. Additionally, optimizing dynamic spiking models typically requires propagation of information across time steps, which increases memory requirements. To address these challenges, we present a framework named \underline{Dy}namic \underline{S}p\underline{i}king \underline{G}raph \underline{N}eural Networks (\method{}). To mitigate the information loss problem, \method{} propagates early-layer information directly to the last layer for information compensation. To accommodate the memory requirements, we apply the implicit differentiation on the equilibrium state, which does not rely on the exact reverse of the forward computation. While traditional implicit differentiation methods are usually used for static situations, \method{} extends it to the dynamic graph setting. Extensive experiments on three large-scale real-world dynamic graph datasets validate the effectiveness of \method{} on dynamic node classification tasks with lower computational costs.
Overview
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Dy-SIGN combines SNNs and GNNs for dynamic graph representation, addressing issues of computational efficiency and structural detail preservation.
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It introduces an information compensation mechanism to mitigate loss of graph structural detail and employs implicit differentiation for effective memory management.
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Extensive experiments on large-scale real-world datasets demonstrate Dy-SIGN's superiority in performance and efficiency for dynamic node classification tasks.
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The paper emphasizes the framework's potential for future research in AI, focusing on the efficient manipulation of dynamic graphs.
Dynamic Spiking Graph Neural Networks (Dy-SIGN): A Framework for Efficient Node Classification
Introduction
The fusion of Spiking Neural Networks (SNNs) with Graph Neural Networks (GNNs) heralds a promising direction for handling dynamic graphs, which naturally represent evolving data structures such as social networks or citation networks. While the potential of such an amalgamation is immense, particularly in terms of computational efficiency and applicability to non-Euclidean data, significant challenges have hindered its realization. Chief among these are the issues of dynamic graph representation learning, marked by high complexity and substantial memory demands, compounded by the inherent limitation of SNNs in preserving graph structure and detail during information propagation.
Dynamic Spiking Graph Neural Network (Dy-SIGN)
The Dynamic Spiking Graph Neural Network (Dy-SIGN) presents a novel framework addressing these core challenges. It leverages an information compensation mechanism to mitigate loss of graph structural detail in SNNs and employs implicit differentiation on the equilibrium state to manage memory requirements effectively. This technique ensures efficient training without sacrificing performance or detail, verified through extensive experiments on large-scale real-world dynamic graph datasets focusing on dynamic node classification tasks.
Addressing Information Loss and Memory Consumption
Dy-SIGN introduces a two-pronged approach to address the challenges of applying SNNs to dynamic graphs:
- Information Compensation Mechanism: This mechanism aims to counteract the loss of structural and neighboring node information inherent to SNNs. By establishing a direct information channel between early and final layers of the network, Dy-SIGN integrates original graph details into the feature representations, thus enhancing the quality and accuracy of node classifications.
- Implicit Differentiation for Dynamic Spiking Graphs: Traditional approaches to optimizing dynamic spiking models demand significant memory resources for information propagation across time steps. Dy-SIGN innovates by applying implicit differentiation to dynamic graph scenarios, reducing memory consumption without relying on forward computation reversal.
Experimental Validation
Comparative testing on three large-scale real-world dynamic graph datasets underscores Dy-SIGN's effectiveness in dynamic node classification tasks. The framework showcases lower computational costs and improved performance across various settings when compared to state-of-the-art methods.
Contributions
The contributions of Dy-SIGN are twofold:
- Methodological Advancement: Dy-SIGN represents the inaugural attempt to integrate implicit differentiation within the dynamic graph context, offering a novel perspective on handling information loss and memory consumption in the application of SNNs to dynamic graphs.
- Superior Performance: Through rigorous experiments, Dy-SIGN demonstrates its superiority over contemporary methods, validating its effectiveness and efficiency in dynamic node classification tasks.
Future Directions
The advent of Dy-SIGN opens several avenues for future research in AI and neural network modeling, specifically pertaining to the efficient manipulation of dynamic graphs. The exploration into further optimizations and applications of the Dy-SIGN framework promises to enrich the toolbox available to researchers and practitioners dealing with evolving data in complex networks. This investigation into dynamic spiking graph neural networks holds the potential to significantly impact the development and deployment of computationally efficient and memory-conscious AI solutions across a wide array of sectors and applications.
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