用信息瓶颈原理压缩图数据，让通信更高效。
Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach
- 基于图信息瓶颈，提取任务相关的紧凑子图。
- 通信开销降低，关键信息保留率超90%。
- 适合低带宽场景，兼容现有数字通信系统。
图数据在知识表示、社交网络等领域至关重要，但大型网络传输效率低下。本文提出一种方法，通过图神经网络与图信息瓶颈（GIB）原理，提取保持关键信息的任务相关子图，降低通信开销。针对图结构不规则导致的GIB优化困难，我们推导出可计算的变分上界。进一步提出VQ-GIB机制，结合向量量化（VQ），将子图表示转为离散码本序列，适配现有数字通信系统。实验表明，该方法显著降低通信成本，同时在多种信道下保持鲁棒性能，适用于连续与离散通信系统。
原文摘要 · Abstract (English)
Graph data, essential in fields like knowledge representation and social networks, often involves large networks with many nodes and edges. Transmitting these graphs can be highly inefficient due to their size and redundancy for specific tasks. This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission. The challenge lies in the irregular structure of graph data, making GIB optimization complex. We address this by deriving a tractable variational upper bound for the objective function. Additionally, we propose the VQ-GIB mechanism, integrating vector quantization (VQ) to convert subgraph representations into a discrete codebook sequence, compatible with existing digital communication systems. Our experiments show that this GIB-based method significantly lowers communication costs while preserving essential task-related information. The approach demonstrates robust performance across various communication channels, suitable for both continuous and discrete systems.
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