Graph-Skeleton：约1%的节点足以表征十亿级规模图
Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale Graph
AI总结：
针对大规模网络图挖掘的存储与计算挑战，本文提出Graph-Skeleton模型，通过提取压缩背景节点，实现用约1%节点表征十亿级图且性能高度相当，为目标节点分类提供了高效方案。
摘要：
由于图数据在网络中无处不在，网络图挖掘已成为研究热点。然而，实际应用中大规模网络图的普及给存储、计算能力以及图模型设计带来了严峻挑战。尽管已有大量研究致力于提升图模型的可扩展性，但学术研究与实际网络图挖掘应用之间仍存在显著差距。其中一个主要原因是，在大多数工业场景中，网络图里实际仅需分析一小部分节点，我们将这些节点称为目标节点，其余节点则称为背景节点。本文提出，从海量网络图数据中合理提取并压缩背景节点，或许是从根本上解决上述障碍的更经济的捷径。为此，我们首次开展面向目标节点分类的海量背景节点压缩问题研究。通过大量实验，我们揭示了背景节点在目标节点分类中发挥的两个关键作用：增强目标节点之间的结构连通性，以及与目标节点的特征相关性。在此基础上，我们提出了一种新颖的Graph-Skeleton（图骨架）模型，该模型可合理提取背景节点，并进一步在相似的目标-背景局部结构中压缩背景节点的语义与拓扑信息。在多个网络图数据集上的大量实验证明了所提方法的有效性与高效性。尤其在拥有2.4亿个节点的MAG240M数据集上，我们生成的骨架图仅包含原图1.8%的节点，却能达到与原图高度相当的性能。
英文摘要：
Due to the ubiquity of graph data on the web, web graph mining has become a hot research spot. Nonetheless, the prevalence of large-scale web graphs in real applications poses significant challenges to storage, computational capacity and graph model design. Despite numerous studies to enhance the scalability of graph models, a noticeable gap remains between academic research and practical web graph mining applications. One major cause is that in most industrial scenarios, only a small part of nodes in a web graph are actually required to be analyzed, where we term these nodes as target nodes, while others as background nodes. In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally. To this end, we make the first attempt to study the problem of massive background nodes compression for target nodes classification. Through extensive experiments, we reveal two critical roles played by the background nodes in target node classification: enhancing structural connectivity between target nodes, and feature correlation with target nodes. Followingthis, we propose a novel Graph-Skeleton1 model, which properly fetches the background nodes, and further condenses the semantic and topological information of background nodes within similar target-background local structures. Extensive experiments on various web graph datasets demonstrate the effectiveness and efficiency of the proposed method. In particular, for MAG240M dataset with 0.24 billion nodes, our generated skeleton graph achieves highly comparable performance while only containing 1.8% nodes of the original graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
