  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.
