  Graph representation learning on vast datasets, like web data, has made
significant strides. However, the associated computational and storage
overheads raise concerns. In sight of this, Graph condensation (GCond) has been
introduced to distill these large real datasets into a more concise yet
information-rich synthetic graph. Despite acceleration efforts, existing GCond
methods mainly grapple with efficiency, especially on expansive web data
graphs. Hence, in this work, we pinpoint two major inefficiencies of current
paradigms: (1) the concurrent updating of a vast parameter set, and (2)
pronounced parameter redundancy. To counteract these two limitations
correspondingly, we first (1) employ the Mean-Field variational approximation
for convergence acceleration, and then (2) propose the objective of Gradient
Information Bottleneck (GDIB) to prune redundancy. By incorporating the leading
explanation techniques (e.g., GNNExplainer and GSAT) to instantiate the GDIB,
our EXGC, the Efficient and eXplainable Graph Condensation method is proposed,
which can markedly boost efficiency and inject explainability. Our extensive
evaluations across eight datasets underscore EXGC's superiority and relevance.
Code is available at https://github.com/MangoKiller/EXGC.
