  In this paper, we tackle a new problem of \textit{multi-source unsupervised
domain adaptation (MSUDA) for graphs}, where models trained on annotated source
domains need to be transferred to the unsupervised target graph for node
classification. Due to the discrepancy in distribution across domains, the key
challenge is how to select good source instances and how to adapt the model.
Diverse graph structures further complicate this problem, rendering previous
MSUDA approaches less effective. In this work, we present the framework
Selective Multi-source Adaptation for Graph ({\method}), with a
graph-modeling-based domain selector, a sub-graph node selector, and a bi-level
alignment objective for the adaptation. Concretely, to facilitate the
identification of informative source data, the similarity across graphs is
disentangled and measured with the transferability of a graph-modeling task
set, and we use it as evidence for source domain selection. A node selector is
further incorporated to capture the variation in transferability of nodes
within the same source domain. To learn invariant features for adaptation, we
align the target domain to selected source data both at the embedding space by
minimizing the optimal transport distance and at the classification level by
distilling the label function. Modules are explicitly learned to select
informative source data and conduct the alignment in virtual training splits
with a meta-learning strategy. Experimental results on five graph datasets show
the effectiveness of the proposed method.
