  Bipartite b-matching is fundamental in algorithm design, and has been widely
applied into economic markets, labor markets, etc. These practical problems
usually exhibit two distinct features: large-scale and dynamic, which requires
the matching algorithm to be repeatedly executed at regular intervals. However,
existing exact and approximate algorithms usually fail in such settings due to
either requiring intolerable running time or too much computation resource. To
address this issue, we propose \texttt{NeuSearcher} which leverages the
knowledge learned from previously instances to solve new problem instances.
Specifically, we design a multichannel graph neural network to predict the
threshold of the matched edges weights, by which the search region could be
significantly reduced. We further propose a parallel heuristic search algorithm
to iteratively improve the solution quality until convergence. Experiments on
both open and industrial datasets demonstrate that \texttt{NeuSearcher} can
speed up 2 to 3 times while achieving exactly the same matching solution
compared with the state-of-the-art approximation approaches.
