  Vector quantization(VQ) is a lossy data compression technique from signal
processing for which simple competitive learning is one standard method to
quantize patterns from the input space. Extending competitive learning VQ to
the domain of graphs results in competitive learning for quantizing input
graphs. In this contribution, we propose an accelerated version of competitive
learning graph quantization (GQ) without trading computational time against
solution quality. For this, we lift graphs locally to vectors in order to avoid
unnecessary calculations of intractable graph distances. In doing so, the
accelerated version of competitive learning GQ gradually turns locally into a
competitive learning VQ with increasing number of iterations. Empirical results
show a significant speedup by maintaining a comparable solution quality.
