arxivst stuff from arxiv that you should probably bookmark

Shift Aggregate Extract Networks

Abstract · Mar 16, 2017 09:52 ·

cs-lg stat-ml

Arxiv Abstract

  • Francesco Orsini
  • Daniele Baracchi
  • Paolo Frasconi

We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested “part-of-part” relations. Unlike recursive neural networks, which unroll a template on input graphs directly, we unroll a neural network template over the decomposition hierarchy, allowing us to deal with the high degree variability that typically characterize social network graphs. Deep hierarchical decompositions are also amenable to domain compression, a technique that reduces both space and time complexity by exploiting symmetries. We show empirically that our approach is competitive with current state-of-the-art graph classification methods, particularly when dealing with social network datasets.

Read the paper (pdf) »