Title: Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back,
Abstract: A graph is a powerful concept for representation of relations between pairs
of entities. Data with underlying graph structure can be found across many
disciplines and there is a natural desire for understanding such data better.
Deep learning (DL) has achieved significant breakthroughs in a variety of
machine learning tasks in recent years, especially where data is structured on
a grid, such as in text, speech, or image understanding. However, surprisingly
little has been done to explore the applicability of DL on arbitrary
graph-structured data directly.
The goal of this thesis is to investigate architectures for DL on graphs and
study how to transfer, adapt or generalize concepts that work well on
sequential and image data to this domain. We concentrate on two important
primitives: embedding graphs or their nodes into a continuous vector space
representation (encoding) and, conversely, generating graphs from such vectors
back (decoding). To that end, we make the following contributions.
First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like
operation on graphs performed in the spatial domain where filters are
dynamically generated based on edge attributes. The method is used to encode
graphs with arbitrary and varying structure.
Second, we propose SuperPoint Graph, an intermediate point cloud
representation with rich edge attributes encoding the contextual relationship
between object parts. Based on this representation, ECC is employed to segment
large-scale point clouds without major sacrifice in fine details.
Third, we present GraphVAE, a graph generator allowing us to decode graphs
with variable but upper-bounded number of nodes making use of approximate graph
matching for aligning the predictions of an autoencoder with its inputs. The
method is applied to the task of molecule generation.