Abstract
Lots of learning tasks require dealing with graph data which contains rich
relation information among elements. Modeling physics system, learning
molecular fingerprints, predicting protein interface, and classifying diseases
require a model to learn from graph inputs. In other domains such as learning
from non-structural data like texts and images, reasoning on extracted
structures, like the dependency tree of sentences and the scene graph of
images, is an important research topic which also needs graph reasoning models.
Graph neural networks (GNNs) are connectionist models that capture the
dependence of graphs via message passing between the nodes of graphs. Unlike
standard neural networks, graph neural networks retain a state that can
represent information from its neighborhood with arbitrary depth. Although the
primitive GNNs have been found difficult to train for a fixed point, recent
advances in network architectures, optimization techniques, and parallel
computation have enabled successful learning with them. In recent years,
systems based on variants of graph neural networks such as graph convolutional
network (GCN), graph attention network (GAT), gated graph neural network (GGNN)
have demonstrated ground-breaking performance on many tasks mentioned above. In
this survey, we provide a detailed review over existing graph neural network
models, systematically categorize the applications, and propose four open
problems for future research.
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