107,493 research outputs found
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Interpretable Structure-Evolving LSTM
This paper develops a general framework for learning interpretable data
representation via Long Short-Term Memory (LSTM) recurrent neural networks over
hierarchal graph structures. Instead of learning LSTM models over the pre-fixed
structures, we propose to further learn the intermediate interpretable
multi-level graph structures in a progressive and stochastic way from data
during the LSTM network optimization. We thus call this model the
structure-evolving LSTM. In particular, starting with an initial element-level
graph representation where each node is a small data element, the
structure-evolving LSTM gradually evolves the multi-level graph representations
by stochastically merging the graph nodes with high compatibilities along the
stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two
connected nodes from their corresponding LSTM gate outputs, which is used to
generate a merging probability. The candidate graph structures are accordingly
generated where the nodes are grouped into cliques with their merging
probabilities. We then produce the new graph structure with a
Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in
local optimums by stochastic sampling with an acceptance probability. Once a
graph structure is accepted, a higher-level graph is then constructed by taking
the partitioned cliques as its nodes. During the evolving process,
representation becomes more abstracted in higher-levels where redundant
information is filtered out, allowing more efficient propagation of long-range
data dependencies. We evaluate the effectiveness of structure-evolving LSTM in
the application of semantic object parsing and demonstrate its advantage over
state-of-the-art LSTM models on standard benchmarks.Comment: To appear in CVPR 2017 as a spotlight pape
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