4,450 research outputs found
Spectral-based Graph Convolutional Network for Directed Graphs
Graph convolutional networks(GCNs) have become the most popular approaches
for graph data in these days because of their powerful ability to extract
features from graph. GCNs approaches are divided into two categories,
spectral-based and spatial-based. As the earliest convolutional networks for
graph data, spectral-based GCNs have achieved impressive results in many graph
related analytics tasks. However, spectral-based models cannot directly work on
directed graphs. In this paper, we propose an improved spectral-based GCN for
the directed graph by leveraging redefined Laplacians to improve its
propagation model. Our approach can work directly on directed graph data in
semi-supervised nodes classification tasks. Experiments on a number of directed
graph datasets demonstrate that our approach outperforms the state-of-the-art
methods
Directed Graph Convolutional Network
Graph Convolutional Networks (GCNs) have been widely used due to their
outstanding performance in processing graph-structured data. However, the
undirected graphs limit their application scope. In this paper, we extend
spectral-based graph convolution to directed graphs by using first- and
second-order proximity, which can not only retain the connection properties of
the directed graph, but also expand the receptive field of the convolution
operation. A new GCN model, called DGCN, is then designed to learn
representations on the directed graph, leveraging both the first- and
second-order proximity information. We empirically show the fact that GCNs
working only with DGCNs can encode more useful information from graph and help
achieve better performance when generalized to other models. Moreover,
extensive experiments on citation networks and co-purchase datasets demonstrate
the superiority of our model against the state-of-the-art methods
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years,
ranging from image classification and video processing to speech recognition
and natural language understanding. The data in these tasks are typically
represented in the Euclidean space. However, there is an increasing number of
applications where data are generated from non-Euclidean domains and are
represented as graphs with complex relationships and interdependency between
objects. The complexity of graph data has imposed significant challenges on
existing machine learning algorithms. Recently, many studies on extending deep
learning approaches for graph data have emerged. In this survey, we provide a
comprehensive overview of graph neural networks (GNNs) in data mining and
machine learning fields. We propose a new taxonomy to divide the
state-of-the-art graph neural networks into four categories, namely recurrent
graph neural networks, convolutional graph neural networks, graph autoencoders,
and spatial-temporal graph neural networks. We further discuss the applications
of graph neural networks across various domains and summarize the open source
codes, benchmark data sets, and model evaluation of graph neural networks.
Finally, we propose potential research directions in this rapidly growing
field.Comment: Minor revision (updated tables and references
Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep
learning methods for non-Euclidean structured data such as graphs. In this
paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture
that alternates convolution-like operations on the graph and its dual. Our
approach allows to learn both vertex- and edge features and generalizes the
previous graph attention (GAT) model. We provide extensive experimental
validation showing state-of-the-art results on a variety of tasks tested on
established graph benchmarks, including CORA and Citeseer citation networks as
well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender
systems
A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
Traffic forecasting is crucial for urban traffic management and guidance.
However, existing methods rarely exploit the time-frequency properties of
traffic speed observations, and often neglect the propagation of traffic flows
from upstream to downstream road segments. In this paper, we propose a hybrid
approach that learns the spatio-temporal dependency in traffic flows and
predicts short-term traffic speeds on a road network. Specifically, we employ
wavelet transform to decompose raw traffic data into several components with
different frequency sub-bands. A Motif-based Graph Convolutional Recurrent
Neural Network (Motif-GCRNN) and Auto-Regressive Moving Average (ARMA) are used
to train and predict low-frequency components and high-frequency components,
respectively. In the Motif-GCRNN framework, we integrate Graph Convolutional
Networks (GCNs) with local sub-graph structures - Motifs - to capture the
spatial correlations among road segments, and apply Long Short-Term Memory
(LSTM) to extract the short-term and periodic patterns in traffic speeds.
Experiments on a traffic dataset collected in Chengdu, China, demonstrate that
the proposed hybrid method outperforms six state-of-art prediction methods.Comment: 7 pages, IJCAI1
Graph2Seq: Scalable Learning Dynamics for Graphs
Neural networks have been shown to be an effective tool for learning
algorithms over graph-structured data. However, graph representation
techniques---that convert graphs to real-valued vectors for use with neural
networks---are still in their infancy. Recent works have proposed several
approaches (e.g., graph convolutional networks), but these methods have
difficulty scaling and generalizing to graphs with different sizes and shapes.
We present Graph2Seq, a new technique that represents vertices of graphs as
infinite time-series. By not limiting the representation to a fixed dimension,
Graph2Seq scales naturally to graphs of arbitrary sizes and shapes. Graph2Seq
is also reversible, allowing full recovery of the graph structure from the
sequences. By analyzing a formal computational model for graph representation,
we show that an unbounded sequence is necessary for scalability. Our
experimental results with Graph2Seq show strong generalization and new
state-of-the-art performance on a variety of graph combinatorial optimization
problems
Topology and Prediction Focused Research on Graph Convolutional Neural Networks
Important advances have been made using convolutional neural network (CNN)
approaches to solve complicated problems in areas that rely on grid structured
data such as image processing and object classification. Recently, research on
graph convolutional neural networks (GCNN) has increased dramatically as
researchers try to replicate the success of CNN for graph structured data.
Unfortunately, traditional CNN methods are not readily transferable to GCNN,
given the irregularity and geometric complexity of graphs. The emerging field
of GCNN is further complicated by research papers that differ greatly in their
scope, detail, and level of academic sophistication needed by the reader.
The present paper provides a review of some basic properties of GCNN. As a
guide to the interested reader, recent examples of GCNN research are then
grouped according to techniques that attempt to uncover the underlying topology
of the graph model and those that seek to generalize traditional CNN methods on
graph data to improve prediction of class membership. Discrete Signal
Processing on Graphs (DSPg) is used as a theoretical framework to better
understand some of the performance gains and limitations of these recent GCNN
approaches. A brief discussion of Topology Adaptive Graph Convolutional
Networks (TAGCN) is presented as an approach motivated by DSPg and future
research directions using this approach are briefly discussed
Topological based classification of paper domains using graph convolutional networks
The main approaches for node classification in graphs are information
propagation and the association of the class of the node with external
information. State of the art methods merge these approaches through Graph
Convolutional Networks. We here use the association of topological features of
the nodes with their class to predict this class. Moreover, combining
topological information with information propagation improves classification
accuracy on the standard CiteSeer and Cora paper classification task.
Topological features and information propagation produce results almost as good
as text-based classification, without no textual or content information. We
propose to represent the topology and information propagation through a GCN
with the neighboring training node classification as an input and the current
node classification as output. Such a formalism outperforms state of the art
methods
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (3) inherent difficulty of long-term forecasting. To address these
challenges, we propose to model the traffic flow as a diffusion process on a
directed graph and introduce Diffusion Convolutional Recurrent Neural Network
(DCRNN), a deep learning framework for traffic forecasting that incorporates
both spatial and temporal dependency in the traffic flow. Specifically, DCRNN
captures the spatial dependency using bidirectional random walks on the graph,
and the temporal dependency using the encoder-decoder architecture with
scheduled sampling. We evaluate the framework on two real-world large scale
road network traffic datasets and observe consistent improvement of 12% - 15%
over state-of-the-art baselines.Comment: Published as a conference paper at ICLR 201
Topology Adaptive Graph Convolutional Networks
Spectral graph convolutional neural networks (CNNs) require approximation to
the convolution to alleviate the computational complexity, resulting in
performance loss. This paper proposes the topology adaptive graph convolutional
network (TAGCN), a novel graph convolutional network defined in the vertex
domain. We provide a systematic way to design a set of fixed-size learnable
filters to perform convolutions on graphs. The topologies of these filters are
adaptive to the topology of the graph when they scan the graph to perform
convolution. The TAGCN not only inherits the properties of convolutions in CNN
for grid-structured data, but it is also consistent with convolution as defined
in graph signal processing. Since no approximation to the convolution is
needed, TAGCN exhibits better performance than existing spectral CNNs on a
number of data sets and is also computationally simpler than other recent
methods.Comment: 13 page
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