1,271 research outputs found
Graph Convolutional Neural Networks via Motif-based Attention
Many real-world problems can be represented as graph-based learning problems.
In this paper, we propose a novel framework for learning spatial and
attentional convolution neural networks on arbitrary graphs. Different from
previous convolutional neural networks on graphs, we first design a
motif-matching guided subgraph normalization method to capture neighborhood
information. Then we implement subgraph-level self-attentional layers to learn
different importances from different subgraphs to solve graph classification
problems. Analogous to image-based attentional convolution networks that
operate on locally connected and weighted regions of the input, we also extend
graph normalization from one-dimensional node sequence to two-dimensional node
grid by leveraging motif-matching, and design self-attentional layers without
requiring any kinds of cost depending on prior knowledge of the graph
structure. Our results on both bioinformatics and social network datasets show
that we can significantly improve graph classification benchmarks over
traditional graph kernel and existing deep models
Higher-order Graph Convolutional Networks
Following the success of deep convolutional networks in various vision and
speech related tasks, researchers have started investigating generalizations of
the well-known technique for graph-structured data. A recently-proposed method
called Graph Convolutional Networks has been able to achieve state-of-the-art
results in the task of node classification. However, since the proposed method
relies on localized first-order approximations of spectral graph convolutions,
it is unable to capture higher-order interactions between nodes in the graph.
In this work, we propose a motif-based graph attention model, called Motif
Convolutional Networks (MCNs), which generalizes past approaches by using
weighted multi-hop motif adjacency matrices to capture higher-order
neighborhoods. A novel attention mechanism is used to allow each individual
node to select the most relevant neighborhood to apply its filter. Experiments
show that our proposed method is able to achieve state-of-the-art results on
the semi-supervised node classification task
Motif-based Convolutional Neural Network on Graphs
This paper introduces a generalization of Convolutional Neural Networks
(CNNs) to graphs with irregular linkage structures, especially heterogeneous
graphs with typed nodes and schemas. We propose a novel spatial convolution
operation to model the key properties of local connectivity and translation
invariance, using high-order connection patterns or motifs. We develop a novel
deep architecture Motif-CNN that employs an attention model to combine the
features extracted from multiple patterns, thus effectively capturing
high-order structural and feature information. Our experiments on
semi-supervised node classification on real-world social networks and multiple
representative heterogeneous graph datasets indicate significant gains of 6-21%
over existing graph CNNs and other state-of-the-art techniques
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
Link Prediction via Higher-Order Motif Features
Link prediction requires predicting which new links are likely to appear in a
graph. Being able to predict unseen links with good accuracy has important
applications in several domains such as social media, security, transportation,
and recommendation systems. A common approach is to use features based on the
common neighbors of an unconnected pair of nodes to predict whether the pair
will form a link in the future. In this paper, we present an approach for link
prediction that relies on higher-order analysis of the graph topology, well
beyond common neighbors. We treat the link prediction problem as a supervised
classification problem, and we propose a set of features that depend on the
patterns or motifs that a pair of nodes occurs in. By using motifs of sizes 3,
4, and 5, our approach captures a high level of detail about the graph topology
within the neighborhood of the pair of nodes, which leads to a higher
classification accuracy. In addition to proposing the use of motif-based
features, we also propose two optimizations related to constructing the
classification dataset from the graph. First, to ensure that positive and
negative examples are treated equally when extracting features, we propose
adding the negative examples to the graph as an alternative to the common
approach of removing the positive ones. Second, we show that it is important to
control for the shortest-path distance when sampling pairs of nodes to form
negative examples, since the difficulty of prediction varies with the
shortest-path distance. We experimentally demonstrate that using off-the-shelf
classifiers with a well constructed classification dataset results in up to 10
percentage points increase in accuracy over prior topology-based and feature
learning methods.Comment: Extended version of paper that appears in ECML/PKDD 201
Graph R-CNN for Scene Graph Generation
We propose a novel scene graph generation model called Graph R-CNN, that is
both effective and efficient at detecting objects and their relations in
images. Our model contains a Relation Proposal Network (RePN) that efficiently
deals with the quadratic number of potential relations between objects in an
image. We also propose an attentional Graph Convolutional Network (aGCN) that
effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and
realistic than existing metrics. We report state-of-the-art performance on
scene graph generation as evaluated using both existing and our proposed
metrics.Comment: 16 pages, ECCV 2018 camera read
MeshGAN: Non-linear 3D Morphable Models of Faces
Generative Adversarial Networks (GANs) are currently the method of choice for
generating visual data. Certain GAN architectures and training methods have
demonstrated exceptional performance in generating realistic synthetic images
(in particular, of human faces). However, for 3D object, GANs still fall short
of the success they have had with images. One of the reasons is due to the fact
that so far GANs have been applied as 3D convolutional architectures to
discrete volumetric representations of 3D objects. In this paper, we propose
the first intrinsic GANs architecture operating directly on 3D meshes (named as
MeshGAN). Both quantitative and qualitative results are provided to show that
MeshGAN can be used to generate high-fidelity 3D face with rich identities and
expressions
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
Identifying controversial posts on social media is a fundamental task for
mining public sentiment, assessing the influence of events, and alleviating the
polarized views. However, existing methods fail to 1) effectively incorporate
the semantic information from content-related posts; 2) preserve the structural
information for reply relationship modeling; 3) properly handle posts from
topics dissimilar to those in the training set. To overcome the first two
limitations, we propose Topic-Post-Comment Graph Convolutional Network
(TPC-GCN), which integrates the information from the graph structure and
content of topics, posts, and comments for post-level controversy detection. As
to the third limitation, we extend our model to Disentangled TPC-GCN
(DTPC-GCN), to disentangle topic-related and topic-unrelated features and then
fuse dynamically. Extensive experiments on two real-world datasets demonstrate
that our models outperform existing methods. Analysis of the results and cases
proves that our models can integrate both semantic and structural information
with significant generalizability.Comment: 12 pages, 3 figures, 6 tables; To appear in ACL 2020 (long paper
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
Graph Neural Networks (GNNs) have been popularly used for analyzing
non-Euclidean data such as social network data and biological data. Despite
their success, the design of graph neural networks requires a lot of manual
work and domain knowledge. In this paper, we propose a Graph Neural
Architecture Search method (GraphNAS for short) that enables automatic search
of the best graph neural architecture based on reinforcement learning.
Specifically, GraphNAS first uses a recurrent network to generate
variable-length strings that describe the architectures of graph neural
networks, and then trains the recurrent network with reinforcement learning to
maximize the expected accuracy of the generated architectures on a validation
data set. Extensive experimental results on node classification tasks in both
transductive and inductive learning settings demonstrate that GraphNAS can
achieve consistently better performance on the Cora, Citeseer, Pubmed citation
network, and protein-protein interaction network. On node classification tasks,
GraphNAS can design a novel network architecture that rivals the best
human-invented architecture in terms of test set accuracy
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Deep learning systems have become ubiquitous in many aspects of our lives.
Unfortunately, it has been shown that such systems are vulnerable to
adversarial attacks, making them prone to potential unlawful uses. Designing
deep neural networks that are robust to adversarial attacks is a fundamental
step in making such systems safer and deployable in a broader variety of
applications (e.g. autonomous driving), but more importantly is a necessary
step to design novel and more advanced architectures built on new computational
paradigms rather than marginally building on the existing ones. In this paper
we introduce PeerNets, a novel family of convolutional networks alternating
classical Euclidean convolutions with graph convolutions to harness information
from a graph of peer samples. This results in a form of non-local forward
propagation in the model, where latent features are conditioned on the global
structure induced by the graph, that is up to 3 times more robust to a variety
of white- and black-box adversarial attacks compared to conventional
architectures with almost no drop in accuracy
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