14,167 research outputs found
Recurrent Attention Walk for Semi-supervised Classification
In this paper, we study the graph-based semi-supervised learning for
classifying nodes in attributed networks, where the nodes and edges possess
content information. Recent approaches like graph convolution networks and
attention mechanisms have been proposed to ensemble the first-order neighbors
and incorporate the relevant neighbors. However, it is costly (especially in
memory) to consider all neighbors without a prior differentiation. We propose
to explore the neighborhood in a reinforcement learning setting and find a walk
path well-tuned for classifying the unlabelled target nodes. We let an agent
(of node classification task) walk over the graph and decide where to direct to
maximize classification accuracy. We define the graph walk as a partially
observable Markov decision process (POMDP). The proposed method is flexible for
working in both transductive and inductive setting. Extensive experiments on
four datasets demonstrate that our proposed method outperforms several
state-of-the-art methods. Several case studies also illustrate the meaningful
movement trajectory made by the agent.Comment: Accepted for WSDM 202
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
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations for
graph-structured data. Graph representation learning methods have generally
fallen into three main categories, based on the availability of labeled data.
The first, network embedding (such as shallow graph embedding or graph
auto-encoders), focuses on learning unsupervised representations of relational
structure. The second, graph regularized neural networks, leverages graphs to
augment neural network losses with a regularization objective for
semi-supervised learning. The third, graph neural networks, aims to learn
differentiable functions over discrete topologies with arbitrary structure.
However, despite the popularity of these areas there has been surprisingly
little work on unifying the three paradigms. Here, we aim to bridge the gap
between graph neural networks, network embedding and graph regularization
models. We propose a comprehensive taxonomy of representation learning methods
for graph-structured data, aiming to unify several disparate bodies of work.
Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which
generalizes popular algorithms for semi-supervised learning on graphs (e.g.
GraphSage, Graph Convolutional Networks, Graph Attention Networks), and
unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc)
into a single consistent approach. To illustrate the generality of this
approach, we fit over thirty existing methods into this framework. We believe
that this unifying view both provides a solid foundation for understanding the
intuition behind these methods, and enables future research in the area
Attention-based Graph Neural Network for Semi-supervised Learning
Recently popularized graph neural networks achieve the state-of-the-art
accuracy on a number of standard benchmark datasets for graph-based
semi-supervised learning, improving significantly over existing approaches.
These architectures alternate between a propagation layer that aggregates the
hidden states of the local neighborhood and a fully-connected layer. Perhaps
surprisingly, we show that a linear model, that removes all the intermediate
fully-connected layers, is still able to achieve a performance comparable to
the state-of-the-art models. This significantly reduces the number of
parameters, which is critical for semi-supervised learning where number of
labeled examples are small. This in turn allows a room for designing more
innovative propagation layers. Based on this insight, we propose a novel graph
neural network that removes all the intermediate fully-connected layers, and
replaces the propagation layers with attention mechanisms that respect the
structure of the graph. The attention mechanism allows us to learn a dynamic
and adaptive local summary of the neighborhood to achieve more accurate
predictions. In a number of experiments on benchmark citation networks
datasets, we demonstrate that our approach outperforms competing methods. By
examining the attention weights among neighbors, we show that our model
provides some interesting insights on how neighbors influence each other
A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling
Human behavior is a continuous stochastic spatio-temporal process which is
governed by semantic actions and affordances as well as latent factors.
Therefore, video-based human activity modeling is concerned with a number of
tasks such as inferring current and future semantic labels, predicting future
continuous observations as well as imagining possible future label and feature
sequences. In this paper we present a semi-supervised probabilistic deep latent
variable model that can represent both discrete labels and continuous
observations as well as latent dynamics over time. This allows the model to
solve several tasks at once without explicit fine-tuning. We focus here on the
tasks of action classification, detection, prediction and anticipation as well
as motion prediction and synthesis based on 3D human activity data recorded
with Kinect. We further extend the model to capture hierarchical label
structure and to model the dependencies between multiple entities, such as a
human and objects. Our experiments demonstrate that our principled approach to
human activity modeling can be used to detect current and anticipate future
semantic labels and to predict and synthesize future label and feature
sequences. When comparing our model to state-of-the-art approaches, which are
specifically designed for e.g. action classification, we find that our
probabilistic formulation outperforms or is comparable to these task specific
models
Deep Learning on Graphs: A Survey
Deep learning has been shown to be successful in a number of domains, ranging
from acoustics, images, to natural language processing. However, applying deep
learning to the ubiquitous graph data is non-trivial because of the unique
characteristics of graphs. Recently, substantial research efforts have been
devoted to applying deep learning methods to graphs, resulting in beneficial
advances in graph analysis techniques. In this survey, we comprehensively
review the different types of deep learning methods on graphs. We divide the
existing methods into five categories based on their model architectures and
training strategies: graph recurrent neural networks, graph convolutional
networks, graph autoencoders, graph reinforcement learning, and graph
adversarial methods. We then provide a comprehensive overview of these methods
in a systematic manner mainly by following their development history. We also
analyze the differences and compositions of different methods. Finally, we
briefly outline the applications in which they have been used and discuss
potential future research directions.Comment: Accepted by Transactions on Knowledge and Data Engineering. 24 pages,
11 figure
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Network embeddings, which learn low-dimensional representations for each
vertex in a large-scale network, have received considerable attention in recent
years. For a wide range of applications, vertices in a network are typically
accompanied by rich textual information such as user profiles, paper abstracts,
etc. We propose to incorporate semantic features into network embeddings by
matching important words between text sequences for all pairs of vertices. We
introduce a word-by-word alignment framework that measures the compatibility of
embeddings between word pairs, and then adaptively accumulates these alignment
features with a simple yet effective aggregation function. In experiments, we
evaluate the proposed framework on three real-world benchmarks for downstream
tasks, including link prediction and multi-label vertex classification. Results
demonstrate that our model outperforms state-of-the-art network embedding
methods by a large margin.Comment: To appear at EMNLP 201
Automatic Severity Classification of Coronary Artery Disease via Recurrent Capsule Network
Coronary artery disease (CAD) is one of the leading causes of cardiovascular
disease deaths. CAD condition progresses rapidly, if not diagnosed and treated
at an early stage may eventually lead to an irreversible state of the heart
muscle death. Invasive coronary arteriography is the gold standard technique
for CAD diagnosis. Coronary arteriography texts describe which part has
stenosis and how much stenosis is in details. It is crucial to conduct the
severity classification of CAD. In this paper, we employ a recurrent capsule
network (RCN) to extract semantic relations between clinical named entities in
Chinese coronary arteriography texts, through which we can automatically find
out the maximal stenosis for each lumen to inference how severe CAD is
according to the improved method of Gensini. Experimental results on the corpus
collected from Shanghai Shuguang Hospital show that our proposed method
achieves an accuracy of 97.0\% in the severity classification of CAD.Comment: 8 pages, 5 figure
Deep Learning for Sentiment Analysis : A Survey
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.Comment: 34 pages, 9 figures, 2 table
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the length of the text. We propose a sequence-to-sequence, purely
convolutional and deconvolutional autoencoding framework that is free of the
above issue, while also being computationally efficient. The proposed method is
simple, easy to implement and can be leveraged as a building block for many
applications. We show empirically that compared to RNNs, our framework is
better at reconstructing and correcting long paragraphs. Quantitative
evaluation on semi-supervised text classification and summarization tasks
demonstrate the potential for better utilization of long unlabeled text data.Comment: Accepted by NIPS 201
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