3,096 research outputs found
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
Learning Large-Scale Topological Maps Using Sum-Product Networks
In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topological map extraction, we have found little previous work on the
problem of learning the topological map using a probabilistic model. Learning a
topological map means learning the structure of the large-scale space and
dependency between places, for example, how the evidence of a group of places
influence the attributes of other places. This is an important step towards
planning complex actions in the environment. In this thesis, we consider the
problem of using probabilistic deep learning model to learn the topological
map, which is essentially a sparse undirected graph where nodes represent
places annotated with their semantic attributes (e.g. place category). We
propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
due to their unique properties. We present two methods for learning topological
maps using SPNs: the place grid method and the template-based method. We
contribute an algorithm that builds SPNs for graphs using template models. Our
experiments evaluate the ability of our models to enable robots to infer
semantic attributes and detect maps with novel semantic attribute arrangements.
Our results demonstrate their understanding of the topological map structure
and spatial relations between places.Comment: 26 pages, 14 figures, senior thesis for departmental honors at the
Allen School of Computer Science and Engineering at the University of
Washingto
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
PyTorch-BigGraph: A Large-scale Graph Embedding System
Graph embedding methods produce unsupervised node features from graphs that
can then be used for a variety of machine learning tasks. Modern graphs,
particularly in industrial applications, contain billions of nodes and
trillions of edges, which exceeds the capability of existing embedding systems.
We present PyTorch-BigGraph (PBG), an embedding system that incorporates
several modifications to traditional multi-relation embedding systems that
allow it to scale to graphs with billions of nodes and trillions of edges. PBG
uses graph partitioning to train arbitrarily large embeddings on either a
single machine or in a distributed environment. We demonstrate comparable
performance with existing embedding systems on common benchmarks, while
allowing for scaling to arbitrarily large graphs and parallelization on
multiple machines. We train and evaluate embeddings on several large social
network graphs as well as the full Freebase dataset, which contains over 100
million nodes and 2 billion edges
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
Language classification from bilingual word embedding graphs
We study the role of the second language in bilingual word embeddings in
monolingual semantic evaluation tasks. We find strongly and weakly positive
correlations between down-stream task performance and second language
similarity to the target language. Additionally, we show how bilingual word
embeddings can be employed for the task of semantic language classification and
that joint semantic spaces vary in meaningful ways across second languages. Our
results support the hypothesis that semantic language similarity is influenced
by both structural similarity as well as geography/contact.Comment: To be published at Coling 201
Pixels to Graphs by Associative Embedding
Graphs are a useful abstraction of image content. Not only can graphs
represent details about individual objects in a scene but they can capture the
interactions between pairs of objects. We present a method for training a
convolutional neural network such that it takes in an input image and produces
a full graph definition. This is done end-to-end in a single stage with the use
of associative embeddings. The network learns to simultaneously identify all of
the elements that make up a graph and piece them together. We benchmark on the
Visual Genome dataset, and demonstrate state-of-the-art performance on the
challenging task of scene graph generation.Comment: Updated numbers. Code and pretrained models available at
https://github.com/umich-vl/px2grap
Applications of Structural Balance in Signed Social Networks
We present measures, models and link prediction algorithms based on the
structural balance in signed social networks. Certain social networks contain,
in addition to the usual 'friend' links, 'enemy' links. These networks are
called signed social networks. A classical and major concept for signed social
networks is that of structural balance, i.e., the tendency of triangles to be
'balanced' towards including an even number of negative edges, such as
friend-friend-friend and friend-enemy-enemy triangles. In this article, we
introduce several new signed network analysis methods that exploit structural
balance for measuring partial balance, for finding communities of people based
on balance, for drawing signed social networks, and for solving the problem of
link prediction. Notably, the introduced methods are based on the signed graph
Laplacian and on the concept of signed resistance distances. We evaluate our
methods on a collection of four signed social network datasets.Comment: 37 page
Multiple-Human Parsing in the Wild
Human parsing is attracting increasing research attention. In this work, we
aim to push the frontier of human parsing by introducing the problem of
multi-human parsing in the wild. Existing works on human parsing mainly tackle
single-person scenarios, which deviates from real-world applications where
multiple persons are present simultaneously with interaction and occlusion. To
address the multi-human parsing problem, we introduce a new multi-human parsing
(MHP) dataset and a novel multi-human parsing model named MH-Parser. The MHP
dataset contains multiple persons captured in real-world scenes with
pixel-level fine-grained semantic annotations in an instance-aware setting. The
MH-Parser generates global parsing maps and person instance masks
simultaneously in a bottom-up fashion with the help of a new Graph-GAN model.
We envision that the MHP dataset will serve as a valuable data resource to
develop new multi-human parsing models, and the MH-Parser offers a strong
baseline to drive future research for multi-human parsing in the wild.Comment: The first two authors are with equal contributio
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels
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