49,583 research outputs found
ANAE: Learning Node Context Representation for Attributed Network Embedding
Attributed network embedding aims to learn low-dimensional node
representations from both network structure and node attributes. Existing
methods can be categorized into two groups: (1) the first group learns two
separated node representations from network structure and node attribute
respectively and concatenates them together; (2) the other group obtains node
representations by translating node attributes into network structure or vice
versa. However, both groups have their drawbacks. The first group neglects the
correlation between network structure and node attributes, while the second
group assumes strong dependence between these two types of information. In this
paper, we address attributed network embedding from a novel perspective, i.e.,
learning node context representation for each node via modeling its attributed
local subgraph. To achieve this goal, we propose a novel attributed network
auto-encoder framework, namely ANAE. For a target node, ANAE first aggregates
the attribute information from its attributed local subgraph, obtaining its
low-dimensional representation. Next, ANAE diffuses the representation of the
target node to nodes in its local subgraph to reconstruct their attributes.
Such an encoder-decoder framework allows the learned representations to better
preserve the context information manifested in both network structure and node
attributes, thus having high capacity to learn good node representations for
attributed network. Extensive experimental results on real-world datasets
demonstrate that the proposed framework outperforms the state-of-the-art
approaches at the tasks of link prediction and node classification
Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate
subsequent graph analytics tasks like link prediction and graph clustering.
Most approaches on graph embedding focus on preserving the graph structure or
minimizing the reconstruction errors for graph data. They have mostly
overlooked the embedding distribution of the latent codes, which unfortunately
may lead to inferior representation in many cases. In this paper, we present a
novel adversarially regularized framework for graph embedding. By employing the
graph convolutional network as an encoder, our framework embeds the topological
information and node content into a vector representation, from which a graph
decoder is further built to reconstruct the input graph. The adversarial
training principle is applied to enforce our latent codes to match a prior
Gaussian or Uniform distribution. Based on this framework, we derive two
variants of adversarial models, the adversarially regularized graph autoencoder
(ARGA) and its variational version, adversarially regularized variational graph
autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit
other potential variations of ARGA and ARVGA to get a deeper understanding on
our designs. Experimental results compared among twelve algorithms for link
prediction and twenty algorithms for graph clustering validate our solutions.Comment: To appear in IEEE Transactions on Cybernetics. arXiv admin note:
substantial text overlap with arXiv:1802.0440
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for
graph data. However, such algorithms have many limitations that arise from the
use of random walks, e.g., the features resulting from these methods are unable
to transfer to new nodes and graphs as they are tied to node identity. In this
work, we introduce the notion of attributed random walks which serves as a
basis for generalizing existing methods such as DeepWalk, node2vec, and many
others that leverage random walks. Our proposed framework enables these methods
to be more widely applicable for both transductive and inductive learning as
well as for use on graphs with attributes (if available). This is achieved by
learning functions that generalize to new nodes and graphs. We show that our
proposed framework is effective with an average AUC improvement of 16.1% while
requiring on average 853 times less space than existing methods on a variety of
graphs from several domains
Modeling polypharmacy side effects with graph convolutional networks
The use of drug combinations, termed polypharmacy, is common to treat
patients with complex diseases and co-existing conditions. However, a major
consequence of polypharmacy is a much higher risk of adverse side effects for
the patient. Polypharmacy side effects emerge because of drug-drug
interactions, in which activity of one drug may change if taken with another
drug. The knowledge of drug interactions is limited because these complex
relationships are rare, and are usually not observed in relatively small
clinical testing. Discovering polypharmacy side effects thus remains an
important challenge with significant implications for patient mortality. Here,
we present Decagon, an approach for modeling polypharmacy side effects. The
approach constructs a multimodal graph of protein-protein interactions,
drug-protein target interactions, and the polypharmacy side effects, which are
represented as drug-drug interactions, where each side effect is an edge of a
different type. Decagon is developed specifically to handle such multimodal
graphs with a large number of edge types. Our approach develops a new graph
convolutional neural network for multirelational link prediction in multimodal
networks. Decagon predicts the exact side effect, if any, through which a given
drug combination manifests clinically. Decagon accurately predicts polypharmacy
side effects, outperforming baselines by up to 69%. We find that it
automatically learns representations of side effects indicative of
co-occurrence of polypharmacy in patients. Furthermore, Decagon models
particularly well side effects with a strong molecular basis, while on
predominantly non-molecular side effects, it achieves good performance because
of effective sharing of model parameters across edge types. Decagon creates
opportunities to use large pharmacogenomic and patient data to flag and
prioritize side effects for follow-up analysis.Comment: Presented at ISMB 201
Capturing Edge Attributes via Network Embedding
Network embedding, which aims to learn low-dimensional representations of
nodes, has been used for various graph related tasks including visualization,
link prediction and node classification. Most existing embedding methods rely
solely on network structure. However, in practice we often have auxiliary
information about the nodes and/or their interactions, e.g., content of
scientific papers in co-authorship networks, or topics of communication in
Twitter mention networks. Here we propose a novel embedding method that uses
both network structure and edge attributes to learn better network
representations. Our method jointly minimizes the reconstruction error for
higher-order node neighborhood, social roles and edge attributes using a deep
architecture that can adequately capture highly non-linear interactions. We
demonstrate the efficacy of our model over existing state-of-the-art methods on
a variety of real-world networks including collaboration networks, and social
networks. We also observe that using edge attributes to inform network
embedding yields better performance in downstream tasks such as link prediction
and node classification
A simple yet effective baseline for non-attributed graph classification
Graphs are complex objects that do not lend themselves easily to typical
learning tasks. Recently, a range of approaches based on graph kernels or graph
neural networks have been developed for graph classification and for
representation learning on graphs in general. As the developed methodologies
become more sophisticated, it is important to understand which components of
the increasingly complex methods are necessary or most effective.
As a first step, we develop a simple yet meaningful graph representation, and
explore its effectiveness in graph classification. We test our baseline
representation for the graph classification task on a range of graph datasets.
Interestingly, this simple representation achieves similar performance as the
state-of-the-art graph kernels and graph neural networks for non-attributed
graph classification. Its performance on classifying attributed graphs is
slightly weaker as it does not incorporate attributes. However, given its
simplicity and efficiency, we believe that it still serves as an effective
baseline for attributed graph classification. Our graph representation is
efficient (linear-time) to compute. We also provide a simple connection with
the graph neural networks.
Note that these observations are only for the task of graph classification
while existing methods are often designed for a broader scope including node
embedding and link prediction. The results are also likely biased due to the
limited amount of benchmark datasets available. Nevertheless, the good
performance of our simple baseline calls for the development of new, more
comprehensive benchmark datasets so as to better evaluate and analyze different
graph learning methods. Furthermore, given the computational efficiency of our
graph summary, we believe that it is a good candidate as a baseline method for
future graph classification (or even other graph learning) studies.Comment: 13 pages. Shorter version appears at 2019 ICLR Workshop:
Representation Learning on Graphs and Manifolds. arXiv admin note: text
overlap with arXiv:1810.00826 by other author
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
However, such algorithms have many limitations that arise from the use of
random walks, e.g., the features resulting from these methods are unable to
transfer to new nodes and graphs as they are tied to vertex identity. In this
work, we introduce the Role2Vec framework which uses the flexible notion of
attributed random walks, and serves as a basis for generalizing existing
methods such as DeepWalk, node2vec, and many others that leverage random walks.
Our proposed framework enables these methods to be more widely applicable for
both transductive and inductive learning as well as for use on graphs with
attributes (if available). This is achieved by learning functions that
generalize to new nodes and graphs. We show that our proposed framework is
effective with an average AUC improvement of 16.55% while requiring on average
853x less space than existing methods on a variety of graphs.Comment: StarAI workshop @ IJCAI 201
Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works studying adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph structure data due to its representation challenges. Given the importance
of graph analysis, an increasing number of works start to analyze the
robustness of machine learning models on graph data. Nevertheless, current
studies considering adversarial behaviors on graph data usually focus on
specific types of attacks with certain assumptions. In addition, each work
proposes its own mathematical formulation which makes the comparison among
different methods difficult. Therefore, in this paper, we aim to survey
existing adversarial learning strategies on graph data and first provide a
unified formulation for adversarial learning on graph data which covers most
adversarial learning studies on graph. Moreover, we also compare different
attacks and defenses on graph data and discuss their corresponding
contributions and limitations. In this work, we systemically organize the
considered works based on the features of each topic. This survey not only
serves as a reference for the research community, but also brings a clear image
researchers outside this research domain. Besides, we also create an online
resource and keep updating the relevant papers during the last two years. More
details of the comparisons of various studies based on this survey are
open-sourced at
https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date
information, please check our Github repository:
https://github.com/YingtongDou/graph-adversarial-learning-literatur
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
Learning graph representations is a fundamental task aimed at capturing
various properties of graphs in vector space. The most recent methods learn
such representations for static networks. However, real world networks evolve
over time and have varying dynamics. Capturing such evolution is key to
predicting the properties of unseen networks. To understand how the network
dynamics affect the prediction performance, we propose an embedding approach
which learns the structure of evolution in dynamic graphs and can predict
unseen links with higher precision. Our model, dyngraph2vec, learns the
temporal transitions in the network using a deep architecture composed of dense
and recurrent layers. We motivate the need of capturing dynamics for prediction
on a toy data set created using stochastic block models. We then demonstrate
the efficacy of dyngraph2vec over existing state-of-the-art methods on two real
world data sets. We observe that learning dynamics can improve the quality of
embedding and yield better performance in link prediction
Deep Feature Learning of Multi-Network Topology for Node Classification
Networks are ubiquitous structure that describes complex relationships
between different entities in the real world. As a critical component of
prediction task over nodes in networks, learning the feature representation of
nodes has become one of the most active areas recently. Network Embedding,
aiming to learn non-linear and low-dimensional feature representation based on
network topology, has been proved to be helpful on tasks of network analysis,
especially node classification. For many real-world systems, multiple types of
relations are naturally represented by multiple networks. However, existing
network embedding methods mainly focus on single network embedding and neglect
the information shared among different networks. In this paper, we propose a
novel multiple network embedding method based on semisupervised autoencoder,
named DeepMNE, which captures complex topological structures of multi-networks
and takes the correlation among multi-networks into account. We evaluate
DeepMNE on the task of node classification with two real-world datasets. The
experimental results demonstrate the superior performance of our method over
four state-of-the-art algorithms
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