2,738 research outputs found
AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection
The explosive growth and increasing sophistication of Android malware call
for new defensive techniques that are capable of protecting mobile users
against novel threats. In this paper, we first extract the runtime Application
Programming Interface (API) call sequences from Android apps, and then analyze
higher-level semantic relations within the ecosystem to comprehensively
characterize the apps. To model different types of entities (i.e., app, API,
IMEI, signature, affiliation) and the rich semantic relations among them, we
then construct a structural heterogeneous information network (HIN) and present
meta-path based approach to depict the relatedness over apps. To efficiently
classify nodes (e.g., apps) in the constructed HIN, we propose the HinLearning
method to first obtain in-sample node embeddings and then learn representations
of out-of-sample nodes without rerunning/adjusting HIN embeddings at the first
attempt. Afterwards, we design a deep neural network (DNN) classifier taking
the learned HIN representations as inputs for Android malware detection. A
comprehensive experimental study on the large-scale real sample collections
from Tencent Security Lab is performed to compare various baselines. Promising
experimental results demonstrate that our developed system AiDroid which
integrates our proposed method outperforms others in real-time Android malware
detection. AiDroid has already been incorporated into Tencent Mobile Security
product that serves millions of users worldwide.Comment: The revised version will be published in IJCAI'2019 entitled
"Out-of-sample Node Representation Learning for Heterogeneous Graph in
Real-time Android Malware Detection
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
A Survey on Embedding Dynamic Graphs
Embedding static graphs in low-dimensional vector spaces plays a key role in
network analytics and inference, supporting applications like node
classification, link prediction, and graph visualization. However, many
real-world networks present dynamic behavior, including topological evolution,
feature evolution, and diffusion. Therefore, several methods for embedding
dynamic graphs have been proposed to learn network representations over time,
facing novel challenges, such as time-domain modeling, temporal features to be
captured, and the temporal granularity to be embedded. In this survey, we
overview dynamic graph embedding, discussing its fundamentals and the recent
advances developed so far. We introduce the formal definition of dynamic graph
embedding, focusing on the problem setting and introducing a novel taxonomy for
dynamic graph embedding input and output. We further explore different dynamic
behaviors that may be encompassed by embeddings, classifying by topological
evolution, feature evolution, and processes on networks. Afterward, we describe
existing techniques and propose a taxonomy for dynamic graph embedding
techniques based on algorithmic approaches, from matrix and tensor
factorization to deep learning, random walks, and temporal point processes. We
also elucidate main applications, including dynamic link prediction, anomaly
detection, and diffusion prediction, and we further state some promising
research directions in the area.Comment: 41 pages, 10 figure
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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
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
Unsupervised learning of latent edge types from multi-relational data
Many relational datasets, including relational databases, feature links of different types (e.g., actors act in movies, users rate movies), known as multi-relational, heterogeneous, or multilayer networks. Edge types/network layers are often not explicitly labeled, even when they influence the underlying graph generation process. For example, IMDb lists Tom Cruise as a cast member of Mission Impossible, but not as its star. Inferring latent layers is useful for relational prediction tasks (e.g., predict Tom Cruise’s salary or his presence in other movies). This thesis discusses Latent Layer Generative Framework - LLGF, a generative framework for learning latent layers that generalizes Variational Graph Auto-Encoders (VGAEs) with arbitrary node representation encoders and link generation decoders. The decoder treats the observed edge type signal as a linear combination of latent layer decoders. The encoder infers parallel node representations, one for each latent layer. We evaluate our proposed framework, LLGF, on eight benchmark graph learning datasets for this study. Four of the datasets are heterogeneous (originally labeled with edge types); we apply LLGF after removing the edge labels to assess how well it recovers ground-truth layers. LLGF increases link prediction accuracy, especially for heterogeneous datasets (up to 5% AUC), and recovers the ground-truth layers exceptionally well
Physical Attribute Prediction Using Deep Residual Neural Networks
Images taken from the Internet have been used alongside Deep Learning for
many different tasks such as: smile detection, ethnicity, hair style, hair
colour, gender and age prediction. After witnessing these usages, we were
wondering what other attributes can be predicted from facial images available
on the Internet. In this paper we tackle the prediction of physical attributes
from face images using Convolutional Neural Networks trained on our dataset
named FIRW. We crawled around 61, 000 images from the web, then use face
detection to crop faces from these real world images. We choose ResNet-50 as
our base network architecture. This network was pretrained for the task of face
recognition by using the VGG-Face dataset, and we finetune it by using our own
dataset to predict physical attributes. Separate networks are trained for the
prediction of body type, ethnicity, gender, height and weight; our models
achieve the following accuracies for theses tasks, respectively: 84.58%,
87.34%, 97.97%, 70.51%, 63.99%. To validate our choice of ResNet-50 as the base
architecture, we also tackle the famous CelebA dataset. Our models achieve an
averagy accuracy of 91.19% on CelebA, which is comparable to state-of-the-art
approaches
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information
Network embedding is the process of learning low-dimensional representations
for nodes in a network, while preserving node features. Existing studies only
leverage network structure information and focus on preserving structural
features. However, nodes in real-world networks often have a rich set of
attributes providing extra semantic information. It has been demonstrated that
both structural and attribute features are important for network analysis
tasks. To preserve both features, we investigate the problem of integrating
structure and attribute information to perform network embedding and propose a
Multimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linear
network structures and the complex interactions among structures and
attributes, using a deep model consisting of multiple layers of non-linear
functions. Since structures and attributes are two different types of
information, a multimodal learning method is adopted to pre-process them and
help the model to better capture the correlations between node structure and
attribute information. We employ both structural proximity and attribute
proximity in the loss function to preserve the respective features and the
representations are obtained by minimizing the loss function. Results of
extensive experiments on four real-world datasets show that the proposed method
performs significantly better than baselines on a variety of tasks, which
demonstrate the effectiveness and generality of our method.Comment: 15 pages, 10 figure
COSINE: Compressive Network Embedding on Large-scale Information Networks
There is recently a surge in approaches that learn low-dimensional embeddings
of nodes in networks. As there are many large-scale real-world networks, it's
inefficient for existing approaches to store amounts of parameters in memory
and update them edge after edge. With the knowledge that nodes having similar
neighborhood will be close to each other in embedding space, we propose COSINE
(COmpresSIve NE) algorithm which reduces the memory footprint and accelerates
the training process by parameters sharing among similar nodes. COSINE applies
graph partitioning algorithms to networks and builds parameter sharing
dependency of nodes based on the result of partitioning. With parameters
sharing among similar nodes, COSINE injects prior knowledge about higher
structural information into training process which makes network embedding more
efficient and effective. COSINE can be applied to any embedding lookup method
and learn high-quality embeddings with limited memory and shorter training
time. We conduct experiments of multi-label classification and link prediction,
where baselines and our model have the same memory usage. Experimental results
show that COSINE gives baselines up to 23% increase on classification and up to
25% increase on link prediction. Moreover, time of all representation learning
methods using COSINE decreases from 30% to 70%
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