Joint Network Embedding of Network Structure and Node Attributes via Deep Autoencoder

2021 ◽  
Author(s):  
Yu Pan ◽  
Junhua Zou ◽  
Junyang Qiu ◽  
Shuaihui Wang ◽  
Guyu Hu ◽  
...  
Keyword(s):  
Network Structure ◽  
Network Embedding ◽  
Node Attributes

Extending Proximity Measures to Attributed Networks for Community Detection

2021 ◽  
Vol 30 (4) ◽  
pp. 441-455
Author(s):  
Rinat Aynulin ◽  
◽  
Pavel Chebotarev ◽  
◽  
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Network Structure ◽  
Real World ◽  
Adjacency Matrix ◽  
Attribute Information ◽  
Node Attributes ◽  
Proximity Measures ◽  
Definition Of ◽  
Attributed Networks

Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.

Network structure exploration in networks with node attributes

2016 ◽  
Vol 449 ◽  
pp. 240-253 ◽  
Author(s):  
Yi Chen ◽  
Xiaolong Wang ◽  
Junzhao Bu ◽  
Buzhou Tang ◽  
Xin Xiang
Keyword(s):  
Network Structure ◽  
Node Attributes

scholarly journals Temporal Network Embedding with High-Order Nonlinear Information

2020 ◽  
Vol 34 (04) ◽  
pp. 5436-5443
Author(s):  
Zhenyu Qiu ◽  
Wenbin Hu ◽  
Jia Wu ◽  
Weiwei Liu ◽  
Bo Du ◽  
...  
Keyword(s):  
Network Structure ◽  
High Order ◽  
Stochastic Gradient Descent ◽  
Temporal Network ◽  
Temporal Networks ◽  
Network Embedding ◽  
Nonlinear Structure ◽  
Temporal Proximity ◽  
Highly Nonlinear ◽  
Gradient Descent Algorithm

Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

scholarly journals User Profile Preserving Social Network Embedding

2017 ◽  
Author(s):  
Daokun Zhang ◽  
Jie Yin ◽  
Xingquan Zhu ◽  
Chengqi Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Dimensional Space ◽  
User Profile ◽  
Network Embedding ◽  
Network Nodes ◽  
Profile Information ◽  
Low Dimensional ◽  
Performance Gains

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.

scholarly journals DGE: Deep Generative Network Embedding Based on Commonality and Individuality

2020 ◽  
Vol 34 (04) ◽  
pp. 6949-6956
Author(s):  
Sheng Zhou ◽  
Xin Wang ◽  
Jiajun Bu ◽  
Martin Ester ◽  
Pinggang Yu ◽  
...  
Keyword(s):  
Network Topology ◽  
Real World ◽  
Information Source ◽  
Model Parameters ◽  
Network Embedding ◽  
Generative Process ◽  
Attributed Network ◽  
Node Attributes ◽  
Real World Datasets ◽  
The One

Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.

scholarly journals Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

2019 ◽  
Vol 33 ◽  
pp. 10061-10062 ◽  
Author(s):  
Yueyang Wang ◽  
Ziheng Duan ◽  
Binbing Liao ◽  
Fei Wu ◽  
Yueting Zhuang
Keyword(s):  
Real World ◽  
Network Embedding ◽  
High Quality ◽  
Embedding Method ◽  
The Real ◽  
Convolutional Networks ◽  
Attributed Network ◽  
Node Attributes ◽  
Attributed Networks

Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method.

scholarly journals Adversarial Mutual Information Learning for Network Embedding

2020 ◽  
Author(s):  
Dongxiao He ◽  
Lu Zhai ◽  
Zhigang Li ◽  
Di Jin ◽  
Liang Yang ◽  
...  
Keyword(s):  
Mutual Information ◽  
Gaussian Distribution ◽  
A Priori ◽  
Learning System ◽  
Generative Adversarial Networks ◽  
Network Embedding ◽  
System A ◽  
Adversarial Networks ◽  
Node Attributes ◽  
Low Dimensional

Network embedding which is to learn a low dimensional representation of nodes in a network has been used in many network analysis tasks. Some network embedding methods, including those based on generative adversarial networks (GAN) (a promising deep learning technique), have been proposed recently. Existing GAN-based methods typically use GAN to learn a Gaussian distribution as a priori for network embedding. However, this strategy makes it difficult to distinguish the node representation from Gaussian distribution. Moreover, it does not make full use of the essential advantage of GAN (that is to adversarially learn the representation mechanism rather than the representation itself), leading to compromised performance of the method. To address this problem, we propose to use the adversarial idea on the representation mechanism, i.e. on the encoding mechanism under the framework of autoencoder. Specifically, we use the mutual information between node attributes and embedding as a reasonable alternative of this encoding mechanism (which is much easier to track). Additionally, we introduce another mapping mechanism (which is based on GAN) as a competitor into the adversarial learning system. A range of empirical results demonstrate the effectiveness of the proposed approach.

scholarly journals Deep attributed network embedding by mutual information maximization

2021 ◽  
Vol 2132 (1) ◽  
pp. 012035
Author(s):  
Wujun Tao ◽  
Yu Ye ◽  
Bailin Feng
Keyword(s):  
Mutual Information ◽  
Network Embedding ◽  
Structure Information ◽  
Attributed Network ◽  
Information Maximization ◽  
Node Attributes ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Mutual Information Maximization ◽  
Node Embeddings

Abstract There is a growing body of literature that recognizes the importance of network embedding. It intends to encode the graph structure information into a low-dimensional vector for each node in the graph, which benefits the downstream tasks. Most of recent works focus on supervised learning. But they are usually not feasible in real-world datasets owing to the high cost to obtain labels. To address this issue, we design a new unsupervised attributed network embedding method, deep attributed network embedding by mutual information maximization (DMIM). Our method focuses on maximizing mutual information between the hidden representations of the global topological structure and the node attributes, which allows us to obtain the node embedding without manual labeling. To illustrate the effectiveness of our method, we carry out the node classification task using the learned node embeddings. Compared with the state-of-the-art unsupervised methods, our method achieves superior results on various datasets.

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