Noise-Resilient Similarity Preserving Network Embedding for Social Networks

Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the structure and inherent properties of the network. Most existing network embedding methods didn't consider network noise. However, it is almost impossible to observe the actual structure of a real-world network without noise. The noise in the network will affect the performance of network embedding dramatically. In this paper, we aim to exploit node similarity to address the problem of social network embedding with noise and propose a node similarity preserving (NSP) embedding method. NSP exploits a comprehensive similarity index to quantify the authenticity of the observed network structure. Then we propose an algorithm to construct a correction matrix to reduce the influence of noise. Finally, an objective function for accurate network embedding is proposed and an efficient algorithm to solve the optimization problem is provided. Extensive experimental results on a variety of applications of real-world networks with noise show the superior performance of the proposed method over the state-of-the-art methods.

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Relation Structure-Aware Heterogeneous Information Network Embedding

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33014456 ◽

2019 ◽

Vol 33 ◽

pp. 4456-4463 ◽

Cited By ~ 8

Author(s):

Yuanfu Lu ◽

Chuan Shi ◽

Linmei Hu ◽

Zhiyuan Liu

Keyword(s):

Real World ◽

Dimensional Space ◽

Structural Characteristics ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Real World Datasets ◽

Low Dimensional ◽

Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

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Attributed Network Embedding with Micro-Meso Structure

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3441486 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-26

Author(s):

Juan-Hui Li ◽

Ling Huang ◽

Chang-Dong Wang ◽

Dong Huang ◽

Jian-Huang Lai ◽

...

Keyword(s):

Community Structure ◽

Optimization Problems ◽

Nonnegative Matrix ◽

Superior Performance ◽

Network Embedding ◽

Attributed Network ◽

Attribute Information ◽

Low Dimensional ◽

Embedding Methods ◽

Proximity Structure

Recently, network embedding has received a large amount of attention in network analysis. Although some network embedding methods have been developed from different perspectives, on one hand, most of the existing methods only focus on leveraging the plain network structure, ignoring the abundant attribute information of nodes. On the other hand, for some methods integrating the attribute information, only the lower-order proximities (e.g., microscopic proximity structure) are taken into account, which may suffer if there exists the sparsity issue and the attribute information is noisy. To overcome this problem, the attribute information and mesoscopic community structure are utilized. In this article, we propose a novel network embedding method termed Attributed Network Embedding with Micro-Meso structure, which is capable of preserving both the attribute information and the structural information including the microscopic proximity structure and mesoscopic community structure. In particular, both the microscopic proximity structure and node attributes are factorized by Nonnegative Matrix Factorization (NMF), from which the low-dimensional node representations can be obtained. For the mesoscopic community structure, a community membership strength matrix is inferred by a generative model (i.e., BigCLAM) or modularity from the linkage structure, which is then factorized by NMF to obtain the low-dimensional node representations. The three components are jointly correlated by the low-dimensional node representations, from which two objective functions (i.e., ANEM_B and ANEM_M) can be defined. Two efficient alternating optimization schemes are proposed to solve the optimization problems. Extensive experiments have been conducted to confirm the superior performance of the proposed models over the state-of-the-art network embedding methods.

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Principled approach to the selection of the embedding dimension of networks

Nature Communications ◽

10.1038/s41467-021-23795-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Weiwei Gu ◽

Aditya Tandon ◽

Yong-Yeol Ahn ◽

Filippo Radicchi

Keyword(s):

Real World ◽

Structural Information ◽

General Purpose ◽

Embedding Dimension ◽

Network Embedding ◽

Machine Learning Technique ◽

Learning Technique ◽

Low Dimensional ◽

Large Corpus ◽

Selection Of

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

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Community detection in complex network by network embedding and density clustering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202961 ◽

2021 ◽

pp. 1-12

Author(s):

JinFang Sheng ◽

Huaiyu Zuo ◽

Bin Wang ◽

Qiong Li

Keyword(s):

Complex Network ◽

Community Detection ◽

Dimensional Space ◽

Detection Algorithm ◽

Superior Performance ◽

Network Embedding ◽

Detection Algorithms ◽

Density Clustering ◽

Community Detection Algorithm ◽

Low Dimensional

In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

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Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding

Applied Sciences ◽

10.3390/app11052371 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2371

Author(s):

Junjian Zhan ◽

Feng Li ◽

Yang Wang ◽

Daoyu Lin ◽

Guangluan Xu

Keyword(s):

State Of The Art ◽

Global Information ◽

Network Embedding ◽

Sampling Process ◽

Attributed Network ◽

Benchmark Datasets ◽

Adversarial Training ◽

Low Dimensional ◽

Embedding Methods ◽

Local Proximity

As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.

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Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

10.1101/2020.11.14.382655 ◽

2020 ◽

Author(s):

Mustafa Coşkun ◽

Mehmet Koyutürk

Keyword(s):

Link Prediction ◽

Similarity Measures ◽

Graph Representation ◽

Supplementary Information ◽

Great Promise ◽

Network Embedding ◽

Common Neighbor ◽

Node Similarity ◽

Topological Characteristics ◽

Low Dimensional

AbstractMotivationLink prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if available– of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art.ResultsWe select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link predictionAvailabilityOur method is implemented as a python library and is available at [email protected] informationSupplementary data are available at Bioinformatics online.

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Learning Network Embedding with Community Structural Information

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/407 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yu Li ◽

Ying Wang ◽

Tingting Zhang ◽

Jiawei Zhang ◽

Yi Chang

Keyword(s):

Community Structure ◽

Link Prediction ◽

Structural Information ◽

Representation Learning ◽

Network Embedding ◽

Learning Network ◽

Optimization Framework ◽

Vertex Representation ◽

Low Dimensional ◽

Embedding Methods

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.

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Discrete Embedding for Latent Networks

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/170 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hong Yang ◽

Ling Chen ◽

Minglong Lei ◽

Lingfeng Niu ◽

Chuan Zhou ◽

...

Keyword(s):

Real World ◽

Representation Learning ◽

Mixed Integer ◽

Information Cascades ◽

Integer Optimization ◽

Network Embedding ◽

Structure Information ◽

Proximity Matrix ◽

Real World Datasets ◽

Embedding Methods

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

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Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder

Applied Sciences ◽

10.3390/app11062873 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2873

Author(s):

Dong Hwan Kim ◽

Woo Jin Ahn ◽

Myo Taeg Lim ◽

Tae Koo Kang ◽

Dong Won Kim

Keyword(s):

Real World ◽

Similarity Index ◽

Structural Similarity ◽

Superior Performance ◽

Structural Similarity Index ◽

Atmospheric Scattering ◽

Rain Removal ◽

Robust Network ◽

Computer Vision Applications ◽

Convolutional Encoder

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net’s capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.

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Multi-View Collaborative Network Embedding

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3441450 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-18

Author(s):

Sezin Kircali Ata ◽

Yuan Fang ◽

Min Wu ◽

Jiaqi Shi ◽

Chee Keong Kwoh ◽

...

Keyword(s):

Real World ◽

State Of The Art ◽

Second Order ◽

Collaborative Network ◽

Multiple Views ◽

Network Embedding ◽

Single View ◽

Video Sharing ◽

Low Dimensional

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

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