scholarly journals Scalable Multiplex Network Embedding

2018 ◽  
Author(s):  
Hongming Zhang ◽  
Liwei Qiu ◽  
Lingling Yi ◽  
Yangqiu Song
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
High Dimensional ◽  
Network Embedding ◽  
Current State ◽  
Multiplex Network ◽  
Different Types ◽  
Comparable Performance ◽  
Combine Information ◽  
Real World Problems

Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

A Survey of Various Encoding Schemes and Associated Placement Algorithms Applied to Packing and Layout Problems

2006 ◽  
Author(s):  
Santosh Tiwari ◽  
Georges Fadel ◽  
Vladimir Gantovnik
Keyword(s):  
State Of The Art ◽  
Qualitative Comparison ◽  
Encoding Scheme ◽  
Packing Problems ◽  
Current State ◽  
Different Types ◽  
Placement Algorithms

In this paper, we investigate the current state-of-the-art in packing algorithms. The focus of this survey is on the different types of encoding schemes and associated placement techniques used to represent the layout of a set of objects. The encoding schemes are investigated with respect to their suitability to different types of packing problems, specific scenarios where a given representation may outperform others and their limitations. The different types of placement algorithms that can be used with a given encoding are described. Some common desirable characteristics that an encoding scheme should follow are also discussed. Finally a qualitative comparison of the various encoding schemes is presented to help in selecting a specific representation based on a set of criteria.

scholarly journals Path-based extensions of local link prediction methods for complex networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Furqan Aziz ◽  
Haji Gul ◽  
Irfan Uddin ◽  
Georgios V. Gkoutos
Keyword(s):  
Complex System ◽  
Link Prediction ◽  
Network Science ◽  
State Of The Art ◽  
Superior Performance ◽  
Prediction Methods ◽  
Local Similarity ◽  
Fundamental Interest ◽  
Current State ◽  
Similarity Indices

AbstractLink prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.

scholarly journals Layer Information Similarity Concerned Network Embedding

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruili Lu ◽  
Pengfei Jiao ◽  
Yinghui Wang ◽  
Huaming Wu ◽  
Xue Chen
Keyword(s):  
Structural Information ◽  
Single Layer ◽  
Network Embedding ◽  
Network Layers ◽  
Multiplex Network ◽  
Proposed Model ◽  
Latent Space ◽  
Comparable Performance ◽  
The Common ◽  
Node Embeddings

Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while ignoring the similarity between different layers. In this paper, for multiplex network embedding, we propose a Layer Information Similarity Concerned Network Embedding (LISCNE) model considering the similarities between layers. Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors learn semantics for each layer. We get the node embeddings in each layer by concatenating the common vectors and layer vectors with the consideration that the node embedding is related not only to the surrounding neighbors but also to the overall semantics. Furthermore, we define an index to formalize the similarity between different layers and the cross-network association. Constrained by layer similarity, the layer vectors with greater similarity are closer to each other and the aligned node embedding in these layers is also closer. To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance compared to existing baseline methods.

scholarly journals Multiplex network embedding for implicit sentiment analysis

2021 ◽  
Author(s):  
Xiaofang Zhao ◽  
Yuhong Liu ◽  
Zhigang Jin
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Link Prediction ◽  
Network Embedding ◽  
Network Information ◽  
Information Embedding ◽  
Multiplex Network ◽  
Highly Nonlinear ◽  
Level Information ◽  
Relationship Network

AbstractAs one of the hot research directions in natural language processing, sentiment analysis has received continuous and extensive attention. Different from explicit sentiment words indicating sentiment polarity, implicit sentiment analysis is a more challenging problem due to the lack of sentiment words, which makes it inadequate to use traditional sentiment analysis method to judge the polarity of implicit sentiment. This paper takes sentiment analysis as a special sign link prediction problem, which is different from traditional text-based method. In particular, by performing the word graph-based text level information embedding and heterogeneous social network information embedding (i.e. user social relationship network embedding, and user-entity sentiment network embedding), the proposed scheme learns the highly nonlinear representations of network nodes, explores early fusion method to combine the strength of these two types of embedding modeling, optimizes all parameters simultaneously and creates enhanced context representations, leading to better capture of implicit sentiment polarity. The proposed method has been examined on real-world dataset, for implicit sentiment link prediction task. The experimental results demonstrate that the proposed method outperforms state-of-the-art schemes, including LINE, node2vec, and SDNE, by 20.2%, 19.8%, and 14.0%, respectively, on accuracy, and achieves at least 14% gains on AUROC. For sentiment analysis accuracy, the proposed method achieves AUROC of 80.6% and accuracy of 78.3%, which is at least 31% better than other models. This work can provide useful guidance on the implicit sentiment analysis.

scholarly journals Liquid fuel cells

2014 ◽  
Vol 5 ◽  
pp. 1399-1418 ◽  
Author(s):  
Grigorii L Soloveichik
Keyword(s):  
Fuel Cells ◽  
Energy Density ◽  
Liquid Fuel ◽  
State Of The Art ◽  
Practical Implementation ◽  
Current State ◽  
Different Types ◽  
Organic Fuels ◽  
Recent Trends ◽  
Oxygen Fuel

The advantages of liquid fuel cells (LFCs) over conventional hydrogen–oxygen fuel cells include a higher theoretical energy density and efficiency, a more convenient handling of the streams, and enhanced safety. This review focuses on the use of different types of organic fuels as an anode material for LFCs. An overview of the current state of the art and recent trends in the development of LFC and the challenges of their practical implementation are presented.

scholarly journals Anchor Link Prediction across Attributed Networks via Network Embedding

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 254 ◽  
Author(s):  
Shaokai Wang ◽  
Xutao Li ◽  
Yunming Ye ◽  
Shanshan Feng ◽  
Raymond Lau ◽  
...  
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
State Of The Art ◽  
User Profiling ◽  
Data Sets ◽  
Network Embedding ◽  
Real World Data ◽  
Intrinsic Structure ◽  
Multiple Networks ◽  
Proposed Model

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.

Hetero-association models of non-covalent molecular complexation

2019 ◽  
Vol 21 (15) ◽  
pp. 7717-7731 ◽  
Author(s):  
Anatoly S. Buchelnikov ◽  
Vladislav P. Evstigneev ◽  
Maxim P. Evstigneev
Keyword(s):  
State Of The Art ◽  
Supramolecular Structures ◽  
Present Review ◽  
Association Models ◽  
Molecular Complexation ◽  
Current State ◽  
Building Models ◽  
Different Types

The present review discusses the current state-of-the-art in building models enabling the description of non-covalent equilibrium complexation of different types of molecules in solution, which results in the formation of supramolecular structures different in length and composition (hetero-association or supramolecular multicomponent co-polymerisation).

scholarly journals One-Class Adversarial Nets for Fraud Detection

2019 ◽  
Vol 33 ◽  
pp. 1286-1293 ◽  
Author(s):  
Panpan Zheng ◽  
Shuhan Yuan ◽  
Xintao Wu ◽  
Jun Li ◽  
Aidong Lu
Keyword(s):  
Online Social Networks ◽  
State Of The Art ◽  
Fraud Detection ◽  
Knowledge Bases ◽  
Training Data ◽  
Training Dataset ◽  
Training Phase ◽  
Classification Models ◽  
Different Types ◽  
Comparable Performance

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection with only benign users as training data. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator of a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art oneclass classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.

scholarly journals Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network

2020 ◽  
Vol 34 (01) ◽  
pp. 147-155
Author(s):  
Xiaoxue Li ◽  
Yanmin Shang ◽  
Yanan Cao ◽  
Yangxi Li ◽  
Jianlong Tan ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Link Prediction ◽  
State Of The Art ◽  
Global Perspective ◽  
Main Trend ◽  
Network Embedding ◽  
Factors Affecting ◽  
Network Applications ◽  
Optimization Framework ◽  
Type Information

Anchor Link Prediction (ALP) across heterogeneous networks plays a pivotal role in inter-network applications. The difficulty of anchor link prediction in heterogeneous networks lies in how to consider the factors affecting nodes alignment comprehensively. In recent years, predicting anchor links based on network embedding has become the main trend. For heterogeneous networks, previous anchor link prediction methods first integrate various types of nodes associated with a user node to obtain a fusion embedding vector from global perspective, and then predict anchor links based on the similarity between fusion vectors corresponding with different user nodes. However, the fusion vector ignores effects of the local type information on user nodes alignment. To address the challenge, we propose a novel type-aware anchor link prediction across heterogeneous networks (TALP), which models the effect of type information and fusion information on user nodes alignment from local and global perspective simultaneously. TALP can solve the network embedding and type-aware alignment under a unified optimization framework based on a two-layer graph attention architecture. Through extensive experiments on real heterogeneous network datasets, we demonstrate that TALP significantly outperforms the state-of-the-art methods.

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