Contrastive Multi-View Multiplex Network Embedding with Applications to Robust Network Alignment

Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.

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MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

Scientific Reports ◽

10.1038/s41598-021-87987-1 ◽

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.

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Disentangled-based Adversarial Network for Multiplex Network Embedding

10.1109/ijcnn52387.2021.9534065 ◽

2021 ◽

Author(s):

Shimeng Zhan ◽

Nianwen Ning ◽

Kai Zhao ◽

Lianwei Li ◽

Bin Wu ◽

...

Keyword(s):

Network Embedding ◽

Adversarial Network ◽

Multiplex Network

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Layer Information Similarity Concerned Network Embedding

Complexity ◽

10.1155/2021/2260488 ◽

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.

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Multiplex network embedding for implicit sentiment analysis

Complex & Intelligent Systems ◽

10.1007/s40747-021-00504-9 ◽

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.

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Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding

10.1145/3459637.3482310 ◽

2021 ◽

Author(s):

Zhehan Liang ◽

Yu Rong ◽

Chenxin Li ◽

Yunlong Zhang ◽

Yue Huang ◽

...

Keyword(s):

Social Network ◽

Large Scale ◽

Network Alignment ◽

Network Embedding

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Attribute Network Alignment Based on Network Embedding

10.1145/3456172.3456217 ◽

2021 ◽

Author(s):

Fan Yang ◽

Wenxin Liang ◽

Linlin Zong

Keyword(s):

Network Alignment ◽

Network Embedding

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A network embedding framework based on integrating multiplex network for drug combination prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbab364 ◽

2021 ◽

Author(s):

Liang Yu ◽

Mingfei Xia ◽

Qi An

Keyword(s):

Drug Combination ◽

Characteristic Curve ◽

Drug Combinations ◽

Alternative Methods ◽

Network Embedding ◽

Disease Treatment ◽

Multiplex Network ◽

External Sources ◽

Precision Recall Curve ◽

Novel Drug

Abstract Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.

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