Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism

2020 ◽

Author(s):

Hong Huang ◽

Zixuan Fang ◽

Xiao Wang ◽

Youshan Miao ◽

Hai Jin

Keyword(s):

Link Prediction ◽

Temporal Dynamics ◽

Dimensional Space ◽

Temporal Network ◽

Hawkes Process ◽

Temporal Networks ◽

Network Embedding ◽

Different Types ◽

Low Dimensional ◽

Link Recommendation

Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation.

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W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

Intelligent Data Analysis ◽

10.3233/ida-205168 ◽

2021 ◽

Vol 25 (3) ◽

pp. 711-738

Author(s):

Phu Pham ◽

Phuc Do

Keyword(s):

Link Prediction ◽

Representation Learning ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Learning Framework ◽

Novel Approach ◽

Proposed Model ◽

Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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ALPINE: Active Link Prediction Using Network Embedding

Applied Sciences ◽

10.3390/app11115043 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5043

Author(s):

Xi Chen ◽

Bo Kang ◽

Jefrey Lijffijt ◽

Tijl De Bie

Keyword(s):

Active Learning ◽

Protein Interactions ◽

Link Prediction ◽

Prediction Accuracy ◽

Real Data ◽

Network Embedding ◽

Protein Protein Interactions ◽

Additional Information ◽

The Cost ◽

Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

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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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Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2021.3094893 ◽

2021 ◽

pp. 1-14

Author(s):

Pengfei Jiao ◽

Qiang Tian ◽

Wang Zhang ◽

Xuan Guo ◽

Di Jin ◽

...

Keyword(s):

Attention Mechanism ◽

Network Embedding

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Network embedding based link prediction in dynamic networks

Future Generation Computer Systems ◽

10.1016/j.future.2021.09.024 ◽

2021 ◽

Author(s):

Shashi Prakash Tripathi ◽

Rahul Kumar Yadav ◽

Abhay Kumar Rai

Keyword(s):

Link Prediction ◽

Dynamic Networks ◽

Network Embedding

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Triple-layer attention mechanism-based network embedding approach for anchor link identification across social networks

Neural Computing and Applications ◽

10.1007/s00521-021-06556-9 ◽

2021 ◽

Author(s):

Yao Li ◽

Huiyuan Cui ◽

Huilin Liu ◽

Xiaoou Li

Keyword(s):

Social Networks ◽

Attention Mechanism ◽

Network Embedding ◽

Triple Layer

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Semisupervised Community Preserving Network Embedding with Pairwise Constraints

Complexity ◽

10.1155/2020/7953758 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Dong Liu ◽

Yan Ru ◽

Qinpeng Li ◽

Shibin Wang ◽

Jianwei Niu

Keyword(s):

Community Structure ◽

Link Prediction ◽

Learning Algorithms ◽

Nonnegative Matrix ◽

Machine Learning Algorithms ◽

Network Visualization ◽

Network Embedding ◽

Pairwise Constraints ◽

Node Clustering ◽

Low Dimensional

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints (must-link and cannot-link) information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones.

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JANE: Jointly Adversarial Network Embedding

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

10.24963/ijcai.2020/192 ◽

2020 ◽

Author(s):

Liang Yang ◽

Yuexue Wang ◽

Junhua Gu ◽

Chuan Wang ◽

Xiaochun Cao ◽

...

Keyword(s):

Link Prediction ◽

Real Data ◽

Semantic Space ◽

Network Embedding ◽

Generative Adversarial Network ◽

Adversarial Learning ◽

Adversarial Network ◽

Node Clustering ◽

Topology Information ◽

Embedding Methods

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).

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