Network Embedding Research Articles

A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks

With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named SEG_SC. This algorithm introduces a new spectral clustering model that explores the underlying structure of graphs through sparse similarity graphs and deep graph representation learning, thereby enhancing graph clustering performance. Experimental analysis with multiple types of real datasets shows that the performance of this model surpasses several advanced benchmark algorithms and performs well in clustering on medium- to large-scale information networks.

A dynamic AI-based algorithm selection for Virtual Network Embedding

A dynamic AI-based algorithm selection for Virtual Network Embedding

High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graph

SummaryIn person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets.

Empirical Analysis of the Effects of International Labor Migration in EU-27 Countries

Abstract This research aims to provide a critical and comparative analysis of the effects induced by international labor migration in Europe, focusing on specific features and key factors that influence and shape the migration process in the context of the COVID-19 pandemic and geopolitical uncertainty. For conducting a comprehensive study on international labor migration among the member states of the European Union (EU-27), a cross-sectional database of specific migration, labor market, and economic welfare indicators was compiled at the level of 2021. These indicators were subjected to advanced econometric modeling embedding spatial and network analyses processed with the Stata 16 package and the JASP platform to obtain robust and valid results. Main findings of this study provide valuable insights into the evolution and impact of international labor migration, particularly in Romania and the European Union at large. This study serves as a benchmark for future migration strategies by exemplifying the specific characteristics of employees and jobs that will be required in the coming years, while also focusing on identifying the key skills and requirements of both native and migrant employees. Future trends are being analyzed by identifying the characteristics of the modern employee and the professions that will dominate the labor market in the coming years. This will help policymakers appraise migrants’ ability to secure employment in an ever-changing labor market.

Multi-View Attributed Network Embedding Using Manifold Regularization Preserving Non-Negative Matrix Factorization

Multi-View Attributed Network Embedding Using Manifold Regularization Preserving Non-Negative Matrix Factorization

Identifying Rarely Mutated Cancer Genes by Heterogeneous Network Embedding

Identifying Rarely Mutated Cancer Genes by Heterogeneous Network Embedding

Detecting local perturbations of networks in a latent hyperbolic embedding space.

This paper introduces two novel scores for detecting local perturbations in networks. For this, we consider a non-Euclidean representation of networks, namely, their embedding onto the Poincaré disk model of hyperbolic geometry. We numerically evaluate the performances of these scores for the detection and localization of perturbations on homogeneous and heterogeneous network models. To illustrate our approach, we study latent geometric representations of real brain networks to identify and quantify the impact of epilepsy surgery on brain regions. Results suggest that our approach can provide a powerful tool for representing and analyzing changes in brain networks following surgical intervention, marking the first application of geometric network embedding in epilepsy research.

Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis.

Deep learning-based diagnosis is becoming an indispensable part of modern healthcare. For high-performance diagnosis, the optimal design of deep neural networks (DNNs) is a prerequisite. Despite its success in image analysis, existing supervised DNNs based on convolutional layers often suffer from their rudimentary feature exploration ability caused by the limited receptive field and biased feature extraction of conventional convolutional neural networks (CNNs), which compromises the network performance. Here, we propose a novel feature exploration network named manifold embedded multilayer perceptron (MLP) mixer (ME-Mixer), which utilizes both supervised and unsupervised features for disease diagnosis. In the proposed approach, a manifold embedding network is employed to extract class-discriminative features; then, two MLP-Mixer-based feature projectors are adopted to encode the extracted features with the global reception field. Our ME-Mixer network is quite general and can be added as a plugin to any existing CNN. Comprehensive evaluations on two medical datasets are performed. The results demonstrate that their approach greatly enhances the classification accuracy in comparison with different configurations of DNNs with acceptable computational complexity.

Firm profiling and competition assessment: A design science approach

Extensive efforts have been made by both academics and practitioners to understand the inter-firm competitive relationship owing to its profound impacts on multiple key business goals, e.g., company benchmarking, marketing strategy planning, and talent acquisition. However, it has never been an easy task to fully characterize firms and assess the competitive relationship among them, mainly due to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and inter-firm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees’ occupation details and educational attainments. Then we adopt a Metapath2vec-based heterogeneous network embedding model to learn firms latent profiles (embeddings). Using the firm features and embeddings as input, we train multiple supervised classifiers to assess the competitive relationship among the firms. Following the principles of design as a search process, we demonstrate the effectiveness of our IT artifact through extensive experimental studies and detailed discussions. Our research has also discovered that the occupation and education specifics of employees are key factors in identifying potential competitors of a focal firm.

Exploring the effectiveness of service migration strategies for virtual network embedding

Network slicing, a key component of post-5G networks, has brought virtual network embedding (VNE) to the forefront of networking research. However, existing VNE approaches are limited by their consideration of static network topologies, failing to account for the dynamic nature of real-world networks. This limitation becomes particularly problematic in the face of link failures or topology modifications, rendering these approaches ineffective. To address this, we propose a new service placement strategy that remains effective even when the network topology changes. Furthermore, we introduce several service migration strategies and thoroughly investigate their effectiveness. Our results demonstrate the adaptability of our proposed strategy, which leverages graph neural networks, in handling link failures without necessitating relearning. This adaptability underlines the potential of our approach to significantly enhance the robustness and flexibility of service migration in VNE, thereby contributing to the evolution of network slicing in post-5G networks.

Reliable multiplex semi-local random walk based on influential nodes to improve link prediction in complex networks

In recent years, the exponential growth of online social networks as complex networks has presented challenges in expanding networks and forging new connections. Link prediction emerges as a crucial technique to anticipate future relationships among users, leveraging the current network state to address this challenge effectively. While link prediction models on monoplex networks have a well-established history, the exploration of similar tasks on multilayer networks has garnered considerable attention. Extracting topological and multimodal features for weighting links can improve link prediction in weighted complex networks. Meanwhile, establishing reliable and trustworthy paths between users is a useful way to create metrics that convert unweighted to weighted similarity. The local random walk is a widely used technique for predicting links in weighted monoplex networks. The aim of this paper is to develop a semi-local random walk over reliable paths to improve link prediction on a multilayer social network as a complex network, which is denoted as Reliable Multiplex semi-Local Random Walk (RMLRW). RMLRW leverages the semi-local random walk technique over reliable paths, integrating intra-layer and inter-layer information from multiplex features to conduct a trustworthy biased random walk for predicting new links within a target layer of multilayer networks. In order to make RMLRW scalable, we develop a semi-local random walk-based network embedding to represent the network in a lower-dimensional space while preserving its original characteristics. Extensive experimental studies on several real-world multilayer networks demonstrate the performance assurance of RMLRW compared to equivalent methods. Specifically, RMLRW improves the average f-measure of the link prediction by 3.2% and 2.5% compared to SEM-Path and MLRW, respectively.

Fusion of single-domain contrastive embedding and cross-domain graph collaborative filtering network for recommendation systems

Fusion of single-domain contrastive embedding and cross-domain graph collaborative filtering network for recommendation systems

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.

Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. We propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species. Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

CurveMEF: Multi-exposure fusion via curve embedding network

Multi-exposure image fusion (MEF) aims to combine multiple images with different exposures into a single image to improve visual quality and preserve details. This paper proposes a curve embedding network for MEF (CurveMEF), which formulates the MEF task as estimating optimal fusion curves based on state feedback using pixel intensities as state variables. The fusion curve is embedded into CurveNet, a lightweight deep network, as a physical prior constraint. Leveraging the proposed physical informed MEF method, the fusion curve can adaptively adjust the pixel distribution of overexposed and underexposed regions based on the pixel intensities, and distinguish the importance of source images. CurveMEF supports both RGB and luminance channel inputs, demonstrating its flexibility. Experimental results show that CurveMEF achieves competitive performance compared to state-of-the-art methods in both qualitative and quantitative analysis. Moreover, CurveNet requires significantly fewer parameters and training data, enabling fast training and inference. The proposed method delivers high-speed and high-quality fusion while significantly reducing computational, providing an efficient and cost-effective solution. The code is publicly available at: https://github.com/PiratePai/CurveMEF.

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Energy-Efficient Virtual Network Embedding: A Deep Reinforcement Learning Approach Based on Graph Convolutional Networks

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms.

Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

Protein features fusion using attributed network embedding for predicting protein-protein interaction

BackgroundProtein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations.MethodsWe introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs’ prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature.ResultsWhen employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively.ConclusionExperimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.

A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.

Drug repositioning based on tripartite cross-network embedding and graph convolutional network

Drug-disease association prediction is an important part of drug discovery, which can help researchers uncover potential drug candidates and disease targets more accurately to deal with the drawbacks of inefficiency and high cost of traditional methods. In contrast, past drug-disease prediction processes often overlooked the associations between drugs and side effects and the impact of proteins on diseases. These issues hindered models from fully learning the potential information within the network. Hence, a method capable of comprehensively learning from multi-source heterogeneous information and extracting high-level features of biological associations is needed. To overcome these challenges, proposes a drug repositioning method based on a tripartite cross-network embedding graph convolutional network, called TGCNDR, to predict the associations between drugs and diseases. Firstly, we construct a tripartite cross-network of drug-disease associations, drug-protein associations, and drug-side effect associations. TGCNDR employs graph convolutional networks to capture information from different nodes to learn drug embedding. Subsequently, TGCNDR uses anchor links for knowledge transfer, and different networks use the Self-attention mechanism to represent drug importance. Experiments on benchmark datasets demonstrate that TGCNDR exhibits good performance (the average AUROC is 0.922, the average AUPR is 0.916, and the average F1-score is 0.860) compared with state-of-the-art methods. In the case study, we perform molecular docking experiments on the predicted drug candidates and identify newly approved drugs for osteosarcoma, which suggests that TGCNDR is an effective tool for drug repositioning.