Network Embedding Methods Research Articles

Graph-Root: Prediction of Root-Associated Proteins in Maize, Sorghum, And Soybean Based on Graph Convolutional Network and Network Embedding Method

Background: The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such a system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. Objective: The purpose of this study was to design an efficient computational method to predict root-associated proteins in three plants: maize, sorghum, and soybean. Method: In this study, we proposed a machine learning based model, named Graph-Root, for the identification of root-related proteins in maize, sorghum, and soybean. The features derived from protein sequences, functional domains, and one network were extracted, where the first type of features were processed by graph convolutional neural network and multi-head attention, the second type of features reflected the essential functions of proteins, and the third type of features abstracted the linkage between proteins. These features were fed into the fully connected layer to make predictions. Results: The 5-fold cross-validation and independent tests suggested its acceptable performance. It also outperformed the only previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated. Conclusion: Graph-Root had a good performance and can be a useful tool to identify novel rootrelated proteins. BLOSUM62 features were found to be important in determining root-related proteins.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Neighbor-Enhanced Representation Learning for Link Prediction in Dynamic Heterogeneous Attributed Networks

Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable effectiveness in various prediction tasks. However, most network representation learning algorithms are based on homogeneous networks and static networks for link prediction that do not consider rich semantic and dynamic information. Additionally, existing dynamic network representation learning methods neglect the neighborhood interaction structure of the node. In this work, we design a neighbor-enhanced dynamic heterogeneous attributed network embedding method (NeiDyHNE) for link prediction. In light of the impressive achievements of the heuristic methods, we learn the information of common neighbors and neighbors’ interaction in heterogeneous networks to preserve the neighbors proximity and common neighbors proximity. NeiDyHNE encodes the attributes and neighborhood structure of nodes as well as the evolutionary features of the dynamic network. More specifically, NeiDyHNE consists of the hierarchical structure attention module and the convolutional temporal attention module. The hierarchical structure attention module captures the rich features and semantic structure of nodes. The convolutional temporal attention module captures the evolutionary features of the network over time in dynamic heterogeneous networks. We evaluate our method and various baseline methods on the dynamic link prediction task. Experimental results demonstrate that our method is superior to baseline methods in terms of accuracy.

Particle Swarm Optimization for 5g and Beyond

Network embedding assigns nodes in a network to low dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses has been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. Particle Swarm Optimization (PSO) algorithm is used as the proposed method. We discuss the classical graph embedding algorithms on cognitive radio environment and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of networks embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and software, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions. Key Word: Particle Swarm Optimization (PSO), Beyond 5G (B5G), Internet of Think (IOT), Machine Learning (ML)etc..

Dynamic Co-Embedding Model for Temporal Attributed Networks.

In this article, we study the problem of embedding temporal attributed networks, with the goal of which is to learn dynamic low-dimensional representations over time for temporal attributed networks. Existing temporal network embedding methods only learn the representations for nodes, which are unable to capture the dynamic affinities between nodes and attributes. Moreover, existing co-embedding methods that learn the static embeddings of both nodes and attributes cannot be naturally utilized to obtain their dynamic embeddings for temporal attributed networks. To address these issues, we propose the dynamic co-embedding model for temporal attributed networks (DCTANs) based on the dynamic stochastic state-space framework. Our model captures the dynamics of a temporal attributed network by modeling the abstract belief states representing the condition of the nodes and attributes of current time step, and predicting the transitions between temporal abstract states of two successive time steps. Our model is able to learn embeddings for both nodes and attributes based on their belief states at each time step of the temporal attributed network, while the state transition tendency for predicting the future network can be tracked and the affinities between nodes and attributes can be preserved. Experimental results on real-world networks demonstrate that our model achieves substantial performance gains in several static and dynamic graph mining applications compared with the state-of-the-art static and dynamic models.

Multidimensional political polarization in online social networks

Political polarization in online social platforms is a rapidly growing phenomenon worldwide. Despite their relevance to modern-day politics, the structure and dynamics of polarized states in digital spaces are still poorly understood. We analyze the community structure of a two-layer, interconnected network of French Twitter users, where one layer contains members of Parliament and the other one regular users. We obtain an optimal representation of the network in a four-dimensional political opinion space by combining network embedding methods and political survey data. We find structurally cohesive groups sharing common political attitudes and relate them to the political party landscape in France. The distribution of opinions of professional politicians is narrower than that of regular users, indicating the presence of more extreme attitudes in the general population. We find that politically extreme communities interact less with other groups as compared to more centrist groups. We apply an empirically tested social influence model to the two-layer network to pinpoint interaction mechanisms that can describe the political polarization seen in data, particularly for centrist groups. Our results shed light on the social behaviors that drive digital platforms towards polarization and uncover an informative multidimensional space to assess political attitudes online. Published by the American Physical Society 2024

Sentiment Analysis Based on Heterogeneous Multi-Relation Signed Network

Existing sentiment prediction methods often only classify users’ emotions into a few categories and cannot predict the variation of emotions under different topics. Meanwhile, network embedding methods that consider structural information often assume that links represent positive relationships, ignoring the possibility of negative relationships. To address these challenges, we present an innovative approach in sentiment analysis, focusing on the construction of a denser heterogeneous signed information network from sparse heterogeneous data. We explore the extraction of latent relationships between similar node types, integrating emotional reversal and meta-path similarity for relationship prediction. Our approach uniquely handles user-entity and topic-entity relationships, offering a tailored methodology for diverse entity types within heterogeneous networks. We contribute to a deeper understanding of emotional expressions and interactions in social networks, enhancing sentiment analysis techniques. Experimental results on four publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches.

Embedding model of multilayer networks structure and its application to identify influential nodes

At present, there are many indexes used to quantify the structure of single layer complex networks, but multilayer networks are affected by the number of layers, which are difficult to represent the structure and complicated to calculate. In this paper, we propose a network embedding method to represent the structure information of multilayer networks, which reduces the computational complexity. After the structure of the multilayer networks is expressed, the key nodes in the network are mined to help control the spread of the epidemic, suppress the spread of rumors, and optimize the structure of the network. However, it is difficult to quantify this problem precisely because of the difference of judgment indexes. On the basis of representing the structure of multilayer networks, we propose a fuzzy Tsallis eXtropy (FTE) method for quantifying influential nodes in multilayer networks by combining fuzzy theory and entropy. In addition, FTE is tested by some practical networks and compared with other methods. The results reveal that the proposed method is effective.

SHEEP, a Signed Hamiltonian Eigenvector Embedding for Proximity

Signed network embedding methods allow for a low-dimensional representation of nodes and primarily focus on partitioning the graph into clusters, hence losing information on continuous node attributes. Here, we introduce a spectral embedding algorithm for understanding proximal relationships between nodes in signed graphs, where edges can take either positive or negative weights. Inspired by a physical model, we construct our embedding as the minimum energy configuration of a Hamiltonian dependent on the distance between nodes and locate the optimal embedding dimension. We show through a series of experiments on synthetic and empirical networks, that our method (SHEEP) can recover continuous node attributes showcasing its main advantages: re-configurability into a computationally efficient eigenvector problem, retrieval of ground state energy which can be used as a statistical test for the presence of strong balance, and measure of node extremism, computed as the distance to the origin in the optimal embedding.

Learning dynamic embeddings for temporal attributed networks

Learning low-dimensional representations for networks has attracted considerable research interest due to the ever-increasing volume and variety of network-based information. It becomes especially important and challenging for temporal networks whose nodes and attributes evolve as time changes. To address the problem of temporal network embedding, we propose a novel Dynamic Bayesian Temporal Network Embedding (DBTNE) method that adopts a probabilistic framework and represents network embeddings as Gaussian distributions for learning effective embeddings for temporal networks. Unlike the existing temporal network embedding methods that learn dynamic embeddings only for nodes, our DBTNE jointly learns the dynamic embeddings of both nodes and attributes in temporal attributed networks for effectively capturing the similarities among them, which greatly facilitates attribute-related learning tasks such as link prediction, object classification and user profiling. In addition, we model the temporal dynamics of both nodes and attributes with neural ordinary differential equations, which supports effective and efficient capture of long-range dependencies among embeddings at different timestamps. Extensive experimental results on real-world temporal networks demonstrate that our DBTNE achieves better performance than popular state-of-the-art methods, including DeepWalk, Node2Vec, CAN, HTNE, M2DNE, MTSN, Dane-ATT, for typical graph learning tasks, such as dynamic link prediction and node classification.

Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks

Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node representations to characterize the similarity between nodes. Traditional network embedding methods focus on single-layer networks, while in reality, a large part of complex networks are not isolated, but interdependent and interrelated, forming multiplex complex networks. Also, how to effectively exploit layer correlations in multiplex networks to learn more robust and valuable representations, to improve link prediction performance, has been a hot research topic in the field of complex network analysis. However, previous studies mainly focus on inferring intralinks in each layer of complex networks or anchor links among layers. Another issue that has not been discussed is how to predict potential links or reconstruct the network in unobserved relations based on existing multiplex networks. To this issue, we define a novel inductive link prediction problem in multiplex networks, in which most existing multichannel network embedding methods fail to solve. This is either because they only emphasize the specific structure information of an individual layer or only capture the common information for all layers. To effectively address this problem, we propose a novel embedding method termed interactive learning across relations (ILAR), to capture and fully exploit the multiple relations and complex layer correlations in multiplex networks. We leverage two convolutional modules and ILAR to capture the sufficient complementary and correlations in multiplex networks. Moreover, during interactive learning, a disparity constraint is introduced, which enforces the features encoded from two convolutional modules to be different and prevents information redundancy. Finally, the extensive experiments in several real-world datasets show that our model can significantly outperform the existing state-of-the-art network embedding methods on the novel link prediction problem in multiplex networks.

L-BGNN: Layerwise Trained Bipartite Graph Neural Networks.

Learning low-dimensional representations of bipartite graphs enables e-commerce applications, such as recommendation, classification, and link prediction. A layerwise-trained bipartite graph neural network (L-BGNN) embedding method, which is unsupervised, efficient, and scalable, is proposed in this work. To aggregate the information across and within two partitions of a bipartite graph, a customized interdomain message passing (IDMP) operation and an intradomain alignment (IDA) operation are adopted by the proposed L-BGNN method. Furthermore, we develop a layerwise training algorithm for L-BGNN to capture the multihop relationship of large bipartite networks and improve training efficiency. We conduct extensive experiments on several datasets and downstream tasks of various scales to demonstrate the effectiveness and efficiency of the L-BGNN method as compared with state-of-the-art methods. Our codes are publicly available at https://github.com/TianXieUSC/L-BGNN.

Temporal Heterogeneous Information Network Embedding via Semantic Evolution

Real-world networks are often heterogeneous and constantly changing over time. Evolution reveals the trend of network development, which is vital for predicting its future state, and network embedding can effectively learn the information from it. Nevertheless, previous works only consider the impact of meta-path instances or node neighbors on the network dynamics but ignore the relationship between them, and hence the hidden semantic information is missed, which will result in performance deterioration. Therefore, we propose a novel temporal heterogeneous information network embedding method (SemE), which abstracts the instance of the meta-path as semantic units and then considers the interaction between them to discover deeper semantic information. Specifically, we first construct semantic networks by the Ethernet topology and the interaction between semantic units. The semantic units are sampled based on a pre-designed meta-path-guided random walk. To further capture the semantic evolution of the semantic network, we learn the embedding of nodes by the attention-Hawkes process. Finally, we generate the final embedding by aggregating the structure, semantic and temporal information with the attention mechanism. Experiments on three real-world temporal heterogeneous information networks show that our model performs better than competitive counterparts.

DyLFG: A Dynamic Network Learning Framework Based on Geometry.

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks. (2) At present, most network-embedding methods, especially dynamic network representation learning approaches, use Euclidean embedding space. However, the network itself is geometrically non-Euclidean, which leads to geometric inconsistencies between the embedded space and the underlying space of the network, which can affect the performance of the model. In order to solve the above two problems, we propose a geometry-based dynamic network learning framework, namely DyLFG. Our proposed framework targets dynamic networks, which allow nodes and edges to join or exit the network over time. In order to extract the structural information of network snapshots, we designed a new hyperbolic geometry processing layer, which is different from the previous literature. In order to deal with the temporal dynamics of the network snapshot sequence, we propose a gated recurrent unit (GRU) module based on Ricci curvature, that is the RGRU. In the proposed framework, we used a temporal attention layer and the RGRU to evolve the neural network weight matrix to capture temporal dynamics in the network snapshot sequence. The experimental results showed that our model outperformed the baseline approaches on the baseline datasets.

Virtual network embedding method based on node delay perception

AbstractWireless virtual network uses software defined network and network function virtualisation technologies to create multiple logically isolated virtual networks on a physical wireless network. Wireless virtual network can improve the utilisation of wireless resources to meet the requirements of different services. Delay is an important performance indicator, which has strict requirements for delay‐sensitive services such as video conferencing and online games. In this study, a virtual network embedding method based on node delay perception (VNE‐NDP) is proposed, which considers both the node and link resources as well as the embedding delay requirements of virtual networks. The virtual network embedding method based on node delay perception consists of two phases: virtual node embedding and virtual link embedding. In the virtual node embedding phase, a physical node sorting method based on node delay perception (PNS‐NDP) is proposed. In PNS‐NDP, the node deployment delay is introduced into the node sorting algorithm for the first time. The authors select candidate physical nodes for each virtual node according to their resource availability and delay performance, which can greatly reduce the VN embedding delay without sacrificing too much other performance. In the virtual link embedding phase, a shortest path algorithm with bandwidth and link deployment delay constraints to find feasible physical paths for each virtual link is used. In addition, the VN embedding (VNE) deployment time is set as a new evaluation index. Simulation results show that compared with other VNE methods, VNE‐NDP can achieve higher success rate, revenue‐to‐expenditure ratio, and lower deployment delay.

Attribute network joint embedding based on global attention

The attribute network not only has a complex topology, but its nodes also contain rich attribute information. Attribute network embedding methods extract both network topology and node attribute information to learn low-dimensional embedding of large attribute networks, which are of great importance for network analysis. In this paper, we propose attribute network joint embedding based on global attention (GAJE). First, GAJE uses the structure information of the attribute network to obtain the structure embedding vectors of nodes. Second, we propose a global attention method to obtain the attribute embedding vectors of nodes. This method captures the relationships of different attributes within and between nodes through convolutional neural networks and attention mechanisms, respectively. Finally, GAJE concatenates the structure embedding vectors and the attribute embedding vectors to obtain the final joint embedding vectors which simultaneously reflects the network structure and attributes information. For link prediction and node classification, we compared GAJE with nine well-known network embedding methods in four real datasets. The experimental results show that the algorithm has good attribute network embedding effects. Our tensorflow implementation of the GAJE is available at https://github.com/Andrewsama/GAJE-master.

SketchNE: Embedding Billion-Scale Networks Accurately in One Hour

We study large-scale network embedding with the goal of generating high-quality embeddings for networks with more than 1 billion vertices and 100 billion edges. Recent attempts LightNE and NetSMF propose to sparsify and factorize the (dense) NetMF matrix for embedding large networks, where NetMF is a theoretically-grounded network embedding method. However, there is a trade-off between their embeddings' quality and scalability due to their expensive memory requirements, making embeddings less effective under real-world memory constraints. Therefore, we present the SketchNE model, a scalable, effective, and memory-efficient network embedding solution developed for a single machine with CPU only. The main idea of SketchNE is to avoid the explicit construction and factorization of the NetMF matrix either sparsely or densely when producing the embeddings through the proposed sparse-sign randomized single-pass SVD algorithm. We conduct extensive experiments on nine datasets of various sizes for vertex classification and link prediction, demonstrating the consistent outperformance of SketchNE over state-of-the-art baselines in terms of both effectiveness and efficiency. SketchNE costs only <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.0 hours</b> to embed the Hyperlink2012 network with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3.5 billion</b> vertices and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">225 billion</b> edges on a CPU-only single machine with embedding superiority (e.g., a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">282%</b> relative HITS@10 gain over LightNE).

Estimating the relative importance of nodes in complex networks based on network embedding and gravity model

Identifying nodes that hold relative importance in complex networks is a burgeoning research area. Previous studies have demonstrated the effectiveness of the gravity model in capturing interactions among nodes in high-dimensional network space. Traditional algorithms predominantly concentrate on structural characteristics and node random walks in high-dimensional network space, overlooking substantial computational overhead and intricacies within the high-dimensional space. Simultaneously, the rapid evolution of the Internet has given rise to a series of emerging technologies, such as embedding models that can map high-dimensional network nodes to low-dimensional vector space. To address this issue, we introduce a novel algorithm for quantifying node relative importance, denoted as the Network Embedding and Gravity Model (NEGM). First, the network embedding method transforms nodes into low-dimensional, real-valued, dense vectors in Euclidean space. Then, drawing inspiration from Newton's law of universal gravitation, it proposes a novel gravity model. Finally, utilizing the novel gravity model, it calculates the aggregate attractive force of all nodes within the target node set. Experimental results show that NEGM excels in measuring the relative importance of nodes in various types of networks, demonstrating its significant potential for mining deep-seated information within authentic networks like protein networks and criminal networks.

A method framework for identifying digital resource clusters in software ecosystems

A popular form of modern software development involves co-creation and sharing of digital resources (e.g., API and SDK) by third-party developers in software ecosystems. In doing so, digital resources, software development projects, and developer organizations are networked together and form digital resource clusters (DRCs), which can be closely or loosely related. Management science and innovation research has focused on how organizations use DRCs to innovate their software products and services. However, the network structures of software ecosystems are spatiotemporally complex since digital resources are transitively, heterogeneously, and temporally related. In existing literature, the extent of spatiotemporal complexity has impeded the empirical identification of DRCs. Our research devises a method framework consisting of two steps toward identifying DRCs using machine learning, which we found to be well suited to representing the spatiotemporal characteristics of networked digital resources. First, we devise a spatiotemporal network embedding method that learns and represents temporal, transitive, and heterogeneous networks of software ecosystems. Second, we devise a clustering method that identifies DRCs using the output of our embedding method as input. The performance test experiment results show that our devised method framework is superior to existing conventional methods at identifying DRCs.

Protein Complex Identification Based on Heterogeneous Protein Information Network.

Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification.