Nodes In Heterogeneous Network Research Articles

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Heterogeneous network representation learning based on role feature extraction

Since most of the real-world networks are heterogeneous, existing methods cannot characterize the roles of nodes in heterogeneous networks. The neighborhood structure of nodes in heterogeneous networks largely determines the node roles, and the basic statistical features of nodes describe the topology of nodes to some extent, so extracting structural features from the adjacency matrix of networks is crucial for role-oriented network representation learning(structural equivalence). Therefore, in this paper, we propose a heterogeneous network representation learning model based on role feature extraction, called HRFE(Heterogeneous Network Representation Learning for Role Feature Extraction). Firstly, we perform feature extraction for each node in the heterogeneous network to obtain a high-dimensional feature matrix, then perform role discovery using non-negative matrix decomposition techniques to obtain a role-based node representation, and finally verify the effectiveness of the model HRFE through experiments on a large number of real datasets.

Multi-Connectivity for 5G Networks and Beyond: A Survey.

To manage a growing number of users and an ever-increasing demand for bandwidth, future 5th Generation (5G) cellular networks will combine different radio access technologies (cellular, satellite, and WiFi, among others) and different types of equipment (pico-cells, femto-cells, small-cells, macro-cells, etc.). Multi-connectivity is an emerging paradigm aiming to leverage this heterogeneous architecture. To achieve this, multi-connectivity proposes to enable UE to simultaneously use component carriers from different and heterogeneous network nodes: base stations, WiFi access points, etc. This could offer many benefits in terms of quality of service, energy efficiency, fairness, mobility, and spectrum and interference management. Therefore, this survey aims to present an overview of multi-connectivity in 5G networks and beyond. To do so, a comprehensive review of existing standards and enabling technologies is proposed. Then, a taxonomy is defined to classify the different elements characterizing multi-connectivity in 5G and future networks. Thereafter, existing research works using multi-connectivity to improve the quality of service, energy efficiency, fairness, mobility management, and spectrum and interference management are analyzed and compared. In addition, lessons common to these different contexts are presented. Finally, open challenges for multi-connectivity in 5G networks and beyond are discussed.

A points of interest recommendation framework based on effective representation of heterogeneous nodes in the Internet of Things

Location-based social networks are a special kind of the Internet of Things in which users and points of interest (POIs) are heterogeneous network nodes. When matching POIs for users, traditional recommendation methods either map users to POI space or map POIs to user space. This direct mapping of the heterogeneous space introduces some noise to the node representation. The sparse interaction between users and POIs makes it difficult to effectively distinguish the representations of similar heterogeneous nodes. Based on the above problems, this paper proposes a recommendation framework that projects heterogeneous network nodes: users and POIs into a unified representation space. Obtain the matching results of nodes in the new problem space and effectively solve the problem of unified representation of heterogeneous nodes in the Internet of Things. The setting of the intermediate latent space can distinguish the representations of similar behavior nodes as much as possible and obtain more accurate recommendation results. We conduct extensive experiments on a four real-world datasets which show that framework has superior performance compared to the state-of-the-art framework for POI recommendation.

Game-Based Channel Selection for UAV Services in Mobile Edge Computing

Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).

Improvement of information technology on the basis of method of metric proximal gradient with introduction of diagonal step

The article presents some aspects of the use of optimization methods in telecommunications networks. The use of optimization methods by means of machine learning is especially important in order to avoid various emergency situations in networks. It is advisable to use machine learning methods to obtain information about signal quality, traffic, etc. At the same time it is possible to make forecasts of various malfunctions, routing, safety control. It is determined that the model of Markov random field is effective in modeling inhomogeneous networks. This approach allows modeling the exponential distribution of nodes in heterogeneous networks. A modification of the proximal gradient algorithm is presented — a method of variable metric proximal gradient. Ensuring fast convergence is achieved by means of diagonal step size, which is more efficient than scalar. The article reveals an adaptive rule for choosing a metric, a diagonal step based on the Barzilai-Borvain (BB) method. The presented algorithm combines two approaches: the standard proximal gradient method and the proximal Newton method. The establishment of clear rules for choosing the diagonal step size for convex optimization algorithms has been implemented. The proposed diagonal metric provides a better estimate of the ill-conditioned local Hessian compared to the standard Barzilai-Borwein BB scalar step, leading to faster convergence of the algorithm. Combined with non-monotonic linear search, the general algorithm is guaranteed to converge. Finally, for several machine learning programs with artificial and real datasets, empirical results demonstrate better convergence behavior for the proposed methodology. Implementation of the proposed metric proximal gradient technique in the management of a heterogeneous telecommunication network will ensure effective decentralized management of heterogeneous network resources and reduce the amount of service information in the network. This will avoid overloading the network in the event of emergency situations. However, the question of the load on the network equipment during its management in the presence of a large number of users still remains open.

Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.

The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug-disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively. We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.

MBRep: Motif-based representation learning in heterogeneous networks

In recent years, there has been a surge of interest in applying machine learning to graphs and networks that already exist in the world around us. The approach has been successfully used for domains as diverse as traffic management, e-commerce recommendation and public opinion monitoring. A critical aspect of representation learning for applied machine learning is feature engineering. Deep learning-based research in representation learning has developed methods for automatically learning a large number of potentially correlated features from original networks. However, most of these methods cannot be applied to heterogeneous networks, which are true expressions of the real-world. This is because they do not adequately capture the structure and semantics of different types of nodes in heterogeneous networks and the links between them. They also struggle to represent higher-order heterogeneous patterns of connection. This paper proposes a generalized motif-based higher-order representation learning method, MBRep, that learns triangle motif embedding in a network, on the basis of which it can obtain the embedding and representation of nodes in a heterogeneous network. Statistically, significant motif structures are extracted from the original heterogeneous network and combined with the heterogeneity of the nodes. A weight-biased random walk is then applied to the motif level higher-order network, using a SkipGram model to embed the motifs. After this, the embedding of the original network nodes is calculated using weighted averages and feature alignment. This can then be used for link prediction. We confirmed the effectiveness of MBRep by comparing its AUC and MRR performance with other state-of-the-art methods on three real-world datasets. Its adaptability was also validated by conducting a cold-start test.

Generalized [formula omitted]-cores of networks under attack with limited knowledge

Network theory has been used as an effective approach for understanding and controlling many real-world large-scale systems. A significant aspect of network operation is its robustness against failures and attacks. Here, we develop a theoretical framework for two classes of network attack with limited knowledge, namely, min−n and max−n attacks, where only n nodes are observed and a node with smallest or largest degree is removed at a time until a fraction 1−p of nodes are attacked. We study the effect of these attacks on the generalized k-core (Gk-core) of the network, which is obtained by implementing a k-leaf pruning process, removing progressively nodes with degree smaller than k alongside their nearest neighbors. This removal process can be viewed as a generation of the ordinary k-core decomposition. It is found that the G2-core undergoes a continuous phase transition with respect to p while Gk-core shows a first-order percolation transition for k≥3 under both types of attacks for all n. We reveal that knowing one more node during attacks, improving from n=1 to n=2, turns out to be most beneficial in terms of changing the robustness of Gk-core in both directions. Moreover, it is shown that degree heterogeneity plays a role in robustness as prioritizing attack on small-degree nodes in heterogeneous networks may help consolidate the Gk-core, but also in stability where hub nodes act like anchors stabilizing the Gk-core structure. Our results offer insight into the design of resilient complex systems and evaluation of network robustness and stability.

MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.

Predicting new drug-target interactions is an important step in new drug development, understanding of its side effects and drug repositioning. Heterogeneous data sources can provide comprehensive information and different perspectives for drug-target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new drug-target interactions (DTI). However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development. To solve this problem, we proposed a multi-modal DTI prediction model named 'MultiDTI' which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, 'MultiDTI' can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve of the model is 0.961 and the area under the precision recall curve is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that 'MultiDTI' is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning. Python codes and dataset are available at https://github.com/Deshan-Zhou/MultiDTI/. Supplementary data are available at Bioinformatics online.

Research on Energy Consumption Optimization Technology of Heterogeneous Network in Computer Communication Industry

In the environment of heterogeneous network nodes, the virtual network mapping problem of energy consumption optimization is not to minimize the number of working nodes and links. In order to study the energy consumption analysis of multi-mode terminals in wireless heterogeneous networks, by investigating the working mechanism of the terminal, using the energy-aware virtual network mapping heuristic algorithm to describe the communication state of the terminal, the energy consumption analysis equation of the terminal is obtained. The analysis results show that terminal energy consumption increases with the increase of call arrival intensity and call duration; the ratio of terminal energy consumption to voice services and the probability of discovering heterogeneous networks are in an approximately linear relationship; the heterogeneous network modules enter low energy consumption the waiting state does not save terminal energy consumption.

AttrHIN: Network Representation Learning Method for Heterogeneous Information Network

Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm. However, most of the existing network representation learning (NRL) methods are for the homogeneous networks, while the real-world networks are usually heterogeneous, therefore, it is more practical to provide an intelligent insight into the evolution of heterogeneous networks. In this paper, we propose a novel heterogeneous network embedding method, called AttrHIN, which adopts weighted meta-path-based random walks strategy, and can make full use of the attribute information to capture the latent features. AttrHIN is suitable for the different types of nodes in heterogeneous networks. Extensive experimental results show that compared with the state-of-art algorithms, AttrHIN achieves better results in Macro-F1 and Micro-F1 for multi-class node classification and Link Prediction on several real-world datasets.

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction.

Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.

An effective method for identifying the key factors of railway accidents based on the network model

In the analysis of railway accidents, the identification of key factors is an essential step for effective monitoring and daily maintenance of railway system. The existing key factor identification methods are generally based on the importance of ranking, which cannot achieve effective identification and verification because the classification of accident causal factors is not considered. Based on the complex network model, this paper proposes a heterogeneous network model which is suitable for the classification of accident causal factors to find the key causal factors of railway accident. The proposed model considers the causal factors of different types of railway accidents separately. Here, the accident causal factors are taken as the nodes, and their relationships are considered as the edges. The key factors will be determined through the extraction of the key nodes. Furthermore, an improved SIS propagation model is used to verify the effectiveness of the proposed method. Because of the strong network heterogeneity, it is proved that the proposed method is suitable for finding the key nodes in heterogeneous networks. Morever, the results show that the key nodes found by using the proposed method have more important impacts due to their faster propagation in the SIS model.

Identifying influential nodes in heterogeneous networks

Identifying influential users and measure the influence of nodes in social networks have become an interesting and important topic of research. It is crucial to find out to what extent individuals influence each other because it can be used to control rumors, diseases, and diffusion. There are numerous relevant models most of which are based on a homogeneous network. However, in the real world, we face heterogeneous networks where the nodes and edges are different types. A network is homogeneous if and only if the edges and nodes are of the same type, and it is considered heterogeneous if the nodes and edges are different. In heterogeneous networks, there is a concept known as meta-path, which indicates the type of communication between two nodes. In this paper, we aim to locate influential nodes by calculating the entropy of different meta-paths. To evaluate information diffusion in a heterogeneous network, we used the known susceptible-infectious model. The results of our experiments on three real-world networks’ dataset show that the proposed method outperforms state-of-the-art influence maximization algorithms.

Bagging Ensembled Perceptron Classifier for Seamless Mobility System in Heterogeneous Network

Wireless mobile devices require a handover decision system to get a seamless connection in a heterogeneous wireless networking environment. The handover process is one of the most significant processes in a cellular network. Few research works have been developed for providing seamless connectivity using different handover techniques. But, controlling data traffic during the process of seamless mobile data connectivity was not solved. So, there is a necessity to introduce a new model to control the traffic and improving the seamless mobility management in heterogeneous network. A new model called Bagging Ensembled Perceptron Classification based Seamless Mobility (BEPC-SM) introduced to achieve higher data delivery rate with minimum packet loss rate and data transmission delay by means of classifying the mobile nodes in heterogeneous network. In BEPC-SM model, randomly considers a number of mobile nodes in the heterogeneous network as input. Then, BEPC-SM model determines signal strength for each mobile node in a heterogeneous network. Bagging Ensembled Perceptron Classification algorithm is used in BEPC-SM model with the aim of accurately classifying all mobile nodes as strong or weak strength node with a lower amount of time consumption. After that, the distance between the weak strength node and the access point in the network is measured. Lastly, BEPC-SM Model selects the nearby access point with maximum bandwidth availability for each weak strength node in the network to perform the handover process. Thus, the performance of seamless data communication in a heterogeneous network is improved in BEPC-SM model. The BEPC-SM model is used in traffic-aware seamless data communication in a heterogeneous network. Simulation evaluation of the BEPC-SM Model is carried out on factors such as data delivery rate, packet loss rate, data transmission delay with respect to a number of data packets. The simulation result depicts that the BEPC-SM Model is able to increases the data delivery rate and also reduces delay when compared to state-of-the-art works.

Hierarchy construction and classification of heterogeneous information networks based on RSDAEf

Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.

A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks

In order to maximize the network performance of heterogeneous sensor networks and effectively control the network cost, a clustering routing algorithm based on wolf pack algorithm (CLWPA) for heterogeneous wireless sensor networks is proposed. Firstly, the optimal deployment of heterogeneous nodes is transformed into a mixed integer programming problem. The approximate optimal solution of the problem is obtained by through the wolf pack algorithm (WPA) which improved by logistic function and levy flight, then a heterogeneous network routing algorithm based on the improved wolf pack algorithm (LWPA) is proposed. Secondly, in order to solve the problem of fixed path in LWPA routing algorithm, the concept of edge degree is introduced to improve DEEC algorithm. The improved DEEC algorithm (IDEEC) is used to dynamically cluster common nodes in heterogeneous networks, and the data transmission mode is carried out after the clustering mode set. Finally, through simulation analysis, compared with other three heterogeneous network routing algorithms, CLWPA algorithm effectively prolongs the network's stable period and lifetime, and the energy consumption is more balanced.

Meta-path-based outlier detection in heterogeneous information network

Mining outliers in heterogeneous networks is crucial to many applications, but challenges abound. In this paper, we focus on identifying meta-path-based outliers in heterogeneous information network (HIN), and calculate the similarity between different types of objects. We propose a meta-path-based outlier detection method (MPOutliers) in heterogeneous information network to deal with problems in one go under a unified framework. MPOutliers calculates the heterogeneous reachable probability by combining different types of objects and their relationships. It discovers the semantic information among nodes in heterogeneous networks, instead of only considering the network structure. It also computes the closeness degree between nodes with the same type, which extends the whole heterogeneous network. Moreover, each node is assigned with a reliable weighting to measure its authority degree. Substantial experiments on two real datasets (AMiner and Movies dataset) show that our proposed method is very effective and efficient for outlier detection.

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.