Nodes In Complex Networks Research Articles

Comparing protein\u2013protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.

Physical Disturbance Reduces Cyanobacterial Relative Abundance and Substrate Metabolism Potential of Biological Soil Crusts on a Gold Mine Tailing of Central China.

As the critical ecological engineers, biological soil crusts (biocrusts) are considered to play essential roles in improving substrate conditions during ecological rehabilitation processes. Physical disturbance, however, often leads to the degradation of biocrusts, and it remains unclear how the physical disturbance affects biocrust microorganisms and their related metabolism. In this study, the photosynthetic biomass (indicated by chlorophyll a), nutrients, enzyme activities, and bacterial communities of biocrusts were investigated in a gold mine tailing of Central China to evaluate the impact of physical disturbance on biocrusts during the rehabilitation process of gold mine tailings. The results show that physical disturbance significantly reduced the photosynthetic biomass, nutrient contents (organic carbon, ammonium nitrogen, nitrate nitrogen, and total phosphorus), and enzyme activities (β-glucosidase, sucrase, nitrogenase, neutral phosphatase, and urease) of biocrusts in the mine tailings. Furthermore, 16S rDNA sequencing showed that physical disturbance strongly changed the composition, structure, and interactions of the bacterial community, leading to a shift from a cyanobacteria dominated community to a heterotrophic bacteria (proteobacteria, actinobacteria, and acidobacteria) dominated community and a more complex bacterial network (higher complexity, nodes, and edges). Altogether, our results show that the biocrusts dominated by cyanobacteria could also develop in the tailings of humid region, and the dominants (e.g., Microcoleus) were the same as those from dryland biocrusts; nevertheless, physical disturbance significantly reduced cyanobacterial relative abundance in biocrusts. Based on our findings, we propose the future work on cyanobacterial inoculation (e.g., Microcoleus), which is expected to promote substrate metabolism and accumulation, ultimately accelerating the development of biocrusts and the subsequent ecological restoration of tailings.

Critical nodes identification in complex networks via similarity coefficient

Critical nodes of complex networks are pivotal for effective information propagation. Several centrality methods have been presented to identify critical nodes relied on the relationships between node and its neighbors, which indicates that the interaction between them will account for determining the importance of nodes. In this paper, a novel ranking method based on the defined similarity coefficient is proposed. The main thought of the proposed method is that the importance of nodes being linked to the local structural similarity between node and its neighbors. By quantifying the distinction between the node’s local structure information, a similarity matrix of the entire network is constructed first. Further, the similarity coefficient of node is defined by taking into account the interaction relationships between nodes and the local structural attribute of complex networks. Finally, a novel method, named Extended Similarity Coefficient Ranking Method (ESCRM), is proposed to determine the influentiality of node based on its neighbors’ information. We perform extensive experiments on ten real-world datasets to comprehensively estimate the performance of the ESCRM and other ten representative centralities. The experimental results demonstrate that under the Susceptible-Infected-Recovered (SIR) model, the ESCRM outperforms the other centralities on identifying critical nodes, and as a relatively efficient ranking method can provide better performance.

New Organizations in Complex Networks: Survival and Success

Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.

A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication

Graph-based clustering identification is a practical method to detect the communication between nodes in complex networks that has obtained considerable comments. Since identifying different communities in large-scale data is a challenging task, by understanding the communication between the behaviors of the elements in a community (a cluster), the general characteristics of clusters can be predicted. Graph-based clustering methods have played an important role in clustering gene expression data because of their ability to show the relations between the data. In order to be able to identify genes that lead to the development of diseases, the communication between the cells must be established. The communication between different cells can be indicated by the expression of different genes within them. In this study, the problem of cell-to-cell communication is expressed as a graph and the communication are extracted by recognizing the communities. The FANTOM5 dataset is used to simulate and calculate the similarity between cells. After preprocessing and normalizing the data, to convert this data into graphs, the expression of genes in different cells was examined and by considering a threshold and Wilcoxon test, the communication between them were identified through using clustering.

Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy.

Identifying influential nodes in complex networks has attracted the attention of many researchers in recent years. However, due to the high time complexity, methods based on global attributes have become unsuitable for large-scale complex networks. In addition, compared with methods considering only a single attribute, considering multiple attributes can enhance the performance of the method used. Therefore, this paper proposes a new multiple local attributes-weighted centrality (LWC) based on information entropy, combining degree and clustering coefficient; both one-step and two-step neighborhood information are considered for evaluating the influence of nodes and identifying influential nodes in complex networks. Firstly, the influence of a node in a complex network is divided into direct influence and indirect influence. The degree and clustering coefficient are selected as direct influence measures. Secondly, based on the two direct influence measures, we define two indirect influence measures: two-hop degree and two-hop clustering coefficient. Then, the information entropy is used to weight the above four influence measures, and the LWC of each node is obtained by calculating the weighted sum of these measures. Finally, all the nodes are ranked based on the value of the LWC, and the influential nodes can be identified. The proposed LWC method is applied to identify influential nodes in four real-world networks and is compared with five well-known methods. The experimental results demonstrate the good performance of the proposed method on discrimination capability and accuracy.

Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy.

In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible–infected–removed and susceptible–infected–removed–susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.

A novel potential edge weight method for identifying influential nodes in complex networks based on neighborhood and position

The identification of influential nodes is one of the most urgent and challenging research issues in complex networks, which is crucial to the robustness and stability of networks. Edges in networks are vital communication channels in the information spreading process, many existing methods for identifying influential nodes are established via employing characteristics of nodes, but most of them consider the edges equally in unweighted networks. In this paper, we introduce a novel potential edge weight to distinguish the influence of each edge by utilizing the H-index, k-shell iteration factor and clustering coefficient of its connection nodes. A novel potential edge weight method, called the HIC centrality (HIC), is proposed to identify influential nodes in complex networks. The key idea of HIC is to incorporate neighborhood, position, and topological structure features to identify influential nodes. To evaluate the performance of HIC, we adopt the susceptible–infected–recovered (SIR) model and the susceptible–infected (SI) model for simulation and conduct a series of comparative experiments on nine real-world networks and three artificial networks. Experimental results show that the proposed method is superior to seven contrast methods in terms of accuracy, effectiveness and distinguishing ability, including five centrality measures and two popular potential edge weight methods. The HIC centrality has low computational complexity, allowing it to be applied to sparse large-scale networks.

Semi-Local Integration Measure of Node Importance

Numerous centrality measures have been introduced as tools to determine the importance of nodes in complex networks, reflecting various network properties, including connectivity, survivability, and robustness. In this paper, we introduce Semi-Local Integration (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood. We illustrate SLI node importance differentiation among nodes in lexical networks and demonstrate its potential in natural language processing (NLP). In the NLP task of sense identification and sense structure analysis, the SLI centrality measure evaluates node integration and provides the necessary local resolution by differentiating the importance of nodes to a greater extent than standard centrality measures. This provides the relevant topological information about different subnetworks based on relatively local information, revealing the more complex sense structure. In addition, we show how the SLI measure can improve the results of sentiment analysis. The SLI measure has the potential to be used in various types of complex networks in different research areas.

Modeling Complex Networks Based on Deep Reinforcement Learning

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

Influential node identification by aggregating local structure information

In complex networks, the identification of influential nodes is very important to study the transmission and control of viruses, the location of key points of network attacks, the spread of public opinion, and the marketing promotion of markets. Therefore, based on analysis of the existing algorithms for the identification of influential nodes in complex networks, this paper proposes a new method to identify influential nodes by aggregating local structure information (ALSI). This method considers two factors: the influence of the node itself and the influence contributed by the neighbor node. The degree and K-shell value of the node are introduced when calculating the influence of the node itself, and the degree and K-shell value of the neighbor node are introduced in the calculation of the influence contributed by the neighbor node. Different calculation methods are adopted according to the comparison result of the K-shell value with the node. The greater the K-shell value and the node degree are, the more important the node is. To evaluate the performance of the algorithm, the susceptible–infected–recovered (SIR) model is used to analyze and compare the running results of 9 algorithms on 8 different networks. The experimental results show that the proposed algorithm can effectively detect the influence of nodes and outperforms many state-of-the-art algorithms.

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type → analysis scope → analysis approach → analysis category → analysis sub-category → analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> and influential spreader nodes in social networks.

Node importance ranking method in complex network based on gravity method

How to use quantitative analysis methods to identify which nodes are the most important in complex network, or to evaluate the importance of a node relative to one or more other nodes, is one of the hot issues in network science research. Now, a variety of effective models have been proposed to identify important nodes in complex network. Among them, the gravity model regards the coreness of nodes as the mass of object, the shortest distance between nodes as the distance between objects, and comprehensively considers the local information of nodes and path information to identify influential nodes. However, only the coreness is used to represente the quality of the object, and the factors considered are relatively simple. At the same time, some studies have shown that the network can easily identify the core-like group nodes with locally and highly clustering characteristics as core nodes when performing &lt;i&gt;k&lt;/i&gt;-core decomposition, which leads to the inaccuracy of the gravity algorithm. Based on the universal gravitation method, considering the node &lt;i&gt;H&lt;/i&gt; index, the number of node cores and the location of node structural holes, this paper proposes an improved algorithm ISM and its extended algorithm ISM&lt;sub&gt;+&lt;/sub&gt;. The SIR model is used to simulate the propagation process in several classical real networks and artificial networks, and the results show that the proposed algorithm can better identify important nodes in the network than other centrality indicators.

Identifying Influential Nodes in Complex Networks Based on Spreading Probability

Identifying Influential Nodes in Complex Networks Based on Spreading Probability

Identifying Influential Nodes in Complex Networks Based on Resource Allocation Similarity

Identifying Influential Nodes in Complex Networks Based on Resource Allocation Similarity

Locally Expansion Directed Community Detection Method Considering Node Leadership

In recent years, community structure has become a research hotspot in complex networks, and there are currently many excellent methods in the field of community detection, such as label propagation algorithms, LFM community detection algorithms, etc. These algorithms primarily investigate undirected networks, but the linking relationships between nodes in real complex networks are mostly directed and asymmetric, such as social networks. Therefore it is imperative to perform community detection on directed networks. We proposed a locally expansion community detection algorithm (DLE-NL) that can be applied to directed networks. There are four essential parts of DLE-NL algorithm: Firstly, the node cross-leadership index is proposed to select seed nodes considering local and global factors and directionality; secondly, seed node confidants are chosen to form the initial community using node following degree, and the core of the community is precisely mined; then node aggregation is carried out considering the node affiliation to the community with the initial community as the core. Finally, the initial reconstruction and optimization of the community are carried out by removing communities with less than three nodes, correcting the affiliation of nodes to the community based on the community directed-fitness function and the gain of the derivative, assigning free nodes and merging communities with high similarity. The experimental results show that the method performs well in the directed modularity and normalized mutual information metrics, while the community detection results are more stable.

Efficient algorithm for finding the influential nodes using local relative change of average shortest path

In complex networks, finding the influential nodes playing a crucial role in theoretical and practical point of view because they are capable of propagating information to large portion of the network. Investigating the dynamics of information spreading in complex networks is a hot topic with a wide range of applications, including information dissemination, information propagation, rumour control, viral marketing, and opinion monitoring. In recent years, several centrality measures have been discovered to find influential nodes in complex networks. In this work, the local relative change of average shortest path (i.e Local RASP) based on the local structure of the network is being proposed. This local RASP measure of a node defined based on the local network’s relative change in average shortest path when the node is deleted. Our local RASP centrality produces good results compared to degree, betweenness, closeness, semi-local, PageRank, Trust-PageRank, and RASP centralities. Our local RASP centrality measure’s computation time is less compared to global centrality measure RASP. It measures the information diffusion efficiently within the network through the initial seed nodes identified by the local RASP.

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

The network topology is the basis of studying the overall performance and dynamic evolution mechanism of network. In regards to the study on the topology of multi-layer complex networks, the current studies fail to consider the heterogeneity of edges and nodes in multi-layer complex networks, and such methods are insufficient when they are used to analyze the static characteristics of topology. Therefore, this paper based on the theoretical basis of the multi-layer complex network, establishes a two-layer public transit complex network model where urban rail transit and bus transit cooperate with each other, and by optimizing this two-layer public transit complex network model, further proposes an integrated public transit network model. According to the method of analyzing the static statistical characteristics of complex networks, and considering the heterogeneity of nodes and edges in multi-layer network, this​ paper gives the evaluation indicators of static statistical characteristics of multi-layer public transit complex networks. Taking the two-layer public transit complex network structure in Lanzhou as an example, this paper finds out the main factors affecting its network stability and efficiency by analyzing the physical structure characteristics of this two-layer public transit complex network in Lanzhou where urban rail transit and bus transit cooperate with each other, and then puts forward the network optimization suggestions from the point of view of optimizing key nodes by using the method of identifying key nodes with centrality. In this study, the coupling among heterogeneous public transit network layers and the static structure characteristics of multi-layer public transit complex network are studied from the perspective of network structure, to assist the traffic management department in finding out the problems of the road network for the management and improvement of the performance and efficiency of the network. This has a profound influence on the development planning of urban public transit system.

A novel method for identifying influential nodes in complex networks based on gravity model

How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall’s tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.