A robust method to discover influential users in social networks
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Keywords:
Robustness
Closeness
Identification
Social network (sociolinguistics)
The importance of an actor in the network is measured by the different type of centrality metrics of Social Network Analysis (SNA). In the research community, who are the most prominent author or key on the network is the major discussion or research issue. Different types of centrality measures and citation based indices are available, but their result is varied from network to network. In this paper, we form a network of author and its co-author based on Maximum Spanning Tree and find out the key author based on social network analysis metrics like degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. After that we compare the result of all centrality measures of MST based network and original network, betweenness centrality value increases and the other centrality value decreases. Finally, we conclude that the betweenness centrality is useful to analyze key author in this type of network.
Katz centrality
Social Network Analysis
Network theory
Network Analysis
Network controllability
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Background/Objectives: Secondary battery is expanding large secondary battery for various applications. In this study, joint research trends were analysed using network analysis in order to investigate the R&D of secondary batteries. Methods/Statistical Analysis: Degree centrality and betweenness centrality among the network analysis methods, the complex degree centrality was used to analyse the joint research network. Furthermore, the relationships among the degree centrality, betweenness centrality, and complex degree centrality were analysed. The analysis results show that 78 out of 91 countries carried out joint research, with the exclusion of 13 countries that did not participate in international joint research. Results: The joint research between China and USA institutions was most active, and China actively conducted joint research with Asian countries. A cluster analysis of the countries participating in joint research found that there were seven clusters in total, and the USA, Germany, France, and the U.K. played central role in each cluster. A correlation analysis of the centrality indices analysis results by country showed strong positive (+) correlation among the three indices. Furthermore, a regression analysis showed that the greater the complex degree centrality was, the greater the degree centrality and betweenness centrality became, and the increasing rate of the betweenness centrality was very high. Conclusion/Application: This study is meaningful that the correlations between complex degree centrality and other centrality indices were analysed in network analysis and investigated joint research status. Keywords: Centrality Indices, Network Analysis, PFNet, Regression, Secondary Battery
Network Analysis
Network theory
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In recent years, social network theory becomes more and more significant in social science. Basing on the fast-growing social network theory, SNA (Social Network Analysis) is also widely used and published in different journals. As social actors are like nodes in the network, we use centrality to measure these nodes in power, activity and communication convenience etc.. Degree centrality, betweenness centrality and closeness centrality are main detailed measurement, and they have different algorithm. In SNA study, the research purpose determines the selection of centrality; and the use of these three centralities constitutes an important part in SNA study.
Social Network Analysis
Network theory
Closeness
Katz centrality
Social network (sociolinguistics)
Network Analysis
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One of the most recent studies on the analysis of complex systems is to understand the role of community structure and centrality in analyzing the networks of complex systems such as protein and social networks. Traditional measures of centrality – degree centrality, closeness centrality, and betweenness centrality – cannot capture how community structures within these networks configure them. In this regard, we propose a new community-consideration centrality method to fill this gap. This method includes a weight of consideration, α, ranging from 0.0 to 1.0, to balance the focus between community and network-wide importance in the centrality calculations. Our analysis of two zachary karate and dolphin datasets shows that including community consideration in the degree, closeness, and betweenness centrality measures accurately captures the proportional significance of both communities and networks. In particular, for the lung adenocarcinoma cancer protein case study, our method not only identified more cancer hallmark genes than the traditional centrality measures without considering communities but also outperformed several other advanced centrality algorithms regarding the detection of crucial cancer-related genes. A balanced objective between network and community impacts was observed at an optimum performance α values of 0.1 and 0.2. It finds a strong significance of community structure in network analysis and features a more nuanced perspective on centrality in complex systems.
Closeness
Network theory
Social Network Analysis
Katz centrality
Network Analysis
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A novel measure to identify influential nodes in complex networks based on network global efficiency
Identifying influential nodes is a basic measure of characterizing the structure and dynamics in complex networks. In this paper, we use network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks. Differing from the traditional network global efficiency, the proposed measure is determined by removing edges from networks, not removing nodes. Instead of static structure properties which are exhibited by other traditional centrality measures, such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), we focus on the perspective of dynamical process and global structure in complex networks. Susceptible-infected (SI) model is utilized to evaluate the performance of the proposed method. Experimental results show that the proposed measure is more effective than the other three centrality measures.
Closeness
Katz centrality
Network theory
Network controllability
Network Structure
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The centrality of a node is a crucial indicator to understand how important this node is in a network, and several measures of centrality have been used to realize the identification. In this paper, we propose a new one named load centrality based on the betweenness changes caused by the removal of some nodes in the network. This measure of centrality combines the ideas: more important node is closer to others and more important node stands between more node pairs. By comparing with the other point centrality measures, the load centrality is validated further.
Katz centrality
Network controllability
Identification
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Importance of estimating the centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications such as social network analysis or route planning. In this paper we find a formula to obtain the betweeness-centrality for grids.
Katz centrality
Network theory
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Developing a tailor-made centrality measure for a given task requires domain- and network-analysis expertise, as well as time and effort. Thus, automatically learning arbitrary centrality measures for providing ground-truth node scores is an important research direction. We propose a generic deep-learning architecture for centrality learning which relies on two insights: 1. Arbitrary centrality measures can be computed using Routing Betweenness Centrality (RBC); 2. As suggested by spectral graph theory, the sound emitted by nodes within the resonating chamber formed by a graph represents both the structure of the graph and the location of the nodes. Based on these insights and our new differentiable implementation of Routing Betweenness Centrality (RBC), we learn routing policies that approximate arbitrary centrality measures on various network topologies. Results show that the proposed architecture can learn multiple types of centrality indices more accurately than the state of the art.
Network theory
Autoencoder
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In this paper we study deliberate attacks on the infrastructure of large scale-free networks. These attacks are based on the importance of individual vertices in the network in order to be successful, and the concept of centrality (originating from social science) has already been utilized in their study with success. Some measures of centrality however, as betweenness, have disadvantages that do not facilitate the research in this area. We show that with the aid of scale-free network characteristics such as the clustering coefficient we can get results that balance the current centrality measures, but also gain insight into the workings of these networks.
Clustering coefficient
Scale-free network
Network controllability
Network theory
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