Topic
Katz centrality
About: Katz centrality is a research topic. Over the lifetime, 601 publications have been published within this topic receiving 77858 citations.
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09 Jul 2018
TL;DR: The problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population, is studied and the existence of Nash equilibria under reasonable assumptions is shown.
Abstract: We study the problem of two competing camps aiming to maximize the adoption of their respective opinions, by optimally investing in nodes of a social network in multiple phases. The final opinion of a node in a phase acts as its biased opinion in the following phase. Using an extension of Friedkin-Johnsen model, we formulate the camps' utility functions, which we show to involve what can be interpreted as multiphase Katz centrality. We hence present optimal investment strategies of the camps, and the loss incurred if myopic strategy is employed. Simulations affirm that nodes attributing higher weightage to bias necessitate higher investment in initial phase. The extended version of this paper analyzes a setting where a camp's influence on a node depends on the node's bias; we show existence and polynomial time computability of Nash equilibrium.
6 citations
12 Sep 2011
TL;DR: This study explores the evolution of a co-authorship network over time and finds that while the association between number of new attached nodes to an existing node and all its main centrality measures is almost positive and significant, the betweenness centrality correlation coefficient is always higher and increasing as network evolved over time.
Abstract: Complex networks (systems) as a phenomenon can be observed by a wide range of networks in nature and society. There is a growing interest to study complex networks from the evolutionary and behavior perspective. Studies on evolving dynamical networks have been resulted in a class of models to explain their evolving dynamic behavior that indicate a new node attaches preferentially to some old nodes in the network based on their number of links. In this study, we aim to explore if there are any other characteristics of the old nodes which affect on the preferential attachment of new nodes. We explore the evolution of a co-authorship network over time and find that while the association between number of new attached nodes to an existing node and all its main centrality measures (i.e., degree, closeness and betweenness) is almost positive and significant but betweenness centrality correlation coefficient is always higher and increasing as network evolved over time. Identifying the attachment behavior of nodes in complex networks (e.g., traders, disease propagation and emergency management) help policy and decision makers to focus on the nodes (actors) in order to control the resources distribution, information dissemination, disease propagation and so on due to type of the network.
6 citations
TL;DR: A component ranking approach that models the component graph of a system as a Discrete-Time Markov Chain and uses it as a basis for component ranking, which produces results that are superior to ranking strategies based on centrality measures such as closeness, betweenness and eigenvector centrality.
Abstract: Large software systems consist of many components some of which are more significant than others. A component is deemed significant if it plays an important role and can have a significant impact on the rest of the system when modified. Identifying such core components is important since change is inevitable as a normal course of evolution in any system and core components must be designed to minimize their impact of change. Several different graph-based strategies exist for ranking software components that can be used to identify the core components within a software system. However, each ranking strategy behaves differently and most fail to pick up all of the significant core components among their top tier of highly ranked components. In this paper, we propose a component ranking approach that models the component graph of a system as a Discrete-Time Markov Chain and uses it as a basis for component ranking. Using this approach produces results that are superior to ranking strategies based on centrality measures such as closeness, betweenness and eigenvector centrality. We demonstrate the utility of the metric and compare it against existing graph-based measures, in the analysis of Kona and JUnit, two published systems with documented architectures.
6 citations
01 Jan 2010
TL;DR: Based on the three centrality indices, an Exploratory Weighted Method (EWM) were put forward, namely system centrality, to do a further research, and the results showed the hierarchical structure with the "Ding Pattern" in the top class and the Flyover Effect in the national network.
Abstract: The airport system is an important component for the organization of aviation transportation. It is an effective way to analyze the constitution of air transportation and urban systems by identifying the spatial structure of airport system. Centrality is one of the basic methods. Based on network analysis, three common indices, degree centrality, closeness centrality and betweenness centrality were used to measure centrality for individual cities in the paper. That is to say, "Being central" is not limited to being connected to others, but also being close to all others and being intermediary between others. In the airport system of China, degree centrality shows the "Ding Pattern" for the top class and the "Flyover Effect" for the national network. Closeness centrality makes it measurable for the airport service. Betweenness centrality is a good measure to analyze regional hubs and the core-periphery pattern. On individual cities, Beijing and Shanghai are clearly the top two central cities in all three indices; and rankings on other cities by the betweenness centrality differ significantly from those by the degree and closeness centralities. Based on the three centrality indices, an Exploratory Weighted Method (EWM) were put forward, namely system centrality, to do a further research. The results showed the hierarchical structure with the "Ding Pattern" in the top class and the "Flyover Effect" in the national network. Besides, the spatial pattern by the system centrality accords well with the five airport clusters designed in the National Civil Airports Deployment Plan.
6 citations
TL;DR: In this article, the authors proposed a method to solve the problem of how to find the best solution for a given problem by using the concept of the "missing link" in the first place.
Abstract: 본 연구는 축구 경기로 형성된 관계 네트워크 내에서 가장 영향력 있는 선수를 발굴하고 선수 개인이 팀 네트워크 내에서 얼마만큼의 정량적 역량을 지니며 팀에 기여하는지와 선수들과 포지션 상호간의 관계 네트워크가 감독의 전술에 부합하는지를 살펴보았다. 분석 결과 전반전에는 감독의 전술에 따라 공격과 수 비수의 네트워크 중심성이 고르게 높은 수치를 나타내었다. 이는 각 선수들이 포지션별 역할을 충실히 수행 한 것으로 판단할 수 있으며, 선수들 간의 네트워크가 유기적으로 이루어져 경기를 리드하고 선순환적인 네트워크 구조를 형성한 것을 의미한다. 반면 후반전 초반에는 전반전에 비해 특정 선수에게 지나치게 네트 워크 중심이 치우치는 양상을 보이며 원활한 경기가 이루어지지 않았다. 하지만 이후 감독의 적절한 선수교 체와 과감한 전술의 변화로 이러한 편향적인 구조를 벗어나 선수들 간의 네트워크 균형을 안정화 시키는 결과를 이루어냈다.
6 citations