A New Group Centrality Measure for Maximizing the Connectedness of Network Under Uncertain Connectivity

2018 ◽  
pp. 3-14 ◽  
Author(s):  
Takayasu Fushimi ◽  
Kazumi Saito ◽  
Tetsuo Ikeda ◽  
Kazuhiro Kazama
Keyword(s):  
Centrality Measure

Three Perspectives on Centrality

2020 ◽  
pp. 333-351
Author(s):  
Stephen P. Borgatti ◽  
Martin G. Everett
Keyword(s):  
Centrality Measures ◽  
Graph Invariant ◽  
Propagation Process ◽  
Centrality Measure ◽  
The Third ◽  
First Arrival ◽  
The Difference

This chapter presents three different perspectives on centrality. In part, the motivation is definitional: what counts as a centrality measure and what doesn’t? But the primary purpose is to lay out ways that centrality measures are similar and dissimilar and point to appropriate ways of interpreting different measures. The first perspective the chapter considers is the “walk structure participation” perspective. In this perspective, centrality measures indicate the extent and manner in which a node participates in the walk structure of a graph. A typology is presented that distinguishes measures based on dimensions such as (1) what kinds of walks are considered (e.g., geodesics, paths, trails, or unrestricted walks) and (2) whether the number of walks is counted or the length of walks is assessed, or both. The second perspective the chapter presents is the “induced centrality” perspective, which views a node’s centrality as its contribution to a specific graph invariant—typically some measure of the cohesiveness of the network. Induced centralities are computed by calculating the graph invariant, removing the node in question, and recalculating the graph invariant. The difference is the node’s centrality. The third perspective is the “flow outcomes” perspective. Here the chapter views centralities as estimators of node outcomes in some kind of propagation process. Generic node outcomes include how often a bit of something propagating passes through a node and the time until first arrival of something flowing. The latter perspective leads us to consider the merits of developing custom measures for different research settings versus using off-the-shelf measures that were not necessarily designed for the current purpose.

Father Status Centrality Measure

2016 ◽  
Author(s):  
Kari Adamsons ◽  
Kay Pasley
Keyword(s):  
Centrality Measure

A New SNA Centrality Measure Quantifying the Distance to the Nearest Center

2010 ◽  
pp. 579-586
Author(s):  
Angela Bohn ◽  
Stefan Theußl ◽  
Ingo Feinerer ◽  
Kurt Hornik ◽  
Patrick Mair ◽  
...  
Keyword(s):  
Centrality Measure

Economic-geographical location as a factor in productivity differences between Russia’s regions

2020 ◽  
pp. 79-91
Author(s):  
K. V. Rostislav
Keyword(s):  
Russian Federation ◽  
Institutional Environment ◽  
Labour Force ◽  
Geographical Location ◽  
Productivity Index ◽  
Centrality Measure ◽  
The Russian Federation ◽  
Productivity Estimation ◽  
The Relationship ◽  
Better Than

The article is devoted to assessing the relationship between productivity as the most important source of sustainable economic development, and various factors that can explain this productivity. The method of productivity estimation used in the paper takes into account that income is created using not only living labour, but also capital stock. In contrast to previous studies, the paper uses the productivity index that meets the transitivity criterion, which allows for geographical comparisons. To assess the benefits of economic-geographical location (EGL), a new centrality measure is presented that reflects the network nature of territorial connections and allows us to switch to accounting for not only points but also areal objects, particularly the subjects of the Russian Federation. Using the new centrality measure, it is shown that EGL explains the differences in productivity between the regions – the subjects of the Russian Federation in 2010–2016 better than other factors. At the same time, it follows from the estimated model that various properties of the labour force described by the concepts of human capital, and the institutional environment are significantly less related to the observed productivity of regions. To demonstrate the superiority of economic-geographical approaches to explaining productivity, we used relatively new for economic geography methods of machine learning.

scholarly journals A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Cheng ◽  
Wenbo Zhang ◽  
Haijuan Yang ◽  
Xing Su ◽  
Tao Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Structure ◽  
Community Detection ◽  
Centrality Measures ◽  
Ideal Solution ◽  
Centrality Measure ◽  
Detection Algorithms ◽  
Pros And Cons ◽  
Topsis Technique

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

Sign in / Sign up

Export Citation Format

Share Document