A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks

Wang, Junyi; Hou, Xiaoni; Li, Kezan; Ding, Yong

doi:10.1016/j.physa.2017.02.007

Cited by 75 publications

(42 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that in most of the unweighted networks, the edges are treated equally, which is not the instance in this study. Each connection in the stock market network may have different underlying significances in network structures and functions, and centrality metrics can be influenced by taking into account the weightings that are applied to them [45,46]. For different kinds of network flows, various centrality measures should be used [47].…”

Section: Centrality Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua

Huang

2019

Symmetry

View full text Add to dashboard Cite

The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.

show abstract

Section: Centrality Metricsmentioning

confidence: 99%

“…The weighted degree centrality takes into consideration the weights of ties, and this has been the preferred measure for analysing weighted networks [46,53]. In this research, a number of connections pointing to or emerging from a stock in the graph, and edge weight is included.…”

Section: Centrality Metricsmentioning

confidence: 99%

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Hua

Huang

2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…Evidence theory has been employed to identify influential nodes in weighted networks [37]. The different importance of the direction of a link in spreading was taken into account by considering weighted neighborhood centrality [38] and asymmetric link weights [39].…”

Section: Introductionmentioning

confidence: 99%

Weighted h-index for Identifying Influential Spreaders

Gao

et al. 2019

Symmetry

View full text Add to dashboard Cite

In this paper, we propose weighted h-index h w and h-index strength s h to measure spreading capability and identify the most influential spreaders. Experimental results on twelve real networks reveal that s h was more accurate and more monotonic than h w and four previous measures in ranking the spreading influence of a node evaluated by the single seed SIR spreading model. We point out that the questions of how to improve monotonicity and how to determine a proper neighborhood range are two interesting future directions.

show abstract

“…flights to many nodes in other layers since they have the necessary infrastructure to support the air traffic to and from many countries. These bridge nodes with a large number of external links are called 'central bridge nodes', which could be considered as influential spreaders or superspreaders [23][24][25]. Hereafter, for simplicity, we will refer to the 'central bridge nodes' as 'bridge nodes'.…”

Section: Introductionmentioning

confidence: 99%

“…A node v in the cluster belongs to the subcluster of the bridge node u when the node u is the nearest bridge node of v among all the bridge nodes in the cluster. (b) Subcluster mass as a function of ℓ subcluster (see equation(11)) in log-log scale for a ER network with k 4 á ñ = and N=10 5 at T=T c =0 25…”

mentioning

confidence: 99%

The role of bridge nodes between layers on epidemic spreading

et al. 2018

View full text Add to dashboard Cite

Real networks, like the international airport network and the Internet, are composed of interconnected layers (or communities) through a small fraction of nodes that we call here 'bridge nodes'. These nodes are crucial in the spreading of epidemics because they enable the spread the disease to the entire system. In this work we study the effect of the bridge nodes on the susceptibleinfected-recovered model in a two layer network with a small fraction r of these nodes. In the dynamical process, we theoretically determine that at criticality and for the limit r→0, the time t b at which the first bridge node is infected diverges as a power-law with r, while above criticality, it appears a crossover between a logarithmic and a power-law behavior. Additionally, in the steady state at criticality, the fraction of recovered nodes scales with r as a power-law whose exponent can be understood from the finite size cluster distribution at criticality. We also test our model on the real international airline network and show that 'high-degree bridge nodes' reduce the time t b .

show abstract

A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks

Cited by 75 publications

References 46 publications

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Centrality Metrics’ Performance Comparisons on Stock Market Datasets

Weighted h-index for Identifying Influential Spreaders

The role of bridge nodes between layers on epidemic spreading

Contact Info

Product

Resources

About