Approximating Betweenness Centrality

Bader, David A.; Kintali, Shiva; Madduri, Kamesh; Mihail, Milena

doi:10.1007/978-3-540-77004-6_10

Cited by 262 publications

(196 citation statements)

References 23 publications

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“…This is double the 5% percentage suggested for use in [4]. For more details on the algorithm used, see [3].…”

Section: Robustness Resultsmentioning

confidence: 85%

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Guéret¹,

Groth²,

Harmelen³

et al. 2010

Lecture Notes in Computer Science

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Abstract. The Web of Data is increasingly becoming an important infrastructure for such diverse sectors as entertainment, government, ecommerce and science. As a result, the robustness of this Web of Data is now crucial. Prior studies show that the Web of Data is strongly dependent on a small number of central hubs, making it highly vulnerable to single points of failure. In this paper, we present concepts and algorithms to analyse and repair the brittleness of the Web of Data. We apply these on a substantial subset of it, the 2010 Billion Triple Challenge dataset. We first distinguish the physical structure of the Web of Data from its semantic structure. For both of these structures, we then calculate their robustness, taking betweenness centrality as a robustnessmeasure. To the best of our knowledge, this is the first time that such robustness-indicators have been calculated for the Web of Data. Finally, we determine which links should be added to the Web of Data in order to improve its robustness most effectively. We are able to determine such links by interpreting the question as a very large optimisation problem and deploying an evolutionary algorithm to solve this problem. We believe that with this work, we offer an effective method to analyse and improve the most important structure that the Semantic Web community has constructed to date.

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“…This is double the 5% percentage suggested for use in [4]. For more details on the algorithm used, see [3].…”

Section: Robustness Resultsmentioning

confidence: 85%

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Guéret¹,

Groth²,

Harmelen³

et al. 2010

Lecture Notes in Computer Science

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“…The Centrality measure [21] is used to find the most important metrics that are residing in the graph. Centrality measure would help the investigators in predicting the important areas where the similar types of crimes happening.…”

Section: Centrality Measurementioning

confidence: 99%

Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures

Sivaranjani¹,

Sivakumari²

2015

IJCA

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Crime detection is the vital and emerging research field in the real world environment which aims to prevent the number of crimes happening in the world. The nature of crime differs in different places based on location, age, religion, habitual characteristics and so on. Mitigating the serial crimes which are identical to each other is the most important scenario to be concerned in the real world. There may be a problem arise while mitigating the hot spots in the different crime locations due to missing values of some important features. Prediction of similar types of crimes also becomes the complex process where the temporal features are scattered. To solve the problem in this work the triangulation based interpolation methodology (TIM) and the graph measures were introduced. The TIM tends to find the missing value among the set of values based on the average level of the most nearer points where the data points are scattered unevenly.And the similarity measures assure the selection of the most nearest neighbour solutions. The similarity measures that are used in this work for predicting the most nearest location with same type of crime behaviour are Distance Measure (DM), Centrality Measure (CM) and Graph Assortativity (GA) measure. The performance evaluations were conducted with the help of spatio temporal data sets where the list of crimes and the location, behaviour are depicted properly. The experimental tests conducted proves that the proposed methodology in this work can mitigate the serial crime hot spots more accurately.

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“…Many of these methods use principles of artificial intelligence and graph theory. While some of these algorithms use betweenness centrality, another category uses hierarchical clustering, k-clique percolation to identify the nodes that form a community [4]. Another class of algorithms views community as a evolution of social interaction.…”

Section: Related Workmentioning

confidence: 99%

A Method for Community Detection in Protein Networks Using Spectral Optimization

Izudheen¹,

Mathew

2011

IJDMS

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Identification of community structures in complex networks has been a challenge in many domain and discipline. In protein networks these community interactions play a vital role in identifying the outcome of many cellular mechanisms. This paper reports the use of spectral optimization of triangular modularity as an effective method to identify these community structures.The algorithm has been carefully tested on real biological data and the results acknowledge that this is a powerful method for extracting community structures from protein networks.

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Approximating Betweenness Centrality

Cited by 262 publications

References 23 publications

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Finding the Achilles Heel of the Web of Data: Using Network Analysis for Link-Recommendation

Mitigating Serial Hot Spots on Crime Data using Interpolation Method and Graph Measures

A Method for Community Detection in Protein Networks Using Spectral Optimization

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