Graph Ranking Guarantees for Numerical Approximations to Katz Centrality

Nathan, Eisha; Sanders, Geoffrey; Fairbanks, James; Henson, Van Emden; Bader, David A.

doi:10.1016/j.procs.2017.05.021

Cited by 23 publications

(17 citation statements)

References 9 publications

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“…We extended our previous work in [1][2][3] by using dynamic algorithms for calculating centrality scores in order to find local communities in evolving networks.…”

Section: Discussionmentioning

confidence: 99%

“…We extend our previous work in [1][2][3] by applying it to the problem of local community detection, defined further in Section 3.1. Our work on updating PageRank and Katz Centrality are necessary steps towards tracking "relevant" subgraphs around seed vertices using personalized centrality metrics.…”

Section: Contributionsmentioning

confidence: 99%

“…Numerical experiments validating the bound including analysis of both precision and performance of our method are presented in Section 6.2. The numerical guarantee is originally derived in our earlier work in [1].…”

Section: Guaranteed Rankingmentioning

confidence: 99%

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Local Community Detection in Dynamic Graphs Using Personalized Centrality

et al. 2017

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Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric.

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“…We extended our previous work in [1][2][3] by using dynamic algorithms for calculating centrality scores in order to find local communities in evolving networks.…”

Section: Discussionmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

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Local Community Detection in Dynamic Graphs Using Personalized Centrality

et al. 2017

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“…Word with the following characters: capitalized, italicized, bold, underlined which are not categorized under the top ranked words are also considered. These characters are represent the importance of the word [18]. The ranked keyword are send to the second phase.…”

Section: Figure 1 Research Flow Of Multi-document Extractive Text Summentioning

confidence: 99%

“…The node centrality is used for finding the relationships among the connected sentences. Node centrality is found by using the Katz centrality measures [18].…”

Section: Topic Modelermentioning

confidence: 99%

Multi-Documents Extractive Text Summarization using Node Centrality

Kuriakose,

Umadevi

2020

IJEAT

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The advancement of technologies produce vast amount of data over the internet. The massive amount of information flooded in the webpages become more difficult to extract the meaningful insights. Social media websites are playing major role in publishing news events on the similar topic with different contents. Extracting the hidden information from the multiple webpages are tedious job for researchers and industrialists. This paper mainly focuses on gathering information from multiple webpages and to produce summary from those contents under similar topic. Multi-document extractive summarization has been developed using the graph based text summarization method. Proposed method builds a graph between the multi-documents using the Katz centrality of nodes. The performance of proposed GeSUM (Graph based Extractive Summarization) is evaluated with the ROUGE metrics.

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Sampling Based Katz Centrality Estimation for Large-Scale Social Networks

Lin

Nguyen

et al. 2020

Lecture Notes in Computer Science

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Graph Ranking Guarantees for Numerical Approximations to Katz Centrality

Cited by 23 publications

References 9 publications

Local Community Detection in Dynamic Graphs Using Personalized Centrality

Local Community Detection in Dynamic Graphs Using Personalized Centrality

Multi-Documents Extractive Text Summarization using Node Centrality

Sampling Based Katz Centrality Estimation for Large-Scale Social Networks

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