An efficient heuristic for betweenness estimation and ordering

Singh, Rishi Ranjan; Iyengar, S. R. S.; Chaudhary, Shubham; Agarwal, Manas

doi:10.1007/s13278-018-0542-x

Cited by 6 publications

(2 citation statements)

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“…It is due to the time complexity for computing betweenness centrality [27]. Efficient algorithms for updating and estimating betweenness centrality measures in dynamic and large-size networks are discussed in [28][29][30].…”

Section: Formulationmentioning

confidence: 99%

Node-weighted centrality: a new way of centrality hybridization

Singh

Iyengar

2020

Comput Soc Netw

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Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.

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Section: Formulationmentioning

confidence: 99%

Node-weighted centrality: a new way of centrality hybridization

Singh

Iyengar

2020

Comput Soc Netw

Self Cite

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“…The size of the vocabulary is 29523, and the average length of the text is 149. We filter 36,692 nodes and 1,83,831 edges from the original dataset [17] [28][29].…”

Section: Email-enronmentioning

confidence: 99%

g-Sum: A Graph Summarization Approach for a Single Large Social Network

Rehman

Nawaz

Ali

et al. 2018

ICST Transactions on Scalable Information Systems

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With the advances in computing resources, the processing of huge amount of data becomes possible. However, the human ability to identify patterns from such data has not scaled accordingly. Consequently, efficient computational approaches for condensing and simplifying data are becoming essential for uncovering actionable insights, especially the big social networks. Many large datasets analyzed today are represented with the help of graphs. In many real-world applications, summarizing large graphs is beneficial to achieve a number of advantages, including 1) significant speedup for graph algorithms, 2) graph storage space reduction, 3) faster network transmission, 4) improved data privacy, 5) more effective graph visualization, etc. All these benefits can be obtained from the summarized graph, if it is summarized accurately. The quality of the summary graph is mostly measured using the Reconstruction Error (RE). In the existing literature, RE is a very challenging task. Most of the approaches investigated so far ending up with high value of RE. Hence, the summarized graph does not express the original graph completely due to the high value of the RE. Therefore, in this work we have examined how can we summarize a big graph structure into a compact summary by reducing its RE and ensuring its usefulness. For this task, we have proposed a novel graph summarization approach, called g-Sum, to calculate the graph structure summaries while minimizing RE and compare to the existing work in the domain. The functionality of the g-Sum is decomposed into three different, but interlinked phases; (1)-graph dataset pre-processing; (2)-graph partitioning; and (3)-graph summarization. We have performed an extensive series of experiments on different real-world social graph dataset, including Ego-Facebook, Enron Email and Web-Stanford graph datasets. The computed results show the effectiveness of the g-Sum and also compared with the state-of-the-art graph summarization approaches, S2L and GraSS.

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Hybrid Centrality Measures for Service Coverage Problem

Singh

Iyengar

2019

Computational Data and Social Networks

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An efficient heuristic for betweenness estimation and ordering

Cited by 6 publications

References 50 publications

Node-weighted centrality: a new way of centrality hybridization

Node-weighted centrality: a new way of centrality hybridization

g-Sum: A Graph Summarization Approach for a Single Large Social Network

Hybrid Centrality Measures for Service Coverage Problem

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