Discussion on Damping Factor Value in PageRank Computation

Srivastava, Atul Kumar; Garg, Rakhi; Mishra, Priyanka

doi:10.5815/ijisa.2017.09.03

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…The parameters used are: (1) a damping factor α = 0.15 as experiments suggest that small changes in α have little effect in practice [51,52]. (2) for the preference vector, we choose to favor the Hubs.…”

Section: Resultsmentioning

confidence: 99%

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M-Centrality: identifying key nodes based on global position and local degree variation

Ibnoulouafi¹,

Haziti²,

Cherifi³

2018

J. Stat. Mech.

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Identifying influential nodes in a network is a major issue due to the great deal of applications concerned, such as disease spreading and rumor dynamics. That is why, a plethora of centrality measures has emerged over the years in order to rank nodes according to their topological importance in the network. Local metrics such as degree centrality make use of a very limited information and are easy to compute. Global metrics such as betweenness centrality exploit the information of the whole network structure at the cost of a very high computational complexity. Recent works have shown that combining multiple metrics is a promising strategy to quantify the node's influential ability. Our work is in this line. In this paper, we introduce a multi-attributes centrality measure called M-Centrality that combines the information on the position of the node in the network with the local information on its nearest neighborhood. The position is measured by the K-shell decomposition, and the degree variation in the neighborhood of the node quantifies the influence of the local context. In order to examine the performances of the proposed measure, we conduct experiments on small and large scale real-world networks from the perspectives of transmission dynamics and network connectivity. According to the empirical results, the M-Centrality outperforms its alternatives in identifying both influential spreaders and nodes essential to maintain the network connectivity. In addition, its low computational complexity makes it easily applied to large scale networks.

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Section: Resultsmentioning

confidence: 99%

“…For networks with unknown information about central nodes, we mark in gray are the nodes that appear frequently in the ranking lists 7. The parameters used are: (1) a damping factor α = 0.15 as experiments suggest that small changes in α have little eect in practice[51,52]. (2) for the preference vector, we choose to favor the Hubs.…”

mentioning

confidence: 99%

M-Centrality: identifying key nodes based on global position and local degree variation

Ibnoulouafi¹,

Haziti²,

Cherifi³

2018

J. Stat. Mech.

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“…The combination depends on the value of the damping factor, which is between 0 and 1. The damping factor α [34] specifies how long a random web surfer spends following the hyperlink structure rather than teleporting. If we consider the damping factor to be 0.8, that means out of total time, 80% of the time has been taken by a random web surfer to follow the hyperlink structure, and the remaining 20% of the time they teleport to new web pages randomly.…”

Section: A Centrality Measure Using Second-level Neighborhood Trust V...mentioning

confidence: 99%

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks

Hajarathaiah

Enduri

Anamalamudi

et al. 2022

Entropy

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Computing influential nodes gets a lot of attention from many researchers for information spreading in complex networks. It has vast applications, such as viral marketing, social leader creation, rumor control, and opinion monitoring. The information-spreading ability of influential nodes is greater compared with other nodes in the network. Several researchers proposed centrality measures to compute the influential nodes in a complex network, such as degree, betweenness, closeness, semi-local centralities, and PageRank. These centrality methods are defined based on the local and/or global information of nodes in the network. However, due to their high time complexity, centrality measures based on the global information of nodes have become unsuitable for large-scale networks. Very few centrality measures exist that are based on the attributes between nodes and the structure of the network. We propose the nearest neighborhood trust PageRank (NTPR) based on the structural attributes of neighbors and nearest neighbors of nodes. We define the measure based on the degree ratio, the similarity between nodes, the trust values of neighbors, and the nearest neighbors. We computed the influential nodes in various real-world networks using the proposed centrality method. We found the maximum influence by using influential nodes with SIR and independent cascade methods. We also compare the maximum influence of our centrality measure with the existing basic centrality measures.

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“…The proposed matrix is a Markov matrix: (1) a convex combination of two stochastic matrices H and 1 n ee T ; (2) irreducible, each node directly connected to every other node; (3) aperiodic, due to the self-loops G ii>0 , for all i and; (4) sparse. For a discussion of the convergence process, please refer to [39], [51]. Coefficient α is typically fixed to 0.85.…”

Section: Pagerank For Graph Centralitymentioning

confidence: 99%

Cluster Density Properties Define a Graph for Effective Pattern Feature Selection

2020

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Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without significant or noticeable loss of information. The purpose of this study is to develop and investigate a new unsupervised feature selection method which uses the k-influence space concept and subspace learning to map features onto a weighted graph and rank them by importance according to the PageRank graph centrality measure. The graph design in this method promotes feature relevance, downgrades redundancy, and is robust to outliers and cluster imbalances. In K-Means classification experiments using the ASU feature selection testing datasets, the method produces better accuracy and normalized mutual information results than state-of-the-art unsupervised feature selection algorithms. In a further evaluation, using a dataset of over 14,000 tweets, conventional classification of features selected by the method gave better sentiment analysis results than deep learning feature selection and classification.

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Discussion on Damping Factor Value in PageRank Computation

Cited by 9 publications

References 28 publications

M-Centrality: identifying key nodes based on global position and local degree variation

M-Centrality: identifying key nodes based on global position and local degree variation

Computing Influential Nodes Using the Nearest Neighborhood Trust Value and PageRank in Complex Networks

Cluster Density Properties Define a Graph for Effective Pattern Feature Selection

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