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FPPR: fast pessimistic (dynamic) PageRank to update PageRank in evolving directed graphs on network changes

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Abstract

The paper presents a new algorithm FPPR which updates PageRanks of a directed network after topological changes in the graphs. The algorithm is capable of regenerating scores on node and link addition/deletion. The changes in the expected value of random surfers are used for updating the scores of the newly added nodes as well as the impacted chain where the nodes/links are added or removed. The complexity of the algorithm for k new node addition is \(\mathcal {O}(k\times d^{(k)}_{avg})\) where \(d^{(k)}_{avg}\) is the average degree of k nodes added. On the other hand for node deletion, the complexity is \(\mathcal {O}(|V_s|+|E_s|)\) where \(V_s\) and \(E_s\) the set of nodes and edges updated using Selective Breath First Update. Extensive experiments have been performed on different synthetic and real-world networks. The experimental result shows that the rank generated by the proposed method is highly correlated with that of the recalculation on changes using the benchmark Power Iteration algorithm.

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Author notes

  1. R. P. Pashikanti and S. Kundu are contributed equally to this work.

Authors and Affiliations

  1. Cognitive and Social Analytics Lab, Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Rajasthan, 342030, Jodhpur, India

    Rohith Parjanya Pashikanti & Suman Kundu

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  1. Rohith Parjanya Pashikanti
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  2. Suman Kundu
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Correspondence to Suman Kundu.

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Pashikanti, R.P., Kundu, S. FPPR: fast pessimistic (dynamic) PageRank to update PageRank in evolving directed graphs on network changes. Soc. Netw. Anal. Min. 12, 141 (2022). https://doi.org/10.1007/s13278-022-00968-8

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  • DOI: https://doi.org/10.1007/s13278-022-00968-8

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