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Fast computation of SimRank for static and dynamic information networks
2010
Proceedings of the 13th International Conference on Extending Database Technology - EDBT '10
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Based on this, we develop a family of novel approximate SimRank computation algorithms for static and dynamic information networks, and give their corresponding theoretical justification and analysis. ...
One of the most important aspects of information network analysis is to measure similarity between nodes in a network. ...
Based on this, we have developed three efficient algorithms to compute SimRank scores for static and dynamic information network. ...
doi:10.1145/1739041.1739098
dblp:conf/edbt/LiHHJSYW10
fatcat:5v7j5k5cizbdxcg2ucebacutre
READS
2017
Proceedings of the VLDB Endowment
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We show that our algorithm outperforms the state-of-the-art static and dynamic SimRank algorithms. ...
In real-life applications, graphs do not only grow in size, requiring fast and precise SimRank computation for large graphs, but also change and evolve continuously over time, demanding an efficient maintenance ...
We implemented our READS (Algorithm 3 static), READS-D (Algorithm 3 dynamic) and READS-Rq (dynamic Algorithm 4 with online walks). ...
doi:10.14778/3099622.3099625
fatcat:ku4zktsdtvcgvok542h5gsgv2m
Cluster Analysis of Stations Based on Weight SimRank in Sharing Bicycle
2021
Converter
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station), and assigned weights to association relationships (the number of times of borrowing and returning) to define the similarity algorithm w-SimRank of stations. ...
in this paper to verify the effectiveness of the W-SimRank algorithm, and analyzed the influence of the key parameters of the algorithm on the algorithm. ...
They use Kronecker and vectorization operators, and propose a new SimRank algorithm for static and dynamic information networks [8] . ...
doi:10.17762/converter.152
fatcat:6uvhvbestzbfxhkureool23jra
Fast Exact CoSimRank Search on Evolving and Static Graphs
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Q on (G ⊕ ∆G) quickly and accurately. To address this issue, we propose a fast accurate dynamic scheme, D-CoSim, for CoSimRank search over evolving graphs. ...
In this study, we propose a fast dynamic scheme, D-CoSim, for accurate CoSimRank search over evolving graphs. ...
CONCLUSIONS This paper presents a dynamic scheme, D-CoSim, for fast accurate CoSimRank retrieval on evolving graphs. ...
doi:10.1145/3178876.3186126
dblp:conf/www/YuW18
fatcat:yj3fzvgpwrg5jjoud7gutydkla
A Space and Time Efficient Algorithm for SimRank Computation
2010
2010 12th International Asia-Pacific Web Conference
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SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. ...
In this paper, we propose novel optimization techniques such that each iteration takes O (min {n · m, n r }) time and O (n + m) space, where m is the number of edges in a web-graph model and r ≤ log 2 ...
Real-life datasets For real datasets, we verified our algorithms over (1) ten-year (from 1998 to 2007) DBLP dataset, and (2) three English Wikipedia category graphs. ...
doi:10.1109/apweb.2010.42
dblp:conf/apweb/YuLL10
fatcat:ktzfyq5jv5ecdgrpn4zfczqoue
A space and time efficient algorithm for SimRank computation
2010
World wide web (Bussum)
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SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. ...
In this paper, we propose novel optimization techniques such that each iteration takes O (min {n · m, n r }) time and O (n + m) space, where m is the number of edges in a web-graph model and r ≤ log 2 ...
Real-life datasets For real datasets, we verified our algorithms over (1) ten-year (from 1998 to 2007) DBLP dataset, and (2) three English Wikipedia category graphs. ...
doi:10.1007/s11280-010-0100-6
fatcat:pirquc2zkfbbbjpkze6yj4ltke
Taming Computational Complexity: Efficient and Parallel SimRank Optimizations on Undirected Graphs
[chapter]
2010
Lecture Notes in Computer Science
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The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efficiency of our proposed techniques. ...
We first present a novel algorithm to estimate the SimRank between vertices in O n 3 + K · n 2 time, where n is the number of vertices, and K is the number of iterations. ...
Acknowledgment We would like to thank the anonymous reviewers for their valuable comments. ...
doi:10.1007/978-3-642-14246-8_29
fatcat:sgjuysmvurgbhpqsaju5pbsvqa
Dynamical SimRank search on time-varying networks
2017
The VLDB journal
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Many real graphs are large, and links are constantly subject to minor changes. In this article, we study the efficient dynamical computation of all-pairs SimRanks on time-varying graphs. ...
Existing methods for the dynamical SimRank computation [e.g., LTSF (Shao et al. in PVLDB 8(8):838-849, 2015) and READS (Zhang et al. in PVLDB 10(5):601-612, 2017)] mainly focus on top-k search with respect ...
In contrast, the precision of Inc-SR-All-P is stable at 1, meaning that it produces the exact SimRank results of [13] , regardless of its top-k size. Thus, Inc-SR-All-P is better for non-top-k query. ...
doi:10.1007/s00778-017-0488-z
fatcat:a3xzy2cfzne2ra3gc7pn3g7wwq
Dynamical SimRank Search on Time-Varying Networks
[article]
2017
arXiv
pre-print
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In this article, we study the efficient dynamical computation of all-pairs SimRanks on time-varying graphs. Li et al.' ...
This provides an incremental method requiring O(Kn^2) time and O(n^2) memory in the worst case to update all pairs of similarities for K iterations. (2) To speed up the computation further, we propose ...
and synthetic datasets, to demonstrate (i) the fast computational time of Inc-SR to incrementally update SimRanks on large time-varying networks, (ii) the pruning power of Inc-SR that can discard unnecessary ...
arXiv:1711.00121v1
fatcat:mphnp4ismbh4np24wz4zknnrrm
Fast incremental SimRank on link-evolving graphs
2014
2014 IEEE 30th International Conference on Data Engineering
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Real graphs are often large, and links constantly evolve with small changes over time. This paper considers fast incremental computations of SimRank on link-evolving graphs. ...
This can further accelerate the incremental SimRank computation to O(K(nd + |AFF|)) time, where d is the average in-degree of the old graph, and |AFF| (≤ n 2 ) is the size of "affected areas" in ∆S, and ...
Recent results on SimRank computation can be distinguished into two broad categories: (i) incremental SimRank on dynamic graphs (e.g., [1] , [8] ), and (ii) batch SimRank on static graphs (e.g., [6] ...
doi:10.1109/icde.2014.6816660
dblp:conf/icde/YuLZ14
fatcat:65bru73y7ndbti42youfmoavma
HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks
[article]
2013
arXiv
pre-print
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Moreover, we analyze the computation characteristics of HeteSim and propose the corresponding quick computation strategies. ...
With the surge of study on heterogeneous networks, the relevance measure on objects with different types becomes increasingly important. ...
QUICK COMPUTATION STRATEGIES AND EXPERIMENTS HeteSim has a high computation demand for time and space. It is not affordable for online query in large-scale information networks. ...
arXiv:1309.7393v1
fatcat:7jexjhhuarg5phxlry5mrx7lhm
Web Pages Ranking Algorithms: A Survey
2021
Qubahan Academic Journal
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Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. ...
to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm ...
Thus, ranking algorithms of the web pages are created to expect the requirements of the user based on a variety of dynamic (such as popularity) and static (such as textual content and hyperlinks count) ...
doi:10.48161/qaj.v1n3a79
fatcat:epf5vkpvd5ebzkfsule5pajsja
Temporal PageRank
[chapter]
2016
Lecture Notes in Computer Science
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PageRank is one of the most popular measures for ranking ...
This work is partially supported by the Academy of Finland project "Nestor" (286211) and the EC H2020 RIA project "SoBigData" (654024). ...
As the size of typical networks has increased significantly over the last years, and as networks tend to grow and evolve fast, research on designing scalable algorithms for computing PageRank is still ...
doi:10.1007/978-3-319-46227-1_42
fatcat:7itvzgdajnd5hk5fygdxrtpyme
The link prediction problem for social networks
2003
Proceedings of the twelfth international conference on Information and knowledge management - CIKM '03
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Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity ...
We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. ...
We thank Jon Herzog, Tommi Jaakkola, Lillian Lee, Frank McSherry, and Grant Wang for helpful discussions and comments on earlier drafts of this paper. ...
doi:10.1145/956863.956972
dblp:conf/cikm/Liben-NowellK03
fatcat:n5mxjlhgyjdpdmvwqsnxo75gfa
The link prediction problem for social networks
2003
Proceedings of the twelfth international conference on Information and knowledge management - CIKM '03
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Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity ...
We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. ...
We thank Jon Herzog, Tommi Jaakkola, Lillian Lee, Frank McSherry, and Grant Wang for helpful discussions and comments on earlier drafts of this paper. ...
doi:10.1145/956958.956972
fatcat:tj47bh7ctzehlmd3qn4gzzylxy
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