ORCID Profile
0000-0002-2200-8711
Current Organisation
Middle East Technical University
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Publisher: ACM
Date: 30-04-2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 07-2011
Publisher: Springer Science and Business Media LLC
Date: 26-11-2010
Publisher: Springer International Publishing
Date: 2014
Publisher: ACM
Date: 29-10-2012
Publisher: IEEE
Date: 07-2008
DOI: 10.1109/WAIM.2008.42
Publisher: Association for Computing Machinery (ACM)
Date: 09-2013
Abstract: Similarity assessment is one of the core tasks in hyperlink analysis. Recently, with the proliferation of applications, e.g. , web search and collaborative filtering, SimRank has been a well-studied measure of similarity between two nodes in a graph. It recursively follows the philosophy that "two nodes are similar if they are referenced (have incoming edges) from similar nodes", which can be viewed as an aggregation of similarities based on incoming paths. Despite its popularity, SimRank has an undesirable property, i.e. , "zero-similarity": It only accommodates paths with equal length from a common "center" node. Thus, a large portion of other paths are fully ignored. This paper attempts to remedy this issue. (1) We propose and rigorously justify SimRank*, a revised version of SimRank, which resolves such counter-intuitive "zero-similarity" issues while inheriting merits of the basic SimRank philosophy. (2) We show that the series form of SimRank* can be reduced to a fairly succinct and elegant closed form, which looks even simpler than SimRank, yet enriches semantics without suffering from increased computational cost. This leads to a fixed-point iterative paradigm of SimRank* in O ( Knm ) time on a graph of n nodes and m edges for K iterations, which is comparable to SimRank. (3) To further optimize SimRank* computation, we leverage a novel clustering strategy via edge concentration. Due to its NP-hardness, we devise an efficient and effective heuristic to speed up SimRank* computation to O ( Kn m) time, where m is generally much smaller than m. (4) Using real and synthetic data, we empirically verify the rich semantics of SimRank*, and demonstrate its high computation efficiency.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2010
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2012
DOI: 10.1142/S021962201240007X
Abstract: We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.
Publisher: Oxford University Press (OUP)
Date: 05-2017
DOI: 10.1104/PP.16.01646
Publisher: Springer Science and Business Media LLC
Date: 11-01-2019
Publisher: Elsevier BV
Date: 12-2014
Publisher: IEEE
Date: 04-2008
Publisher: Springer Science and Business Media LLC
Date: 09-02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
DOI: 10.1109/TKDE.2008.52
Publisher: Springer Science and Business Media LLC
Date: 09-04-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/ICDMW.2013.7
Publisher: Springer International Publishing
Date: 2014
Publisher: ACM
Date: 09-06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 04-09-2010
Publisher: Wiley
Date: 14-10-2010
DOI: 10.1002/SAM.10094
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.55
Publisher: Springer Science and Business Media LLC
Date: 26-04-2015
Publisher: Springer International Publishing
Date: 2015
Publisher: Oxford University Press (OUP)
Date: 14-12-2015
DOI: 10.1104/PP.15.01194
No related grants have been discovered for Jian Pei.