Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893695676> ?p ?o ?g. }
- W2893695676 endingPage "1968" @default.
- W2893695676 startingPage "1954" @default.
- W2893695676 abstract "Structural clustering is a fundamental graph mining operator which is not only able to find densely-connected clusters, but it can also identify hub vertices and outliers in the graph. Previous structural clustering algorithms are tailored to deterministic graphs. Many real-world graphs, however, are not deterministic, but are probabilistic in nature because the existence of the edge is often inferred using a variety of statistical approaches. In this paper, we formulate the problem of structural clustering on probabilistic graphs, with the aim of finding reliable clusters in a given probabilistic graph. Unlike the traditional structural clustering problem, our problem relies mainly on a novel concept called reliable structural similarity which measures the probability of the similarity between two vertices in the probabilistic graph. We develop a dynamic programming algorithm with several powerful pruning strategies to efficiently compute the reliable structural similarities. With the reliable structural similarities, we adapt an existing solution framework to calculate the structural clustering on probabilistic graphs. Comprehensive experiments on five real-life datasets demonstrate the effectiveness and efficiency of the proposed approaches." @default.
- W2893695676 created "2018-10-05" @default.
- W2893695676 creator A5038832637 @default.
- W2893695676 creator A5047828703 @default.
- W2893695676 creator A5054991337 @default.
- W2893695676 creator A5070014390 @default.
- W2893695676 creator A5074090518 @default.
- W2893695676 creator A5075642293 @default.
- W2893695676 creator A5091368933 @default.
- W2893695676 date "2019-10-01" @default.
- W2893695676 modified "2023-10-17" @default.
- W2893695676 title "Efficient Structural Clustering on Probabilistic Graphs" @default.
- W2893695676 cites W143174683 @default.
- W2893695676 cites W1490591387 @default.
- W2893695676 cites W1596588646 @default.
- W2893695676 cites W1603304162 @default.
- W2893695676 cites W1709660540 @default.
- W2893695676 cites W1904294951 @default.
- W2893695676 cites W1951702208 @default.
- W2893695676 cites W1997987084 @default.
- W2893695676 cites W1999446611 @default.
- W2893695676 cites W2007954020 @default.
- W2893695676 cites W2017987256 @default.
- W2893695676 cites W2023536175 @default.
- W2893695676 cites W2029682635 @default.
- W2893695676 cites W2047940964 @default.
- W2893695676 cites W2055245094 @default.
- W2893695676 cites W2057272970 @default.
- W2893695676 cites W2061820396 @default.
- W2893695676 cites W2066303519 @default.
- W2893695676 cites W2070593017 @default.
- W2893695676 cites W2080182143 @default.
- W2893695676 cites W2095293504 @default.
- W2893695676 cites W2101434765 @default.
- W2893695676 cites W2116007667 @default.
- W2893695676 cites W2116117181 @default.
- W2893695676 cites W2119757574 @default.
- W2893695676 cites W2121947440 @default.
- W2893695676 cites W2129256050 @default.
- W2893695676 cites W2133238553 @default.
- W2893695676 cites W2134008243 @default.
- W2893695676 cites W2134737843 @default.
- W2893695676 cites W2137442080 @default.
- W2893695676 cites W2139694815 @default.
- W2893695676 cites W2142599626 @default.
- W2893695676 cites W2148606196 @default.
- W2893695676 cites W2151936673 @default.
- W2893695676 cites W2158501189 @default.
- W2893695676 cites W2160313295 @default.
- W2893695676 cites W2233456153 @default.
- W2893695676 cites W2243560673 @default.
- W2893695676 cites W2432087854 @default.
- W2893695676 cites W2436321460 @default.
- W2893695676 cites W2440113929 @default.
- W2893695676 cites W2595853853 @default.
- W2893695676 cites W2962936633 @default.
- W2893695676 doi "https://doi.org/10.1109/tkde.2018.2872553" @default.
- W2893695676 hasPublicationYear "2019" @default.
- W2893695676 type Work @default.
- W2893695676 sameAs 2893695676 @default.
- W2893695676 citedByCount "18" @default.
- W2893695676 countsByYear W28936956762019 @default.
- W2893695676 countsByYear W28936956762020 @default.
- W2893695676 countsByYear W28936956762021 @default.
- W2893695676 countsByYear W28936956762022 @default.
- W2893695676 countsByYear W28936956762023 @default.
- W2893695676 crossrefType "journal-article" @default.
- W2893695676 hasAuthorship W2893695676A5038832637 @default.
- W2893695676 hasAuthorship W2893695676A5047828703 @default.
- W2893695676 hasAuthorship W2893695676A5054991337 @default.
- W2893695676 hasAuthorship W2893695676A5070014390 @default.
- W2893695676 hasAuthorship W2893695676A5074090518 @default.
- W2893695676 hasAuthorship W2893695676A5075642293 @default.
- W2893695676 hasAuthorship W2893695676A5091368933 @default.
- W2893695676 hasConcept C108010975 @default.
- W2893695676 hasConcept C124101348 @default.
- W2893695676 hasConcept C154945302 @default.
- W2893695676 hasConcept C24404364 @default.
- W2893695676 hasConcept C41008148 @default.
- W2893695676 hasConcept C49937458 @default.
- W2893695676 hasConcept C6557445 @default.
- W2893695676 hasConcept C73555534 @default.
- W2893695676 hasConcept C80444323 @default.
- W2893695676 hasConcept C86803240 @default.
- W2893695676 hasConcept C94641424 @default.
- W2893695676 hasConceptScore W2893695676C108010975 @default.
- W2893695676 hasConceptScore W2893695676C124101348 @default.
- W2893695676 hasConceptScore W2893695676C154945302 @default.
- W2893695676 hasConceptScore W2893695676C24404364 @default.
- W2893695676 hasConceptScore W2893695676C41008148 @default.
- W2893695676 hasConceptScore W2893695676C49937458 @default.
- W2893695676 hasConceptScore W2893695676C6557445 @default.
- W2893695676 hasConceptScore W2893695676C73555534 @default.
- W2893695676 hasConceptScore W2893695676C80444323 @default.
- W2893695676 hasConceptScore W2893695676C86803240 @default.
- W2893695676 hasConceptScore W2893695676C94641424 @default.
- W2893695676 hasFunder F4320321001 @default.
- W2893695676 hasFunder F4320323110 @default.