FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313590945> ?p ?o ?g. }

• W4313590945 endingPage "11273" @default.
• W4313590945 startingPage "11259" @default.
• W4313590945 abstract "Densest subgraphs are often interpreted as <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>communities</i> , based on a basic assumption that the connections inside a community are much denser than those between communities. In a graph with temporal information, a densest periodic subgraph is the most densely connected periodic behavior which needs to be captured. Unfortunately, the existing work do not model the densest periodic subgraph in temporal graphs, and the current algorithms for mining the densest subgraph cannot be applied to detect the densest periodic subgraph in the temporal networks. To tackle this problem, we propose a novel model, called the densest <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$sigma$</tex-math></inline-formula> -periodic subgraph, which presents the densest periodic subgraph whose period size is <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$sigma$</tex-math></inline-formula> . We prove that finding the densest <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$sigma$</tex-math></inline-formula> -periodic subgraph can be solved in polynomial time, but it is still challenging because the naive algorithm needs to repeatedly invoke a maximum flow algorithm for many periodic subgraphs. To compute the densest <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$sigma$</tex-math></inline-formula> -periodic subgraph efficiently, we first develop an effective pruning technique based on the degeneracy of the graph to significantly prune the number of the periodic subgraphs. Then, we present a more efficient algorithm that can reduce the computations for the degeneracy and maximum flow. Next, we develop a greedy algorithm that can compute the approximate densest <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$sigma$</tex-math></inline-formula> -periodic subgraph and achieve an approximation ratio of 1/2. Finally, the results of extensive experiments on several real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms." @default.
• W4313590945 created "2023-01-06" @default.
• W4313590945 creator A5043984767 @default.
• W4313590945 creator A5059652667 @default.
• W4313590945 creator A5073072234 @default.
• W4313590945 creator A5074648621 @default.
• W4313590945 creator A5077534973 @default.
• W4313590945 date "2023-11-01" @default.
• W4313590945 modified "2023-10-15" @default.
• W4313590945 title "Densest Periodic Subgraph Mining on Large Temporal Graphs" @default.
• W4313590945 cites W1500512125 @default.
• W4313590945 cites W1533959098 @default.
• W4313590945 cites W1535144194 @default.
• W4313590945 cites W1888358353 @default.
• W4313590945 cites W1964043357 @default.
• W4313590945 cites W1964669181 @default.
• W4313590945 cites W1977458171 @default.
• W4313590945 cites W2005499394 @default.
• W4313590945 cites W2013469283 @default.
• W4313590945 cites W2032279394 @default.
• W4313590945 cites W2044023374 @default.
• W4313590945 cites W2046598510 @default.
• W4313590945 cites W2055518297 @default.
• W4313590945 cites W2084166631 @default.
• W4313590945 cites W2097805736 @default.
• W4313590945 cites W2119757574 @default.
• W4313590945 cites W2126077878 @default.
• W4313590945 cites W2127758989 @default.
• W4313590945 cites W2153715991 @default.
• W4313590945 cites W2155258238 @default.
• W4313590945 cites W2167598145 @default.
• W4313590945 cites W2218562164 @default.
• W4313590945 cites W2259724869 @default.
• W4313590945 cites W2373263324 @default.
• W4313590945 cites W2528743325 @default.
• W4313590945 cites W2616312054 @default.
• W4313590945 cites W2776104937 @default.
• W4313590945 cites W2795336911 @default.
• W4313590945 cites W2810219908 @default.
• W4313590945 cites W2885685052 @default.
• W4313590945 cites W2888920258 @default.
• W4313590945 cites W2898207411 @default.
• W4313590945 cites W2950140341 @default.
• W4313590945 cites W2951402897 @default.
• W4313590945 cites W2963249925 @default.
• W4313590945 cites W2964670465 @default.
• W4313590945 cites W2967280136 @default.
• W4313590945 cites W2970828623 @default.
• W4313590945 cites W3003413336 @default.
• W4313590945 cites W3012585669 @default.
• W4313590945 cites W3022297969 @default.
• W4313590945 cites W3032557024 @default.
• W4313590945 cites W3035455649 @default.
• W4313590945 cites W3046691040 @default.
• W4313590945 cites W3102074997 @default.
• W4313590945 cites W3102137745 @default.
• W4313590945 cites W3102446989 @default.
• W4313590945 cites W3196565839 @default.
• W4313590945 cites W4253848510 @default.
• W4313590945 doi "https://doi.org/10.1109/tkde.2022.3233788" @default.
• W4313590945 hasPublicationYear "2023" @default.
• W4313590945 type Work @default.
• W4313590945 citedByCount "0" @default.
• W4313590945 crossrefType "journal-article" @default.
• W4313590945 hasAuthorship W4313590945A5043984767 @default.
• W4313590945 hasAuthorship W4313590945A5059652667 @default.
• W4313590945 hasAuthorship W4313590945A5073072234 @default.
• W4313590945 hasAuthorship W4313590945A5074648621 @default.
• W4313590945 hasAuthorship W4313590945A5077534973 @default.
• W4313590945 hasConcept C11413529 @default.
• W4313590945 hasConcept C114614502 @default.
• W4313590945 hasConcept C118615104 @default.
• W4313590945 hasConcept C132525143 @default.
• W4313590945 hasConcept C33923547 @default.
• W4313590945 hasConcept C41008148 @default.
• W4313590945 hasConcept C45357846 @default.
• W4313590945 hasConcept C80444323 @default.
• W4313590945 hasConcept C94375191 @default.
• W4313590945 hasConceptScore W4313590945C11413529 @default.
• W4313590945 hasConceptScore W4313590945C114614502 @default.
• W4313590945 hasConceptScore W4313590945C118615104 @default.
• W4313590945 hasConceptScore W4313590945C132525143 @default.
• W4313590945 hasConceptScore W4313590945C33923547 @default.
• W4313590945 hasConceptScore W4313590945C41008148 @default.
• W4313590945 hasConceptScore W4313590945C45357846 @default.
• W4313590945 hasConceptScore W4313590945C80444323 @default.
• W4313590945 hasConceptScore W4313590945C94375191 @default.
• W4313590945 hasFunder F4320321001 @default.
• W4313590945 hasIssue "11" @default.
• W4313590945 hasLocation W43135909451 @default.
• W4313590945 hasOpenAccess W4313590945 @default.
• W4313590945 hasPrimaryLocation W43135909451 @default.
• W4313590945 hasRelatedWork W149041114 @default.
• W4313590945 hasRelatedWork W1583261078 @default.
• W4313590945 hasRelatedWork W1595229445 @default.
• W4313590945 hasRelatedWork W1965815883 @default.
• W4313590945 hasRelatedWork W2024638892 @default.
• W4313590945 hasRelatedWork W2963177394 @default.

• next