Matches in SemOpenAlex for { <https://semopenalex.org/work/W3200609053> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W3200609053 endingPage "284" @default.
- W3200609053 startingPage "265" @default.
- W3200609053 abstract "Dynamic networks are popularly used to describe networks that change with time. Although there have been a large number of research works on understanding dynamic networks using link prediction, node classification and community detection, there is rare work that is specially designed to address the challenge of big network size of dynamic networks. To this end, we study in this paper an emerging and challenging problem of network coarsening in dynamic networks. Network coarsening refers to a class of network “zoom-out” operations where node pairs and edges are grouped together for efficient analysis on big networks. However, existing network coarsening approaches can only handle static networks where network structure weights have been predefined before the coarsening calculation. Under the observation that big networks are highly dynamic and naturally change over time, we consider in this paper to embed information diffusion data which reflect the dynamics of networks for network coarsening. Specifically, we present a new Semi-NetCoarsen approach that jointly maximizes the likelihood of observing the information diffusion data and minimizes the network regularization with respect to the predefined network structural data. The learning function is convex and we use the accelerated proximal gradient algorithm to obtain the global optimal solution. We conduct experiments on two synthetic and five real-world data sets to validate the performance of the proposed method." @default.
- W3200609053 created "2021-09-27" @default.
- W3200609053 creator A5010058972 @default.
- W3200609053 creator A5026161579 @default.
- W3200609053 creator A5029360035 @default.
- W3200609053 creator A5032277491 @default.
- W3200609053 creator A5047118636 @default.
- W3200609053 creator A5061330948 @default.
- W3200609053 creator A5085319576 @default.
- W3200609053 date "2021-11-01" @default.
- W3200609053 modified "2023-10-17" @default.
- W3200609053 title "Towards embedding information diffusion data for understanding big dynamic networks" @default.
- W3200609053 cites W155548076 @default.
- W3200609053 cites W1981377410 @default.
- W3200609053 cites W1984069252 @default.
- W3200609053 cites W1992537918 @default.
- W3200609053 cites W1996816151 @default.
- W3200609053 cites W1999170091 @default.
- W3200609053 cites W2008620264 @default.
- W3200609053 cites W2033389579 @default.
- W3200609053 cites W2036120890 @default.
- W3200609053 cites W2042991924 @default.
- W3200609053 cites W2061820396 @default.
- W3200609053 cites W2068510557 @default.
- W3200609053 cites W2079065101 @default.
- W3200609053 cites W2094013877 @default.
- W3200609053 cites W2100556411 @default.
- W3200609053 cites W2108119513 @default.
- W3200609053 cites W2117289809 @default.
- W3200609053 cites W2127492100 @default.
- W3200609053 cites W2130983041 @default.
- W3200609053 cites W2144309280 @default.
- W3200609053 cites W2151936673 @default.
- W3200609053 cites W2155033583 @default.
- W3200609053 cites W2169876669 @default.
- W3200609053 cites W2545868467 @default.
- W3200609053 cites W2569283211 @default.
- W3200609053 cites W3126076156 @default.
- W3200609053 doi "https://doi.org/10.1016/j.neucom.2021.09.024" @default.
- W3200609053 hasPublicationYear "2021" @default.
- W3200609053 type Work @default.
- W3200609053 sameAs 3200609053 @default.
- W3200609053 citedByCount "0" @default.
- W3200609053 crossrefType "journal-article" @default.
- W3200609053 hasAuthorship W3200609053A5010058972 @default.
- W3200609053 hasAuthorship W3200609053A5026161579 @default.
- W3200609053 hasAuthorship W3200609053A5029360035 @default.
- W3200609053 hasAuthorship W3200609053A5032277491 @default.
- W3200609053 hasAuthorship W3200609053A5047118636 @default.
- W3200609053 hasAuthorship W3200609053A5061330948 @default.
- W3200609053 hasAuthorship W3200609053A5085319576 @default.
- W3200609053 hasConcept C124101348 @default.
- W3200609053 hasConcept C127413603 @default.
- W3200609053 hasConcept C13540734 @default.
- W3200609053 hasConcept C154945302 @default.
- W3200609053 hasConcept C31258907 @default.
- W3200609053 hasConcept C41008148 @default.
- W3200609053 hasConcept C41608201 @default.
- W3200609053 hasConcept C62611344 @default.
- W3200609053 hasConcept C66938386 @default.
- W3200609053 hasConcept C75684735 @default.
- W3200609053 hasConceptScore W3200609053C124101348 @default.
- W3200609053 hasConceptScore W3200609053C127413603 @default.
- W3200609053 hasConceptScore W3200609053C13540734 @default.
- W3200609053 hasConceptScore W3200609053C154945302 @default.
- W3200609053 hasConceptScore W3200609053C31258907 @default.
- W3200609053 hasConceptScore W3200609053C41008148 @default.
- W3200609053 hasConceptScore W3200609053C41608201 @default.
- W3200609053 hasConceptScore W3200609053C62611344 @default.
- W3200609053 hasConceptScore W3200609053C66938386 @default.
- W3200609053 hasConceptScore W3200609053C75684735 @default.
- W3200609053 hasFunder F4320321001 @default.
- W3200609053 hasLocation W32006090531 @default.
- W3200609053 hasOpenAccess W3200609053 @default.
- W3200609053 hasPrimaryLocation W32006090531 @default.
- W3200609053 hasRelatedWork W2055709700 @default.
- W3200609053 hasRelatedWork W2368437561 @default.
- W3200609053 hasRelatedWork W2548059104 @default.
- W3200609053 hasRelatedWork W2794527914 @default.
- W3200609053 hasRelatedWork W2805960763 @default.
- W3200609053 hasRelatedWork W2894330012 @default.
- W3200609053 hasRelatedWork W2901726430 @default.
- W3200609053 hasRelatedWork W3210598353 @default.
- W3200609053 hasRelatedWork W4220722750 @default.
- W3200609053 hasRelatedWork W786186891 @default.
- W3200609053 hasVolume "466" @default.
- W3200609053 isParatext "false" @default.
- W3200609053 isRetracted "false" @default.
- W3200609053 magId "3200609053" @default.
- W3200609053 workType "article" @default.