FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2896852898> ?p ?o ?g. }

• W2896852898 endingPage "1568" @default.
• W2896852898 startingPage "1556" @default.
• W2896852898 abstract "Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network. The vast majority of existing network embedding algorithms, however, are only designed for unsigned networks, and the signed networks containing both positive and negative links, have pretty distinct properties from the unsigned counterpart. In this paper, we propose a deep network embedding model to learn the low-dimensional node vector representations with structural balance preservation for the signed networks. The model employs a semi-supervised stacked auto-encoder to reconstruct the adjacency connections of a given signed network. As the adjacency connections are overwhelmingly positive in the real-world signed networks, we impose a larger penalty to make the auto-encoder focus more on reconstructing the scarce negative links than the abundant positive links. In addition, to preserve the structural balance property of signed networks, we design the pairwise constraints to make the positively connected nodes much closer than the negatively connected nodes in the embedding space. Based on the network representations learned by the proposed model, we conduct link sign prediction and community detection in signed networks. Extensive experimental results in real-world datasets demonstrate the superiority of the proposed model over the state-of-the-art network embedding algorithms for graph representation learning in signed networks." @default.
• W2896852898 created "2018-10-26" @default.
• W2896852898 creator A5043016512 @default.
• W2896852898 creator A5083883714 @default.
• W2896852898 date "2020-04-01" @default.
• W2896852898 modified "2023-10-14" @default.
• W2896852898 title "Deep Network Embedding for Graph Representation Learning in Signed Networks" @default.
• W2896852898 cites W108936587 @default.
• W2896852898 cites W1574010683 @default.
• W2896852898 cites W1966472199 @default.
• W2896852898 cites W1980769375 @default.
• W2896852898 cites W2028513945 @default.
• W2896852898 cites W2031489346 @default.
• W2896852898 cites W2037588580 @default.
• W2896852898 cites W2055269041 @default.
• W2896852898 cites W2062797058 @default.
• W2896852898 cites W2066215526 @default.
• W2896852898 cites W2073415627 @default.
• W2896852898 cites W2080940830 @default.
• W2896852898 cites W2097216034 @default.
• W2896852898 cites W2100026763 @default.
• W2896852898 cites W2105951053 @default.
• W2896852898 cites W2106401878 @default.
• W2896852898 cites W2130354913 @default.
• W2896852898 cites W2142517301 @default.
• W2896852898 cites W2156894402 @default.
• W2896852898 cites W2262682506 @default.
• W2896852898 cites W2321627895 @default.
• W2896852898 cites W2336855346 @default.
• W2896852898 cites W2344138609 @default.
• W2896852898 cites W2393319904 @default.
• W2896852898 cites W2402969480 @default.
• W2896852898 cites W2408719493 @default.
• W2896852898 cites W2514893530 @default.
• W2896852898 cites W2570842749 @default.
• W2896852898 cites W2572767459 @default.
• W2896852898 cites W2605427894 @default.
• W2896852898 cites W2622849676 @default.
• W2896852898 cites W2740997336 @default.
• W2896852898 cites W2766448712 @default.
• W2896852898 cites W2768914708 @default.
• W2896852898 cites W2798989084 @default.
• W2896852898 cites W2801477643 @default.
• W2896852898 cites W2962756421 @default.
• W2896852898 cites W2964140784 @default.
• W2896852898 cites W3104097132 @default.
• W2896852898 cites W3105705953 @default.
• W2896852898 cites W4298299405 @default.
• W2896852898 doi "https://doi.org/10.1109/tcyb.2018.2871503" @default.
• W2896852898 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30307885" @default.
• W2896852898 hasPublicationYear "2020" @default.
• W2896852898 type Work @default.
• W2896852898 sameAs 2896852898 @default.
• W2896852898 citedByCount "64" @default.
• W2896852898 countsByYear W28968528982019 @default.
• W2896852898 countsByYear W28968528982020 @default.
• W2896852898 countsByYear W28968528982021 @default.
• W2896852898 countsByYear W28968528982022 @default.
• W2896852898 countsByYear W28968528982023 @default.
• W2896852898 crossrefType "journal-article" @default.
• W2896852898 hasAuthorship W2896852898A5043016512 @default.
• W2896852898 hasAuthorship W2896852898A5083883714 @default.
• W2896852898 hasBestOaLocation W28968528982 @default.
• W2896852898 hasConcept C108583219 @default.
• W2896852898 hasConcept C110484373 @default.
• W2896852898 hasConcept C11413529 @default.
• W2896852898 hasConcept C127413603 @default.
• W2896852898 hasConcept C132525143 @default.
• W2896852898 hasConcept C154945302 @default.
• W2896852898 hasConcept C184898388 @default.
• W2896852898 hasConcept C2779773260 @default.
• W2896852898 hasConcept C41008148 @default.
• W2896852898 hasConcept C41608201 @default.
• W2896852898 hasConcept C59404180 @default.
• W2896852898 hasConcept C62611344 @default.
• W2896852898 hasConcept C66938386 @default.
• W2896852898 hasConcept C80444323 @default.
• W2896852898 hasConcept C83665646 @default.
• W2896852898 hasConceptScore W2896852898C108583219 @default.
• W2896852898 hasConceptScore W2896852898C110484373 @default.
• W2896852898 hasConceptScore W2896852898C11413529 @default.
• W2896852898 hasConceptScore W2896852898C127413603 @default.
• W2896852898 hasConceptScore W2896852898C132525143 @default.
• W2896852898 hasConceptScore W2896852898C154945302 @default.
• W2896852898 hasConceptScore W2896852898C184898388 @default.
• W2896852898 hasConceptScore W2896852898C2779773260 @default.
• W2896852898 hasConceptScore W2896852898C41008148 @default.
• W2896852898 hasConceptScore W2896852898C41608201 @default.
• W2896852898 hasConceptScore W2896852898C59404180 @default.
• W2896852898 hasConceptScore W2896852898C62611344 @default.
• W2896852898 hasConceptScore W2896852898C66938386 @default.
• W2896852898 hasConceptScore W2896852898C80444323 @default.
• W2896852898 hasConceptScore W2896852898C83665646 @default.
• W2896852898 hasFunder F4320321592 @default.
• W2896852898 hasIssue "4" @default.
• W2896852898 hasLocation W28968528981 @default.
• W2896852898 hasLocation W28968528982 @default.
• W2896852898 hasLocation W28968528983 @default.

• next