Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313423347> ?p ?o ?g. }
- W4313423347 endingPage "10991" @default.
- W4313423347 startingPage "10981" @default.
- W4313423347 abstract "Heterogeneous social networks, which are characterized by diverse interaction types, have resulted in new challenges for missing link prediction. Most deep learning models tend to capture type-specific features to maximize the prediction performances on specific link types. However, the types of missing links are uncertain in heterogeneous social networks; this restricts the prediction performances of existing deep learning models. To address this issue, we propose a multi-type transferable method ( <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MTTM</i> ) for missing link prediction in heterogeneous social networks, which exploits adversarial neural networks to remain robust against type differences. It comprises a generative predictor and a discriminative classifier. The generative predictor can extract link representations and predict whether the unobserved link is a missing link. To generalize well for different link types to improve the prediction performance, it attempts to deceive the discriminative classifier by learning transferable feature representations among link types. In order not to be deceived, the discriminative classifier attempts to accurately distinguish link types, which indirectly helps the generative predictor judge whether the learned feature representations are transferable among link types. Finally, the integrated <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MTTM</i> is constructed on this minimax two-player game between the generative predictor and discriminative classifier to predict missing links based on transferable feature representations among link types. Extensive experiments show that the proposed <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MTTM</i> can outperform state-of-the-art baselines for missing link prediction in heterogeneous social networks." @default.
- W4313423347 created "2023-01-06" @default.
- W4313423347 creator A5010769217 @default.
- W4313423347 creator A5025938663 @default.
- W4313423347 creator A5053886260 @default.
- W4313423347 creator A5054184997 @default.
- W4313423347 creator A5075107934 @default.
- W4313423347 date "2023-11-01" @default.
- W4313423347 modified "2023-10-15" @default.
- W4313423347 title "A Multi-type Transferable Method for Missing Link Prediction in Heterogeneous Social Networks" @default.
- W4313423347 cites W1964940342 @default.
- W4313423347 cites W1967531152 @default.
- W4313423347 cites W1971421925 @default.
- W4313423347 cites W1977382765 @default.
- W4313423347 cites W2016621483 @default.
- W4313423347 cites W2064503471 @default.
- W4313423347 cites W2101900104 @default.
- W4313423347 cites W2157825442 @default.
- W4313423347 cites W2167467982 @default.
- W4313423347 cites W2521812403 @default.
- W4313423347 cites W2533282150 @default.
- W4313423347 cites W2609338674 @default.
- W4313423347 cites W2736566518 @default.
- W4313423347 cites W2743104969 @default.
- W4313423347 cites W2765811365 @default.
- W4313423347 cites W2809645418 @default.
- W4313423347 cites W2892341857 @default.
- W4313423347 cites W2908732234 @default.
- W4313423347 cites W2941287638 @default.
- W4313423347 cites W2945266622 @default.
- W4313423347 cites W2951050019 @default.
- W4313423347 cites W2952343887 @default.
- W4313423347 cites W2962756421 @default.
- W4313423347 cites W2963707260 @default.
- W4313423347 cites W2963919031 @default.
- W4313423347 cites W2965857891 @default.
- W4313423347 cites W2986416218 @default.
- W4313423347 cites W2995868056 @default.
- W4313423347 cites W3004507689 @default.
- W4313423347 cites W3006318668 @default.
- W4313423347 cites W3012871709 @default.
- W4313423347 cites W3037988456 @default.
- W4313423347 cites W3080152140 @default.
- W4313423347 cites W3082268164 @default.
- W4313423347 cites W3093861821 @default.
- W4313423347 cites W3100863972 @default.
- W4313423347 cites W3134531423 @default.
- W4313423347 cites W3156968278 @default.
- W4313423347 cites W3172515294 @default.
- W4313423347 cites W3204140782 @default.
- W4313423347 cites W4205911334 @default.
- W4313423347 doi "https://doi.org/10.1109/tkde.2022.3233481" @default.
- W4313423347 hasPublicationYear "2023" @default.
- W4313423347 type Work @default.
- W4313423347 citedByCount "15" @default.
- W4313423347 countsByYear W43134233472023 @default.
- W4313423347 crossrefType "journal-article" @default.
- W4313423347 hasAuthorship W4313423347A5010769217 @default.
- W4313423347 hasAuthorship W4313423347A5025938663 @default.
- W4313423347 hasAuthorship W4313423347A5053886260 @default.
- W4313423347 hasAuthorship W4313423347A5054184997 @default.
- W4313423347 hasAuthorship W4313423347A5075107934 @default.
- W4313423347 hasBestOaLocation W43134233472 @default.
- W4313423347 hasConcept C108583219 @default.
- W4313423347 hasConcept C119857082 @default.
- W4313423347 hasConcept C154945302 @default.
- W4313423347 hasConcept C167966045 @default.
- W4313423347 hasConcept C2778827112 @default.
- W4313423347 hasConcept C39890363 @default.
- W4313423347 hasConcept C41008148 @default.
- W4313423347 hasConcept C9357733 @default.
- W4313423347 hasConcept C95623464 @default.
- W4313423347 hasConcept C97931131 @default.
- W4313423347 hasConceptScore W4313423347C108583219 @default.
- W4313423347 hasConceptScore W4313423347C119857082 @default.
- W4313423347 hasConceptScore W4313423347C154945302 @default.
- W4313423347 hasConceptScore W4313423347C167966045 @default.
- W4313423347 hasConceptScore W4313423347C2778827112 @default.
- W4313423347 hasConceptScore W4313423347C39890363 @default.
- W4313423347 hasConceptScore W4313423347C41008148 @default.
- W4313423347 hasConceptScore W4313423347C9357733 @default.
- W4313423347 hasConceptScore W4313423347C95623464 @default.
- W4313423347 hasConceptScore W4313423347C97931131 @default.
- W4313423347 hasFunder F4320321001 @default.
- W4313423347 hasFunder F4320324775 @default.
- W4313423347 hasIssue "11" @default.
- W4313423347 hasLocation W43134233471 @default.
- W4313423347 hasLocation W43134233472 @default.
- W4313423347 hasOpenAccess W4313423347 @default.
- W4313423347 hasPrimaryLocation W43134233471 @default.
- W4313423347 hasRelatedWork W2093104230 @default.
- W4313423347 hasRelatedWork W2128027845 @default.
- W4313423347 hasRelatedWork W2593887162 @default.
- W4313423347 hasRelatedWork W2751624083 @default.
- W4313423347 hasRelatedWork W2987280934 @default.
- W4313423347 hasRelatedWork W4281766347 @default.
- W4313423347 hasRelatedWork W4294238563 @default.
- W4313423347 hasRelatedWork W4365211920 @default.