LEADER 01376nam2 22003011i 450 001 UON00001742 005 20231205101852.231 100 $a20020107d1990 |0itac50 ba 101 $amul 102 $aIL 105 $a|||| 1|||| 200 1 $aRuth Amiran Volume$fed. A. Eitan, R. Gophna, M. Kochavi 210 $aJerusalem$cIsrael Exploration Society$d1990 215 $aVIII, 110, 258 p., p. di tav.$cill.$d27 cm 461 1$1001UON00001633$12001 $aEretz-Israel$eArchaeological, Historical and Geographical Studies$1210 $aJerusalem$cThe Israel Exploration Society ; in cooperation with The Institute of Archaeology, Hebrew University$d1951- $1215 $a v.$d27 cm$v21 606 $aSTUDI EBRAICI$3UONC003707$2FI 620 $aIL$dY?r?sh?layim$3UONL004472 686 $aSEB GEN D X$cSTUDI EBRAICI - STUDI IN ONORE DI - ARCHEOLOGIA$2A 702 1$aEITAN$bA.$3UONV008360 702 1$aGOPHNA$bR.$3UONV008361 702 1$aKOCHAVI$bMoshe$3UONV008362 712 $a*Israel *Exploration *Society$3UONV002152$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00001742 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI SEB GEN D X 002 (21) $eSI SA 68278 7 002 (21) 996 $aRuth Amiran Volume$91209346 997 $aUNIOR LEADER 04564nam 22006495 450 001 9910743356503321 005 20251113203755.0 010 $a981-16-6166-9 010 $a981-16-6165-0 010 $a981-16-6166-9 024 7 $a10.1007/978-981-16-6166-2 035 $a(MiAaPQ)EBC6876633 035 $a(Au-PeEL)EBL6876633 035 $a(CKB)21028245100041 035 $a(PPN)269154809 035 $a(OCoLC)1298387696 035 $a(DE-He213)978-981-16-6166-2 035 $a(EXLCZ)9921028245100041 100 $a20220130d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHeterogeneous Graph Representation Learning and Applications /$fby Chuan Shi, Xiao Wang, Philip S. Yu 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (329 pages) 225 1 $aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 311 08$aPrint version: Shi, Chuan Heterogeneous Graph Representation Learning and Applications Singapore : Springer Singapore Pte. Limited,c2022 9789811661655 320 $aIncludes bibliographical references. 327 $aIntroduction -- The State-of-the-art of Heterogeneous Graph Representation -- Part One: Techniques -- Structure-preserved Heterogeneous Graph Representation -- Attribute-assisted Heterogeneous Graph Representation -- Dynamic Heterogeneous Graph Representation -- Supplementary of Heterogeneous Graph Representation -- Part Two: Applications -- Heterogeneous Graph Representation for Recommendation -- Heterogeneous Graph Representation for Text Mining -- Heterogeneous Graph Representation for Industry Application -- Future Research Directions -- Conclusion. . 330 $aRepresentation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. Moreimportantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning. 410 0$aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 606 $aData mining 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aData Mining and Knowledge Discovery 606 $aMachine Learning 606 $aData Science 615 0$aData mining. 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 14$aData Mining and Knowledge Discovery. 615 24$aMachine Learning. 615 24$aData Science. 676 $a511.5 700 $aShi$b Chuan$0964112 702 $aWang$b Xiao 702 $aYu$b Philip S. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910743356503321 996 $aHeterogeneous graph representation learning and applications$93558732 997 $aUNINA