LEADER 03089oam 2200517 450 001 996464441403316 005 20210520194148.0 010 $a981-334-022-3 024 7 $a10.1007/978-981-33-4022-0 035 $a(CKB)4100000011610215 035 $a(MiAaPQ)EBC6413286 035 $a(DE-He213)978-981-33-4022-0 035 $a(PPN)252508408 035 $a(EXLCZ)994100000011610215 100 $a20210520d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning in social networks $eembedding nodes, edges, communities, and graphs /$fManasvi Aggarwal and M. N. Murty 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (XI, 112 p. 29 illus., 18 illus. in color.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704 311 $a981-334-021-5 327 $aIntroduction -- Representations of Networks -- Deep Learning -- Node Representations -- Embedding Graphs -- Conclusions. 330 $aThis book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein?protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties. . 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3704 606 $aMachine learning 606 $aArtificial intelligence 606 $aComputational intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 676 $a006.31 700 $aAggarwal$b Manasvi$01217066 702 $aMurty$b M. Narasimha 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a996464441403316 996 $aMachine learning in social networks$92814483 997 $aUNISA