Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Editorial Editors' Introduction: Fall 2025(Cambridge Univ Press, 2025-10-28) Dincer, Evren M.; Yukseker, Deniz; Kolluoglu, BirayArticle Citation - WoS: 5Citation - Scopus: 7New Perspectives on Turkey Roundtable on the COVID-19 Pandemic : Prospects for the International Political Economic Order in the Post-Pandemic World(Cambridge Univ Press, 2020-09-21) Bugra, Ayse; Gurkaynak, Refet; Keyder, Caglar; Palat, Ravi Arvind; Pamuk, Sevket; Dincer, Evren M.Article Citation - WoS: 3Citation - Scopus: 3Consensus Embedding for Multiple Networks: Computation and Applications(Cambridge Univ Press, 2022-05-30) Li, Mengzhen; Coskun, Mustafa; Koyuturk, MehmetMachine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are "similar" with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be "multiplex" with multiple types of edges with different semantics. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the performance of three dimensionality reduction methods in computing consensus embeddings on networks with multiple versions: singular value decomposition, variational auto-encoders, and canonical correlation analysis (CCA). Our results show that (i) CCA outperforms other dimensionality reduction methods in computing concensus embeddings, (ii) in the context of link prediction, consensus embeddings can be used to make predictions with accuracy close to that provided by embeddings of integrated networks, and (iii) consensus embeddings can be used to improve the efficiency of combinatorial link prediction queries on multiple networks by multiple orders of magnitude.