Journal: IEEE Transactions on Knowledge and Data Engineering

Abbreviation

IEEE Trans. Knowl. Data Eng

Publisher

IEEE

Journal Volumes

ISSN

1041-4347
1558-2191

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Publications 1 - 10 of 17
  • SLADE: A smart large-scale task decomposer in crowdsourcing
    Item type: Journal Article
    Tong, Yongxin; Chen, Lei; Zhou, Zimu; et al. (2018)
    IEEE Transactions on Knowledge and Data Engineering
  • Conditional Reliability in Uncertain Graphs
    Item type: Journal Article
    Khan, Arjiit; Bonchi, Francesco; Gullo, Francesco; et al. (2018)
    IEEE Transactions on Knowledge and Data Engineering
  • Main-Memory Hash Joins on Modern Processor Architectures
    Item type: Journal Article
    Balkesen, Cagri; Teubner, Jens; Alonso, Gustavo; et al. (2015)
    IEEE Transactions on Knowledge and Data Engineering
  • Unsupervised Nonnegative Adaptive Feature Extraction for Data Representation
    Item type: Journal Article
    Zhang, Yan; Zhang, Zhao; Li, Sheng; et al. (2019)
    IEEE Transactions on Knowledge and Data Engineering
  • On Modularity Clustering
    Item type: Journal Article
    Brandes, Ulrik; Delling, Daniel; Gaertler, Marco; et al. (2008)
    IEEE Transactions on Knowledge and Data Engineering
  • Making Digital Artifacts on the Web Verifiable and Reliable
    Item type: Journal Article
    Kuhn, Tobias; Dumontier, Michel (2015)
    IEEE Transactions on Knowledge and Data Engineering
  • Using optimistic atomic broadcast in transaction processing systems
    Item type: Journal Article
    Kemme, Bettina; Pedone, Fernando; Alonso, Gustavo; et al. (2003)
    IEEE Transactions on Knowledge and Data Engineering
  • Large Scale Network Embedding: A Separable Approach
    Item type: Journal Article
    Song, Guojie; Zhang, Liang; Li, Ziyao; et al. (2022)
    IEEE Transactions on Knowledge and Data Engineering
    Many successful methods have been proposed for learning low-dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on large-scale or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We compare our SMF framework with approximate SVD algorithms and demonstrate SMF can capture more information when factorizing a given matrix. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to large networks. To further incorporate complex information into SepNE, we discuss several methods that can be used to leverage high-order proximities in large networks. We demonstrate the effectiveness of SepNE on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.
  • High-Level Programming Abstractions for Distributed Graph Processing
    Item type: Journal Article
    Kalavri, Vasiliki; Vlassov, Vladimir; Haridi, Seif (2018)
    IEEE Transactions on Knowledge and Data Engineering
  • Frequent Item Computation on a Chip
    Item type: Journal Article
    Teubner, Jens; Mueller, Rene; Alonso, Gustavo (2011)
    IEEE Transactions on Knowledge and Data Engineering
Publications 1 - 10 of 17