Search Efficient Binary Network Embedding
نویسندگان
چکیده
Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving structure and/or node content information, such that off-the-shelf machine algorithms can be easily applied to the vector-format representations analysis. However, learned are inefficient large-scale similarity search, which often involves finding nearest neighbors measured by distance or in space. In this article, we propose search efficient binary algorithm called BinaryNE learn code simultaneously modeling context relations and attribute through three-layer neural network. learns using stochastic gradient descent-based online algorithm. The encoding not only reduces memory usage represent but also allows fast bit-wise comparisons support faster than Euclidean other measures. Extensive experiments demonstrate delivers more 25 times speed, provides comparable better quality traditional based methods. codes render competitive performance classification clustering tasks. source of is available at https://github.com/daokunzhang/BinaryNE.
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ژورنال
عنوان ژورنال: ACM Transactions on Knowledge Discovery From Data
سال: 2021
ISSN: ['1556-472X', '1556-4681']
DOI: https://doi.org/10.1145/3436892