کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6856694 1437968 2018 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonnegative matrix factorization with mixed hypergraph regularization for community detection
ترجمه فارسی عنوان
فاکتورسازی ماتریس غیر انتزاعی با تنظیم مجدد هیگرافی مخلوط برای تشخیص جامعه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Community structure is the most significant attribute of networks, which is often identified to help discover the underlying organization of networks. Currently, nonnegative matrix factorization (NMF) based community detection method makes use of the related topology information and assumes that networks are able to be projected onto a latent low-dimensional space, in which the nodes can be efficiently clustered. In this paper, we propose a novel framework named mixed hypergraph regularized nonnegative matrix factorization (MHGNMF), which takes higher-order information among the nodes into consideration to enhance the clustering performance. The hypergraph regularization term forces the nodes within the identical hyperedge to be projected onto the same latent subspace, so that a more discriminative representation is achieved. In the proposed framework, we generate a set of hyperedges by mixing two kinds of neighbors for each centroid, which makes full use of topological connection information and structural similarity information. By testing on two artificial benchmarks and eight real-world networks, the proposed framework demonstrates better detection results than the other state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 435, April 2018, Pages 263-281
نویسندگان
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