Spectral redemption in clustering sparse networks
نویسندگان
چکیده
منابع مشابه
Spectral redemption: clustering sparse networks
SFI Working Papers contain accounts of scienti5ic work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-‐reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited v...
متن کاملSpectral redemption in clustering sparse networks.
Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here, we present a class of spectral algorithms based on a nonbacktracking walk on the directed ...
متن کاملHighly Sparse Reductions to Kernel Spectral Clustering
Kernel spectral clustering is a model-based spectral clustering method formulated in a primal-dual framework. It has a powerful out-of-sample extension property and a model selection procedure based on the balanced line fit criterion. This paper is an improvement of a previous work which sparsified the kernel spectral clustering method using the line structure of the data projections in the eig...
متن کاملSpectral Clustering in Heterogeneous Networks
Many real-world systems consist of several types of entities, and heterogeneous networks are required to represent such systems. However, the current statistical toolbox for network data can only deal with homogeneous networks, where all nodes are supposed to be of the same type. This article introduces a statistical framework for community detection in heterogeneous networks. For modeling hete...
متن کاملSpectral community detection in sparse networks
Spectral methods based on the eigenvectors of matrices are widely used in the analysis of network data, particularly for community detection and graph partitioning. Standard methods based on the adjacency matrix and related matrices, however, break down for very sparse networks, which includes many networks of practical interest. As a solution to this problem it has been recently proposed that ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 2013
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.1312486110