arXiv:2606. 01546v1 Announce Type: new Abstract: Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces a degree‑corrected joint matrix factorization technique for detecting communities in multilayer networks. It uses a nonnegative symmetric matrix trifactorization that enforces disjoint, shared communities across layers while allowing each layer to have distinct connectivity patterns and node degrees. An efficient algorithm is presented and evaluated on a multilayer degree‑corrected stochastic block model, showing superior performance compared to existing methods.
The paper introduces the Marcus mapping, an extension of Marcus theorem that allows certain sparse symmetric matrices to be transformed into doubly stochastic symmetric matrices via diagonal matrices. Leveraging this mapping, the authors propose the Doubly Stochastic Adaptive Neighbors Clustering algorithm (ANCMM), which incorporates rank constraints to ensure the learned similarity graph naturally partitions into the desired number of clusters. Experiments demonstrate ANCMM’s effectiveness compared to state‑of‑the‑art methods, and the authors also establish a connection between the Marcus mapping and a specific optimal transport problem.
arXiv:2608.21607v1 Announce Type: cross Abstract: We investigate when a sparse nonnegative matrix can be recovered from a real-valued matrix of much lower rank by zeroing out its negative elements. T...
arXiv:2608.29362v1 Announce Type: cross Abstract: Sparse computations are fundamental to scientific computing, graph analytics, and machine learning, yet their performance is highly sensitive to the...
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.
arXiv:2607. 21039v1 Announce Type: new Abstract: Spectral methods are among the most widely used techniques for community detection, clustering, and graph learning.