Graph factorization involves decomposing a network into spanning subgraphs or “factors” whose components satisfy specified structural constraints, such as paths, cycles or stars. By systematically ...

Abstract: Incomplete multi-view clustering (IMC) has received increasing attention since missing observations of views are common in real-world applications. Existing approaches often learn similarity ...

Abstract: Nonnegative matrix factorization (NMF) is an effective dimensionality reduction and representation learning technique that captures the intrinsic structure of nonnegative data by learning ...

Graph Factorization embeds communities very closely and keeps leaf nodes far away from other nodes. HOPE embeds nodes with low Katz similarity in the original graph farthest apart (considering dot ...

ABSTRACT: In this paper, we mainly consider characterize all the pairs { R,S } of 2-edge-connected graphs G such that every { R,S } -free graph G has an even factor with exactly two components if and ...

This codebase contains PyTorch implementation of the paper: TuckER: Tensor Factorization for Knowledge Graph Completion. Ivana Balažević, Carl Allen, and Timothy M. Hospedales. Empirical Methods in ...