In this paper, we propose a novel collaborative linear manifold learning algorithm CLML to improve the performance of link prediction with auxiliary networks.
Link prediction in heterogeneous networks aims at predicting missing interactions between pairs of nodes with the help of the topology of the target network ...
Feb 1, 2020 · In this paper, we introduce a novel Collaborative Linear Manifold Learning (CLML) algorithm. It can optimize the consistency of nodes ...
Collaborative linear manifold learning for link prediction in ... - X-MOL
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Link prediction in heterogeneous networks aims at predicting missing interactions between pairs of nodes with the help of the topology of the target network ...
Oct 10, 2024 · In this paper, we propose a novel framework of Heterogeneous Hypergraph Representation Learning method (HHRL) to capture high-order interactions for learning ...
Missing: manifold | Show results with:manifold
Abstract—Link prediction is an important task in network analysis, benefiting researchers and organizations in a variety of fields. Many networks in the ...
Read online or download for free from Z-Library the Book: Collaborative linear manifold learning for link prediction in heterogeneous networks, Autor: Liu, ...
Jun 3, 2021 · Collaborative linear manifold learning for link prediction in heterogeneous networks. Abstract. Link prediction in heterogeneous networks aims ...
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Aug 16, 2023 · In this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks ...
Missing: manifold | Show results with:manifold
Mar 27, 2017 · The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity ...
Missing: linear manifold