Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information Networks [PDF]
Entropy, 2022 The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended.
Xianglin Zuo +4 more
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Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks. [PDF]
PLoS ONE, 2017 Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable
Jibing Wu +5 more
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Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks [PDF]
Molecular Therapy: Nucleic Acids, 2021 Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases.
Ji-Ren Zhou +3 more
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Information Spread and Topic Diffusion in Heterogeneous Information Networks. [PDF]
Sci Rep, 2018 AbstractDiffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges.
Molaei S, Babaei S, Salehi M, Jalili M.
europepmc +4 more sources
FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks. [PDF]
PLoS ONE, 2015 It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the ...
Jing Yang, Limin Chen, Jianpei Zhang
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Graph Filtering for Recommendation on Heterogeneous Information Networks [PDF]
IEEE Access, 2020 Various kinds of auxiliary data in web services have been proved to be valuable to handler data sparsity and cold-start problems of recommendation. However, it is challenging to develop effective approaches to model and utilize these various and complex ...
Chuanyan Zhang, Xiaoguang Hong
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DHNE: Network Representation Learning Method for Dynamic Heterogeneous Networks
IEEE Access, 2019 Analyzing the rich information behind heterogeneous networks through network representation learning methods is signifcant for many application tasks such as link prediction, node classifcation and similarity research.
Ying Yin +3 more
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Measuring diversity in heterogeneous information networks [PDF]
Theoretical Computer Science, 2021 Diversity is a concept relevant to numerous domains of research varying from ecology, to information theory, and to economics, to cite a few. It is a notion that is steadily gaining attention in the information retrieval, network analysis, and artificial neural networks communities.
Ramaciotti Morales, Pedro +5 more
openaire +4 more sources
Temporal Heterogeneous Information Network Embedding [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the ...
Hong Huang 0001 +5 more
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Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [PDF]
Jisuanji kexue, 2022 Most of the information works in real world are heterogeneous information networks (HIN).Network representation methods aiming to represent node data in low dimensional space have been widely used to analyze heterogeneous information networks,so as to ...
JIANG Zong-li, FAN Ke, ZHANG Jin-li
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