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An Effective Two-way Metapath Encoder over Heterogeneous Information Network for Recommendation
2022
Proceedings of the 2022 International Conference on Multimedia Retrieval
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To tackle these limitations, we propose a novel recommendation model with two-way metapath encoder for top-N recommendation, which models metapath similarity and sequence relation dependency in HIN to ...
Heterogeneous information networks (HINs) are widely used in recommender system research due to their ability to model complex auxiliary information beyond historical interactions to alleviate data sparsity ...
National Natural Science Foundation of China (61762078, 61363058, 61966004, U1811264, 61966009), Gansu Natural Science Foundation Project (21JR7RA114), Research Fund of Guangxi Key Lab of Multi-source Information ...
doi:10.1145/3512527.3531402
fatcat:zn7o7j6xyrb7zdkjormf5nw434
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
[article]
2021
arXiv
pre-print
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To address these limitations, we propose a Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN) to improve performance. ...
Specially, MHN employs node base embedding to encapsulate node attributes, BFS and DFS neighbors aggregation within a metapath to capture local and global information, and metapaths aggregation to combine ...
HERec [11] proposes an embedding method for heterogeneous graph and applies to the recommendation scene by matrix factorization. ...
arXiv:2103.06474v1
fatcat:rt7lwzapebccrg3ta4zrztwc4u
Semantic Path-Based Learning for Review Volume Prediction
[chapter]
2020
Lecture Notes in Computer Science
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In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared ...
Graphs offer a natural abstraction for modeling complex real-world systems where entities are represented as nodes and edges encode relations between them. ...
Hu et al. use an exhaustive list of semantically-meaningful metapaths for extracting Top-N recommendations with a neural co-attention network [10] . ...
doi:10.1007/978-3-030-45439-5_54
fatcat:tvfnbiz3xffudfdjvzsarpd7na
Research on multi-role classification task of online mall based on heterogeneous graph neural network
2024
Applied and Computational Engineering
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Therefore, multi-role task classification of online shopping malls based on heterogeneous graph neural networks is of great significance for improving the user experience and recommendation effect of online ...
We found that the classification model based on heterogeneous graph can effectively classify multiple roles in the online mall to provide personalized services and recommendations for the mall. ...
In fact, the network pattern is an abstraction for better understanding and analysis of heterogeneous graphs, focusing on the local structure of the graph. Definition 3: Metapath. ...
doi:10.54254/2755-2721/39/20230581
fatcat:ymdrsc277nh5dageyvdoic72ea
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
[article]
2020
arXiv
pre-print
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There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. ...
On the other hand, other graph-based methods aim to learn effective heterogeneous network representations by compressing node together with its neighborhood information into single embedding before prediction ...
The second way is meta path-based, which builds heterogeneous information network (HIN) on the side information. ...
arXiv:2007.00216v1
fatcat:cruhwo3hdzdmpe5v3cvdboafaa
HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks
[article]
2022
arXiv
pre-print
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Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. ...
information. ...
For the Kuaishou dataset, the metapaths take a comparatively balanced effect while the random sampled path takes an auxiliary effect. ...
arXiv:2208.02068v1
fatcat:tnvdwgtijzcwfdy3kqz7mryeba
Metapath- and Entity-aware Graph Neural Network for Recommendation
[article]
2021
arXiv
pre-print
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We propose metaPath and Entity-Aware Graph Neural Network (PEAGNN), which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. ...
., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights leads to inadequate distillation of collaborative signals. ...
To overcome these limitations, we propose MetaPath-and Entity-Aware Graph Neural Network (PEAGNN), a unified GNN framework, which aggregates information over multiple metapath-aware subgraphs and fuse ...
arXiv:2010.11793v3
fatcat:xn6j6yhyevdcdmssxpgmo4cwcq
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
[article]
2022
arXiv
pre-print
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Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied ...
To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning. ...
ACKNOWLEDGMENTS To Robert, for the bagels and explaining CMYK and color spaces. ...
arXiv:2211.11752v1
fatcat:ladn4xdjjbfv5dcpl6co3s5qee
Factor graph-aggregated heterogeneous network embedding for disease-gene association prediction
2021
BMC Bioinformatics
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This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous ...
It also has good interpretability and can be extended to large-scale biomedical network data analysis. ...
the experimental results. • We designed a deep learning model for heterogeneous network link prediction, which can effectively learn rich topological information and semantic information in heterogeneous ...
doi:10.1186/s12859-021-04099-3
pmid:33781206
fatcat:pzxugjqhvvdp3a3jyaigktzlui
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
[article]
2020
arXiv
pre-print
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2020
2020 IEEE International Conference on Knowledge Graph (ICKG)
unpublished
doi:10.1109/icbk50248.2020.00076 fatcat:tae2tubkkjfc5ltb3zxgt6mk2m
doi:10.1109/icbk50248.2020.00076 fatcat:tae2tubkkjfc5ltb3zxgt6mk2m
The method organizes metapath guided random walks to create heterogeneous neighborhood for all node types in the network. ...
In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. ...
The optimization allows the metapath-guided random walks carry out on the tripartite network to handle imbalanced node numbers for an effective link prediction. ...
arXiv:2010.06816v1
fatcat:hckr4hofjnghtkofemmk3dpohy
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
[article]
2021
arXiv
pre-print
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Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. ...
module to deal with different metapaths. ...
The second way is meta path-based, which builds heterogeneous information network (HIN) on the side information. ...
arXiv:2011.12683v2
fatcat:nv27cwdpkrblto3fja7wqdnhdy
Heterogeneous Information Network Embedding for Recommendation
[article]
2017
arXiv
pre-print
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Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called ...
It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. ...
., convolutional neural network, auto encoder) to exploit text information [26] , image information [27] and network structure information [28] for better recommendation. ...
arXiv:1711.10730v1
fatcat:g3z5i6gnd5aljeyscma2cco64m
Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning
2022
Sensors
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In this paper, we propose a novel framework named community-aware dynamic heterogeneous graph embedding (CDHNE) for relationship assessment, capable of mining heterogeneous information, latent community ...
In order to uncover the temporal evolutionary patterns, we devise an encoder–decoder structure, containing multiple recurrent memory units, which helps to capture the dynamics for relation assessment efficiently ...
encoding component scarcely take effects. ...
doi:10.3390/s22041402
pmid:35214304
pmcid:PMC8963052
fatcat:h5553hhrkjh3nnnlavy7k6trmq
A Bird's-Eye Tutorial of Graph Attention Architectures
[article]
2022
arXiv
pre-print
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Graph Neural Networks (GNNs) have shown tremendous strides in performance for graph-structured problems especially in the domains of natural language processing, computer vision and recommender systems ...
Incorporating "attention" into graph mining has been viewed as a way to overcome the noisiness, heterogenity and complexity associated with graph-structured data as well as to encode soft-inductive bias ...
Choi for their useful suggestions. The figures of the animated characters are sourced from the webpages of (inline linked) Jerry, Tom, mixed mouse and Minnie. ...
arXiv:2206.02849v1
fatcat:o3xi6bmlrfgyvlhjrmqo4qc34m
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
[chapter]
2018
Lecture Notes in Computer Science
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A metagraph contains multiple paths between nodes, each describing one type of relationships, so the augmentation of multiple metapaths provides an effective way to capture rich contexts and semantic relations ...
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. ...
This work is partially supported by the Australian Research Council (ARC) under discovery grant DP140100545, and by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions ...
doi:10.1007/978-3-319-93037-4_16
fatcat:jqrx2jllfvb45dtw5hlv3smkbi
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