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Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
[article]
2018
arXiv
pre-print
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Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks,where a meta-graph is a composition of meta-paths that captures the complex structural information ...
The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta information of ...
In this paper, we think the HIN with different views such as meta-paths and meta-graph, and fuse the different information for node embedding. ...
arXiv:1809.04110v1
fatcat:l6wd5ytqizamvgpsgi754yeorm
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research. ...
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. ...
It also utilizes the high-level semantic attention to differentiate and aggregate information from different meta paths. Heterogeneous Graph Transformer. ...
doi:10.24963/ijcai.2020/677
dblp:conf/ijcai/DongHWS020
fatcat:srd5r66dovefrpa5drxmrek25e
Heterogeneous Graph Attention Network
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1903.07293v1 fatcat:zporjhmmsnasrdaew7m25cwy7i
The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. ...
However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. ...
ACKNOWLEDGMENTS This work is supported in part by the National Natural Science Foundation of China (No. 61702296, 61772082, 61532006) , the Beijing Municipal Natural Science Foundation (4182043), and ...
arXiv:1903.07293v2
fatcat:wrwdajlnm5bahhh26ngd5z7pfe
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
[article]
2021
arXiv
pre-print
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Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. ...
In this work, we propose \ours for joint learning of cluster embeddings as well as cluster-aware HIN embedding. ...
A Heterogeneous Information Network or HIN is defined as a graph G = {V, E, A, R, , } with the sets of nodes and edges represented by V and E respectively. ...
arXiv:2108.03953v1
fatcat:xde5gvcgxzejpf7rhnuljqjzcq
Attention-Guide Walk Model in Heterogeneous Information Network for Multi-Style Recommendation Explanation
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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the heterogeneous information network. ...
Too many interactive factors between users and items can be used to interpret the recommendation in a heterogeneous information network. ...
Acknowledgments This work was supported by a grant from the National Natural Science Foundation of China under grants (No. 61602057, No. 61872161 and No. 61976103 ...
doi:10.1609/aaai.v34i04.6095
fatcat:mb636x2jbbhgxhxjyzxihj53w4
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
[article]
2019
arXiv
pre-print
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Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph ...
Heterogeneous information network (HIN) embedding has gained increasing interests recently. ...
heterogeneous networks to demonstrate that the proposed methods considerably outperform both conventional homogeneous embedding and heterogeneous meta-path/meta-graph guided embedding methods in two HIN ...
arXiv:1909.03228v1
fatcat:uzpzogehuvhovd2tfsmcf7gutm
Network Schema Preserving Heterogeneous Information Network Embedding
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. ...
As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous ...
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2020/190
dblp:conf/ijcai/ZhaoWSLY20
fatcat:32hdufftejd75e2lbbnqrlizdi
Personalized Scientific Paper Recommendation based on Heterogeneous Graph Representation
2019
IEEE Access
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INDEX TERMS Recommender systems, paper recommendation, heterogeneous information networks, graph representation, meta-paths. ...
Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. ...
Definition 3 ( 3 Heterogeneous Information Network Representation Learning): For a given network G(V , E), V and E represent the set of nodes and edges, respectively. ...
doi:10.1109/access.2019.2923293
fatcat:owaxatica5fd7paux4hgneb5ym
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations ...
Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. ...
Graph Neural Networks (GNNs) More recently, graph neural networks (
Conclusion The paper proposes HetSANN to perform meta-path-free embedding based on structural information in heterogeneous graphs ...
arXiv:1912.10832v1
fatcat:trv7zyezzjf2dc6gr7xs4ukeoi
An Attention-Based Graph Neural Network for Heterogeneous Structural Learning
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations ...
Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. ...
Conclusion The paper proposes HetSANN to perform meta-path-free embedding based on structural information in heterogeneous graphs. ...
doi:10.1609/aaai.v34i04.5833
fatcat:pztajppn4jaehn7kapyw54qnra
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
[article]
2016
arXiv
pre-print
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The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. ...
We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. ...
Acknowledgement We would like to thank anonymous reviewers for helpful suggestions. This work is partially supported by NSF CAREER #1453800. ...
arXiv:1612.02814v2
fatcat:g5lhuqmqpnfjjjckbkaeaiojfe
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
[article]
2022
arXiv
pre-print
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user-item paths induced by meta-paths. ...
Accordingly, the rich semantics reflected by social relationships and item categories, which lie in the recommendation data-based heterogeneous graphs, are not fully exploited. ...
As a typical instantiation of heterogeneous graphs, meta-path [19] is widely used to capture the semantics reflected from complex information. ...
arXiv:2203.03982v1
fatcat:bgevmwxadnanva666ut24lzonq
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
[article]
2021
arXiv
pre-print
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On the other hand, current heterogeneous graph neural networks cannot be simply used on an EHR graph because of the existence of hub nodes in it. ...
The preprocessing method normalizes edges and splits the EHR graph into multiple homogeneous graphs while each homogeneous graph contains partial information of the original EHR graph. ...
It exploits meta-paths and employs a joint embedding framework to predict diagnosis for patients. We use the same graph structure on this model as HSGNN. • MAGNN [20] . ...
arXiv:2101.06800v1
fatcat:d3e2xvjebbc7dh7yk6hfclibau
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification
[article]
2019
arXiv
pre-print
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We formulate the program reidentification as a graph classification problem and develop an effective attentional heterogeneous graph embedding algorithm to solve it. ...
In this paper, we propose an attentional heterogeneous graph neural network model (DeepHGNN) to verify the program's identity based on its system behaviors. ...
a heterogeneous information network into a multi-channel graph; • We propose a heterogeneous graph neural network based approach to learn the graph embedding via propagating the contextual information ...
arXiv:1812.04064v2
fatcat:7vujchgqbfgenjskt66njvgsye
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
[article]
2022
arXiv
pre-print
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Learning knowledge representation of scientific paper data is a problem to be solved, and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this ...
Based on the heterogeneous graph representation, this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes, that is, the relationship ...
This paper constructs a heterogeneous graph including 3025 papers (P), 5835 authors (A), and 56 topics (S). Experiment with the meta-path set {PAP, PSP}. ...
arXiv:2203.16751v1
fatcat:xtelw33xjbdwpmukle5m3zvrr4
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