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GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network [article]

Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola
2021 arXiv   pre-print
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.  ...  In this paper, we propose GraphHINGE (Heterogeneous INteract and aggreGatE), which captures and aggregates the interactive patterns between each pair of nodes through their structured neighborhoods.  ...  ACKNOWLEDGMENTS The corresponding author Weinan Zhang thanks the support of Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX0102) and National Natural Science Foundation of China  ... 
arXiv:2011.12683v2 fatcat:nv27cwdpkrblto3fja7wqdnhdy

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph [article]

Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola
2020 arXiv   pre-print
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  ...  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.  ...  The corresponding author Weinan Zhang thanks the support of National Natural Science Foundation of China (Grant No. 61702327, 61772333, 61632017) .  ... 
arXiv:2007.00216v1 fatcat:cruhwo3hdzdmpe5v3cvdboafaa

LUNAR Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network

Deshan Zhou, Shaoliang Peng, Dongqing Wei, Zhong Wu, Yutao Dou, Xiaolan Xie
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance  ...  of the sum of different types of neighborhood information to obtain the representation characteristics of each node.  ...  The flow of our model is as follows: we first formed a heterogeneous network of 12 different types of relationship networks, including drug-drug interaction, drug-structure similarity, drug-disease association  ... 
doi:10.1109/tcbb.2021.3085972 pmid:34081583 pmcid:PMC8769035 fatcat:hlxu773otjeknhkdz7gnjtpczq

NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng, Jonathan Wren
2018 Bioinformatics  
many network-related prediction tasks, we develop a new nonlinear endto-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns  ...  Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of  ...  This work was supported in part by the National Natural Science Foundation of China [61472205, 81630103], China's Youth 1000-Talent Program, Beijing Advanced Innovation Center for Structural Biology.  ... 
doi:10.1093/bioinformatics/bty543 fatcat:4pmchscvrzhjjj5iid3mbz7bkq

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions [article]

Fangping Wan, Lixiang Hong, An Xiao, Tao Jiang, Jianyang Zeng
2018 bioRxiv   pre-print
of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns  ...  Results: Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance  ...  Funding This work was supported in part by the National Natural Science Foundation of China [61472205, 81630103], China's Youth 1000-Talent Program, Beijing Advanced Innovation Center for Structural Biology  ... 
doi:10.1101/261396 fatcat:ziaiqumn2nhylgyvdq3ivs5vhe

GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network

Zhixian Liu, Qingfeng Chen, Wei Lan, Haiming Pan, Xinkun Hao, Shirui Pan
2021 Frontiers in Genetics  
The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing  ...  In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse  ...  NeoDTI (Wan et al., 2019) uses a deep learning method based on neighborhood information aggregation to discover new DTIs.  ... 
doi:10.3389/fgene.2021.650821 pmid:33912218 pmcid:PMC8072283 fatcat:lohwdhopivdonnransj5ktbgqe

Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks [article]

Joshua Melton, Michael Ridenhour, Siddharth Krishnan
2022 arXiv   pre-print
Complex systems, often represented by heterogeneous, multiplex networks present a more difficult challenge for GCN models and require that such techniques capture the diverse contexts and assorted interactions  ...  Our model incorporates node attributes, motif-based features, relation-based GCN approaches, and relational self-attention to learn embeddings of nodes with respect to the various relations in a heterogeneous  ...  that capture the complex structure of attributed heterogeneous multiplex networks.  ... 
arXiv:2203.01903v1 fatcat:4jyfddcwnzarlotbqpkhxxvmu4

GNE: A deep learning framework for gene network inference by aggregating biological information [article]

Kishan KC, Rui Li, Feng Cui, Anne Haake
2018 biorxiv/medrxiv   pre-print
These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling.  ...  The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins.  ...  The interaction datasets for yeast and E. coli were downloaded from BioGRID https://thebiogrid.org.  ... 
doi:10.1101/300996 fatcat:qzynkhdnobfwllxvpdxvfed664

GNE: a deep learning framework for gene network inference by aggregating biological information

Kishan KC, Rui Li, Feng Cui, Qi Yu, Anne R. Haake
2019 BMC Systems Biology  
The topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins.  ...  However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of  ...  The interaction datasets for yeast and E. coli were downloaded from BioGRID https://thebiogrid.org.  ... 
doi:10.1186/s12918-019-0694-y pmid:30953525 pmcid:PMC6449883 fatcat:ivtugel7mnga3pzmsrvve653ii

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage [article]

Siyuan Chen, Jiahai Wang, Xin Du, Yanqing Hu
2020 arXiv   pre-print
Since network structure, profile and content information describe different aspects of users, it is critical to learn effective user representations that integrate heterogeneous information.  ...  The importance of node embeddings and neighborhood embeddings are weighted for final prediction. The proposed method is evaluated on real-world social network data.  ...  Based on the node embeddings, the potential matched neighbors of a given user pair can be identified, and the neighborhood enhancement component, a novel graph neural network model, is applied to learn  ... 
arXiv:2003.07122v1 fatcat:cviulmja4rebhm6oskcrzrjqdu

Latent Influence Based Self-Attention Framework for Heterogeneous Network Embedding

Yang YAN, Qiuyan WANG, Lin LIU
2022 IEICE transactions on information and systems  
To model the heterogeneity and mutual interactions, we redesign the attention mechanism with latent influence factor on single-type relation level, which learns the importance coefficient from its adjacent  ...  relations under complicated network structure.  ...  Acknowledgments This work was supported by the Science & Technology Development Fund of Tianjin Education Commission for Higher Education under grant No. 2020KJ112.  ... 
doi:10.1587/transinf.2021edl8093 fatcat:zwknk4qutbckjkhiyx3zcnu23a

Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers [article]

Shangbin Feng, Zhaoxuan Tan, Rui Li, Minnan Luo
2021 arXiv   pre-print
We then propose relational graph transformers to model heterogeneous influence between users and learn node representations.  ...  Specifically, we construct a heterogeneous information network with users as nodes and diversified relations as edges.  ...  Heterogeneous Information Networks Real-world network data often consist of large quantities of diversified and interactive entities, which can be called heterogeneous information networks (HINs).  ... 
arXiv:2109.02927v3 fatcat:oq5si6mw6jh3xgyts7u7ftap3u

Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction [article]

Fang Zheng, Le Wang, Sanping Zhou, Wei Tang, Zhenxing Niu, Nanning Zheng, Gang Hua
2021 arXiv   pre-print
To address these problems, we propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN), which predicts trajectories of heterogeneous agents in multiple categories.  ...  Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously, which is adaptive to any number of agents and  ...  Our Method In this section, we introduce our proposed UNIN, which aims to model interactions of heterogeneous traffic agents under the guidance of unlimited neighborhood interaction.  ... 
arXiv:2108.00238v3 fatcat:7htq7amps5bbtl4gunsb6galrq

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation [article]

Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang
2020 arXiv   pre-print
NI model is more expressive and can capture more complicated structural patterns behind user-item interactions.  ...  We reveal an early summarization problem in existing graph-based models, and propose Neighborhood Interaction (NI) model to capture each neighbor pair (between user-side and item-side) distinctively.  ...  ACKNOWLEDGMENTS We would like to thank the support of National Natural Science Foundation of China (61632017, 61702327, 61772333), Shanghai Sailing Program (17YF1428200).  ... 
arXiv:1908.04032v2 fatcat:ujccm4mxvre2pcx7yazfpyodsu

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks [article]

Manoj Reddy Dareddy, Mahashweta Das, Hao Yang
2019 arXiv   pre-print
walk to efficiently explore higher order neighborhoods, and then employ heterogeneous skip-gram model to generate the embeddings.  ...  However, most of the methods are not adequate to handle heterogeneous information networks which pretty much represents most real-world data today.  ...  heterogeneous network neighborhood structure and semantics is preserved.  ... 
arXiv:1908.08227v1 fatcat:v2rz4tieyzh4pkr6wajqdfunfq
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