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Статті в журналах з теми "Heterogeneous neural networks"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Zeng, Wei, Ge Fan, Shan Sun, Biao Geng, Weiyi Wang, Jiacheng Li, and Weibo Liu. "Collaborative filtering via heterogeneous neural networks." Applied Soft Computing 109 (September 2021): 107516. http://dx.doi.org/10.1016/j.asoc.2021.107516.

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2

Drakopoulos, John A., and Ahmad Abdulkader. "Training neural networks with heterogeneous data." Neural Networks 18, no. 5-6 (July 2005): 595–601. http://dx.doi.org/10.1016/j.neunet.2005.06.011.

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3

Turner, Andrew James, and Julian Francis Miller. "NeuroEvolution: Evolving Heterogeneous Artificial Neural Networks." Evolutionary Intelligence 7, no. 3 (November 2014): 135–54. http://dx.doi.org/10.1007/s12065-014-0115-5.

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4

Zhang, Chen, Zhouhua Tang, Bin Yu, Yu Xie, and Ke Pan. "Deep heterogeneous network embedding based on Siamese Neural Networks." Neurocomputing 388 (May 2020): 1–11. http://dx.doi.org/10.1016/j.neucom.2020.01.012.

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5

Sun, Yizhou, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. "Heterogeneous information networks." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3807–11. http://dx.doi.org/10.14778/3554821.3554901.

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In 2011, we proposed PathSim to systematically define and compute similarity between nodes in a heterogeneous information network (HIN), where nodes and links are from different types. In the PathSim paper, we for the first time introduced HIN with general network schema and proposed the concept of meta-paths to systematically define new relation types between nodes. In this paper, we summarize the impact of PathSim paper in both academia and industry. We start from the algorithms that are based on meta-path-based feature engineering, then move on to the recent development in heterogeneous network representation learning, including both shallow network embedding and heterogeneous graph neural networks. In the end, we make the connection between knowledge graphs and HINs and discuss the implication of meta-paths in the symbolic reasoning scenario. Finally, we point out several future directions.

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6

Iddianozie, Chidubem, and Gavin McArdle. "Towards Robust Representations of Spatial Networks Using Graph Neural Networks." Applied Sciences 11, no. 15 (July 27, 2021): 6918. http://dx.doi.org/10.3390/app11156918.

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The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

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7

Gracious, Tony, Shubham Gupta, Arun Kanthali, Rui M. Castro, and Ambedkar Dukkipati. "Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4054–62. http://dx.doi.org/10.1609/aaai.v35i5.16526.

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Although static networks have been extensively studied in machine learning, data mining, and AI communities for many decades, the study of dynamic networks has recently taken center stage due to the prominence of social media and its effects on the dynamics of social networks. In this paper, we propose a statistical model for dynamically evolving networks, together with a variational inference approach. Our model, Neural Latent Space Model with Variational Inference, encodes edge dependencies across different time snapshots. It represents nodes via latent vectors and uses interaction matrices to model the presence of edges. These matrices can be used to incorporate multiple relations in heterogeneous networks by having a separate matrix for each of the relations. To capture the temporal dynamics, both node vectors and interaction matrices are allowed to evolve with time. Existing network analysis methods use representation learning techniques for modelling networks. These techniques are different for homogeneous and heterogeneous networks because heterogeneous networks can have multiple types of edges and nodes as opposed to a homogeneous network. Unlike these, we propose a unified model for homogeneous and heterogeneous networks in a variational inference framework. Moreover, the learned node latent vectors and interaction matrices may be interpretable and therefore provide insights on the mechanisms behind network evolution. We experimented with a single step and multi-step link forecasting on real-world networks of homogeneous, bipartite, and heterogeneous nature, and demonstrated that our model significantly outperforms existing models.

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8

Wu, Nan, and Chaofan Wang. "Ensemble Graph Attention Networks." Transactions on Machine Learning and Artificial Intelligence 10, no. 3 (June 12, 2022): 29–41. http://dx.doi.org/10.14738/tmlai.103.12399.

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Graph neural networks have demonstrated its success in many applications on graph-structured data. Many efforts have been devoted to elaborating new network architectures and learning algorithms over the past decade. The exploration of applying ensemble learning techniques to enhance existing graph algorithms have been overlooked. In this work, we propose a simple generic bagging-based ensemble learning strategy which is applicable to any backbone graph models. We then propose two ensemble graph neural network models – Ensemble-GAT and Ensemble-HetGAT by applying the ensemble strategy to the graph attention network (GAT), and a heterogeneous graph attention network (HetGAT). We demonstrate the effectiveness of the proposed ensemble strategy on GAT and HetGAT through comprehensive experiments with four real-world homogeneous graph datasets and three real-world heterogeneous graph datasets on node classification tasks. The proposed Ensemble-GAT and Ensemble-HetGAT outperform the state-of-the-art graph neural network and heterogeneous graph neural network models on most of the benchmark datasets. The proposed ensemble strategy also alleviates the over-smoothing problem in GAT and HetGAT.

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9

Son, Ha Min, Moon Hyun Kim, and Tai-Myoung Chung. "Comparisons Where It Matters: Using Layer-Wise Regularization to Improve Federated Learning on Heterogeneous Data." Applied Sciences 12, no. 19 (October 3, 2022): 9943. http://dx.doi.org/10.3390/app12199943.

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Federated Learning is a widely adopted method for training neural networks over distributed data. One main limitation is the performance degradation that occurs when data are heterogeneously distributed. While many studies have attempted to address this problem, a more recent understanding of neural networks provides insight to an alternative approach. In this study, we show that only certain important layers in a neural network require regularization for effective training. We additionally verify that Centered Kernel Alignment (CKA) most accurately calculates similarities between layers of neural networks trained on different data. By applying CKA-based regularization to important layers during training, we significantly improved performances in heterogeneous settings. We present FedCKA, a simple framework that outperforms previous state-of-the-art methods on various deep learning tasks while also improving efficiency and scalability.

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10

Hosny, Khalid M., Marwa M. Khashaba, Walid I. Khedr, and Fathy A. Amer. "An Efficient Neural Network-Based Prediction Scheme for Heterogeneous Networks." International Journal of Sociotechnology and Knowledge Development 12, no. 2 (April 2020): 63–76. http://dx.doi.org/10.4018/ijskd.2020040104.

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In mobile wireless networks, the challenge of providing full mobility without affecting the quality of service (QoS) is becoming essential. These challenges can be overcome using handover prediction. The process of determining the next station which mobile user desires to transfer its data connection can be termed as handover prediction. A new proposed prediction scheme is presented in this article dependent on scanning all signal quality between the mobile user and all neighboring stations in the surrounding areas. Additionally, the proposed scheme efficiency is enhanced essentially for minimizing the redundant handover (unnecessary handovers) numbers. Both WLAN and long term evolution (LTE) networks are used in the proposed scheme which is evaluated using various scenarios with several numbers and locations of mobile users and with different numbers and locations of WLAN access point and LTE base station, all randomly. The proposed prediction scheme achieves a success rate of up to 99% in several scenarios consistent with LTE-WLAN architecture. To understand the network characteristics for enhancing efficiency and increasing the handover successful percentage especially with mobile station high speeds, a neural network model is used. Using the trained network, it can predict the next target station for heterogeneous network handover points. The proposed neural network-based scheme added a significant improvement in the accuracy ratio compared to the existing schemes using only the received signal strength (RSS) as a parameter in predicting the next station. It achieves a remarkable improvement in successful percentage ratio up to 5% compared with using only RSS.

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11

Moreno-Alvarez, Sergio, Juan M. Haut, Mercedes E. Paoletti, and Juan A. Rico-Gallego. "Heterogeneous model parallelism for deep neural networks." Neurocomputing 441 (June 2021): 1–12. http://dx.doi.org/10.1016/j.neucom.2021.01.125.

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12

Zhao, Jianan, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye. "Heterogeneous Graph Structure Learning for Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4697–705. http://dx.doi.org/10.1609/aaai.v35i5.16600.

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Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i.e., the original heterogeneous graph structure is reliable. However, this assumption is usually unrealistic, since the heterogeneous graph in reality is inevitably noisy or incomplete. Therefore, it is vital to learn the heterogeneous graph structure for HGNNs rather than rely only on the raw graph structure. In light of this, we make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameters learning for classification task. Different from traditional GSL on homogeneous graph, considering the heterogeneity of different relations in heterogeneous graph, HGSL generates each relation subgraph independently. Specifically, in each generated relation subgraph, HGSL not only considers the feature similarity by generating feature similarity graph, but also considers the complex heterogeneous interactions in features and semantics by generating feature propagation graph and semantic graph. Then, these graphs are fused to a learned heterogeneous graph and optimized together with a GNN towards classification objective. Extensive experiments on real-world graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods.

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13

Uchikoba, Fumio, Minami Kaneko, and Ken Saito. "Heterogeneous Integration on Neural Networks IC Mounted MEMS Microrobot." Journal of Japan Institute of Electronics Packaging 20, no. 6 (2017): 376–81. http://dx.doi.org/10.5104/jiep.20.376.

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14

Yang, Xiaocheng, Mingyu Yan, Shirui Pan, Xiaochun Ye, and Dongrui Fan. "Simple and Efficient Heterogeneous Graph Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10816–24. http://dx.doi.org/10.1609/aaai.v37i9.26283.

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Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) designed for homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. In this paper, we conduct an in-depth and detailed study of these mechanisms and propose the Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of a simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.

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15

Martin Happ, Matthias Herlich, Christian Maier, Jia Lei Du, and Peter Dorfinger. "Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies." ITU Journal on Future and Evolving Technologies 2, no. 4 (June 25, 2021): 1–8. http://dx.doi.org/10.52953/tejx5530.

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Modeling communication networks to predict performance such as delay and jitter is important for evaluating and optimizing them. In recent years, neural networks have been used to do this, which may have advantages over existing models, for example from queueing theory. One of these neural networks is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions. One key simplification is the restriction to a single scheduling policy, which describes how packets of different flows are prioritized for transmission. In this paper we propose a solution that supports multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can handle mixed scheduling policies in a single communication network. Our solution is based on the RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute percentage error under 1% with our extended model on the evaluation data set from the challenge. This takes neural-network-based delay estimation one step closer to practical use.

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16

Saxena, Shruti, and Joydeep Chandra. "SAlign: A Graph Neural Attention Framework for Aligning Structurally Heterogeneous Networks." Journal of Artificial Intelligence Research 77 (July 12, 2023): 949–69. http://dx.doi.org/10.1613/jair.1.14427.

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Network alignment techniques that map the same entities across multiple networks assume that the mapping nodes in two different networks have similar attributes and neighborhood proximity. However, real-world networks often violate such assumptions, having diverse attributes and structural properties. Node mapping across such structurally heterogeneous networks remains a challenge. Although capturing the nodes’ entire neighborhood (in low-dimensional embeddings) may help deal with these characteristic differences, the issue of over-smoothing in the representations that come from higherorder learning still remains a major problem. To address the above concerns, we propose SAlign: a supervised graph neural attention framework for aligning structurally heterogeneous networks that learns the correlation of structural properties of mapping nodes using a set of labeled (mapped) anchor nodes. SAlign incorporates nodes’ graphlet information with a novel structure-aware cross-network attention mechanism that transfers the required higher-order structure information across networks. The information exchanged across networks helps in enhancing the expressivity of the graph neural network, thereby handling any potential over-smoothing problem. Extensive experiments on three real datasets demonstrate that SAlign consistently outperforms the state-of-the-art network alignment methods by at least 1.3-8% in terms of accuracy score. The code is available at https://github.com/shruti400/SAlign for reproducibility.

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17

Ma, Lili, Jiangping Liu, and Jidong Luo. "Method of Wireless Sensor Network Data Fusion." International Journal of Online Engineering (iJOE) 13, no. 09 (September 22, 2017): 114. http://dx.doi.org/10.3991/ijoe.v13i09.7589.

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<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm. Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.</span></span></p>

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18

Yuan, Peisen, Yi Sun, and Hengliang Wang. "Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search." Mathematics 10, no. 16 (August 12, 2022): 2895. http://dx.doi.org/10.3390/math10162895.

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Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning tasks on heterogeneous information networks have a large number of parameters and neural network architectures that need to be set artificially. The main goal is to find the optimal hyperparameter settings and neural network architectures for the performance of a task in the set of hyperparameter space. To address this problem, we propose a metapath search method for heterogeneous information networks based on a network architecture search, which can search for metapaths that are more suitable for different heterogeneous information networks and recommendation tasks. We conducted experiments on Amazon and Yelp datasets and compared the architecture settings obtained from an automatic search with manually set structures to verify the effectiveness of the algorithm.

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19

Yang, Zan, Xiaoxin Zhang, and Yuping Zhao. "Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network." Wireless Personal Communications 68, no. 3 (December 24, 2011): 993–1012. http://dx.doi.org/10.1007/s11277-011-0495-0.

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20

Ma, Minglin, Kangling Xiong, Zhijun Li, and Yichuang Sun. "Dynamic Behavior Analysis and Synchronization of Memristor-Coupled Heterogeneous Discrete Neural Networks." Mathematics 11, no. 2 (January 10, 2023): 375. http://dx.doi.org/10.3390/math11020375.

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Continuous memristors have been widely studied in recent years; however, there are few studies on discrete memristors in the field of neural networks. In this paper, a four-stable locally active discrete memristor (LADM) is proposed as a synapse, which is used to connect a two-dimensional Chialvo neuron and a three-dimensional KTZ neuron, and construct a simple heterogeneous discrete neural network (HDNN). Through a bifurcation diagram and Lyapunov exponents diagram, the period and chaotic regions of the discrete neural network model are shown. Through numerical analysis, it was found that the chaotic region and periodic region of the neural network based on DLAM are significantly improved. In addition, coexisting chaos and chaos attractors, coexisting periodic and chaotic attractors, and coexisting periodic and periodic attractors will appear when the initial value of the LADM is changed. Coupled by a LADM synapse, two heterogeneous discrete neurons are gradually synchronized by changing the coupling strength. This paper lays a good foundation for the future analysis of LADMs and the related research of discrete neural networks coupled by LADMs.

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21

Lu, Guangquan, Jiecheng Li, and Jian Wei. "Aspect sentiment analysis with heterogeneous graph neural networks." Information Processing & Management 59, no. 4 (July 2022): 102953. http://dx.doi.org/10.1016/j.ipm.2022.102953.

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22

Zhang, Mengmei, Xiao Wang, Meiqi Zhu, Chuan Shi, Zhiqiang Zhang, and Jun Zhou. "Robust Heterogeneous Graph Neural Networks against Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4363–70. http://dx.doi.org/10.1609/aaai.v36i4.20357.

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Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. However, despite their wide use, there is currently no understanding of their robustness to adversarial attacks. In this work, we first systematically study the robustness of HGNNs and show that they can be easily fooled by adding the adversarial edge between the target node and large-degree node (i.e., hub). Furthermore, we show two key reasons for such vulnerability of HGNNs: one is perturbation enlargement effect, i.e., HGNNs, failing to encode transiting probability, will enlarge the effect of the adversarial hub in comparison of GCNs, and the other is soft attention mechanism, i.e., such mechanism assigns positive attention values to obviously unreliable neighbors. Based on the two facts, we propose a novel robust HGNN framework RoHe against topology adversarial attacks by equipping an attention purifier, which can prune malicious neighbors based on topology and feature. Specifically, to eliminate the perturbation enlargement, we introduce the metapath-based transiting probability as the prior criterion of the purifier, restraining the confidence of malicious neighbors from the adversarial hub. Then the purifier learns to mask out neighbors with low confidence, thus can effectively alleviate the negative effect of malicious neighbors in the soft attention mechanism. Extensive experiments on different benchmark datasets for multiple HGNNs are conducted, where the considerable improvement of HGNNs under adversarial attacks will demonstrate the effectiveness and generalization ability of our defense framework.

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23

Timofeev. "Theory and modeling for heterogeneous polynomial neural networks." SPIIRAS Proceedings, no. 4 (March 17, 2014): 73. http://dx.doi.org/10.15622/sp.4.4.

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24

Casilari, E., A. Jurado, G. Pansard, A. Díaz-Estrella, and F. Sandoval. "Modelling aggregate heterogeneous ATM sources using neural networks." Electronics Letters 32, no. 4 (1996): 363. http://dx.doi.org/10.1049/el:19960273.

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25

Chavez, M., M. Besserve, and M. Le Van Quyen. "Dynamics of excitable neural networks with heterogeneous connectivity." Progress in Biophysics and Molecular Biology 105, no. 1-2 (March 2011): 29–33. http://dx.doi.org/10.1016/j.pbiomolbio.2010.11.002.

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26

Leon, Florin, and Mihai Horia Zaharia. "Stacked Heterogeneous Neural Networks for Time Series Forecasting." Mathematical Problems in Engineering 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/373648.

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A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

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27

Tsuji, Masayuki, Teijiro Isokawa, Takayuki Yumoto, Nobuyuki Matsui, and Naotake Kamiura. "Heterogeneous recurrent neural networks for natural language model." Artificial Life and Robotics 24, no. 2 (November 23, 2018): 245–49. http://dx.doi.org/10.1007/s10015-018-0507-1.

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28

Ozcan, Alper, and Sule Gunduz Oguducu. "Link prediction in evolving heterogeneous networks using the NARX neural networks." Knowledge and Information Systems 55, no. 2 (July 11, 2017): 333–60. http://dx.doi.org/10.1007/s10115-017-1073-x.

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29

SCHLIEBS, STEFAN, NIKOLA KASABOV, and MICHAËL DEFOIN-PLATEL. "ON THE PROBABILISTIC OPTIMIZATION OF SPIKING NEURAL NETWORKS." International Journal of Neural Systems 20, no. 06 (December 2010): 481–500. http://dx.doi.org/10.1142/s0129065710002565.

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The construction of a Spiking Neural Network (SNN), i.e. the choice of an appropriate topology and the configuration of its internal parameters, represents a great challenge for SNN based applications. Evolutionary Algorithms (EAs) offer an elegant solution for these challenges and methods capable of exploring both types of search spaces simultaneously appear to be the most promising ones. A variety of such heterogeneous optimization algorithms have emerged recently, in particular in the field of probabilistic optimization. In this paper, a literature review on heterogeneous optimization algorithms is presented and an example of probabilistic optimization of SNN is discussed in detail. The paper provides an experimental analysis of a novel Heterogeneous Multi-Model Estimation of Distribution Algorithm (hMM-EDA). First, practical guidelines for configuring the method are derived and then the performance of hMM-EDA is compared to state-of-the-art optimization algorithms. Results show hMM-EDA as a light-weight, fast and reliable optimization method that requires the configuration of only very few parameters. Its performance on a synthetic heterogeneous benchmark problem is highly competitive and suggests its suitability for the optimization of SNN.

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30

Zhong, Hongwei, Mingyang Wang, and Xinyue Zhang. "HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network." Electronics 12, no. 9 (May 6, 2023): 2124. http://dx.doi.org/10.3390/electronics12092124.

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Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.

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31

Liu, Zhenguo, Chao Ma, Jimiao Zhao, Haiyang Hu, and Xiongyi Yin. "Graph Transformation Based on Heterogeneous Information Network for Graph Algorithms." Journal of Physics: Conference Series 2575, no. 1 (August 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2575/1/012006.

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Abstract In the finical risk control, the graph structure data increasingly show its unique charm, especially the heterogeneous information network (HIN). And, graph computing algorithms for the data mining based on network is the most popular way at the moment. But, most of them require the graph network must have homogeneity and could not be applied to heterogeneous networks. Although the neural network models have some work in the HIN, which like heterogeneous graph neural networks, but these ways cannot provide enough interpretability due to its operations in black-box. In this paper, we summarize strategies to transform the HIN into homogeneous network. And we propose methods aim to rebuild HIN as several homogeneous networks at fine-grained level and greatly retain the original network topological structure information compared the previous which easy to lose sight of. The effectiveness of our method is verified by real data and community detection algorithms. In the experiment, the analysis found that our approached consistently perform promising results compared with the coarse-grained data processing on HIN.

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32

Liao, Shengbin, Xiaofeng Wang, and ZongKai Yang. "A heterogeneous two-stream network for human action recognition." AI Communications 36, no. 3 (August 21, 2023): 219–33. http://dx.doi.org/10.3233/aic-220188.

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The most widely used two-stream architectures and building blocks for human action recognition in videos generally consist of 2D or 3D convolution neural networks. 3D convolution can abstract motion messages between video frames, which is essential for video classification. 3D convolution neural networks usually obtain good performance compared with 2D cases, however it also increases computational cost. In this paper, we propose a heterogeneous two-stream architecture which incorporates two convolutional networks. One uses a mixed convolution network (MCN), which combines some 3D convolutions in the middle of 2D convolutions to train RGB frames, another one adopts BN-Inception network to train Optical Flow frames. Considering the redundancy of neighborhood video frames, we adopt a sparse sampling strategy to decrease the computational cost. Our architecture is trained and evaluated on the standard video actions benchmarks of HMDB51 and UCF101. Experimental results show our approach obtains the state-of-the-art performance on the datasets of HMDB51 (73.04%) and UCF101 (95.27%).

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33

Bulygin, M. V., M. M. Gayanova, A. M. Vulfin, A. D. Kirillova, and R. Ch Gayanov. "Convolutional neural network in the images colorization problem." Information Technology and Nanotechnology, no. 2416 (2019): 340–53. http://dx.doi.org/10.18287/1613-0073-2019-2416-340-353.

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Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.

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34

Aldana, Carlos I., Rodrigo Munguía, Emmanuel Cruz-Zavala, and Emmanuel Nuño. "Pose Consensus of Multiple Robots with Time-Delays Using Neural Networks." Robotica 37, no. 5 (January 15, 2019): 883–905. http://dx.doi.org/10.1017/s0263574718001388.

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SummaryThis paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in theSE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.

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35

Li, Ji-chao, Dan-ling Zhao, Bing-Feng Ge, Ke-Wei Yang, and Ying-Wu Chen. "A link prediction method for heterogeneous networks based on BP neural network." Physica A: Statistical Mechanics and its Applications 495 (April 2018): 1–17. http://dx.doi.org/10.1016/j.physa.2017.12.018.

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36

Hah, Jen M., Po L. Tien, and Maria C. Yuang. "Neural-network-based call admission control in ATM networks with heterogeneous arrivals." Computer Communications 20, no. 9 (September 1997): 732–40. http://dx.doi.org/10.1016/s0140-3664(97)00101-1.

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37

Balakrishnan, Mathiarasi, and Geetha T. V. "A neural network framework for predicting dynamic variations in heterogeneous social networks." PLOS ONE 15, no. 4 (April 27, 2020): e0231842. http://dx.doi.org/10.1371/journal.pone.0231842.

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38

Zhang, Quan, Hongyu Li, Xueting Lu, and Chunliu Wu. "A Neural Network-Based Approach to Multi-Attribute Group Decision-Making with Heterogeneous Preference Information." Scientific Programming 2022 (August 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/9033237.

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For the problem of multi-attribute group decision-making with heterogeneous preference information on attribute values and overall preference orderings on alternatives, this article proposes a neural network-based approach. In the approach, firstly, the heterogeneous preference information on attribute values and overall preference orderings on alternatives are normalized. Secondly, based on the normalization results, two optimization models are set up to determine attribute weights and expert weights, respectively. Thirdly, two neural networks are set up and trained to determine attribute weights and expert weights based on the optimization models. Then, the overall values of the alternatives are obtained as well as their rankings. Simulations on the proposed neural networks are conducted for illustrations.

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39

Lavrenkov, Yuriy N. "Heterogeneous convolutional neural networks to build movement routes of the objects in spatial environment with accumulated energy potential." Journal Of Applied Informatics 16, no. 93 (June 29, 2021): 21–37. http://dx.doi.org/10.37791/2687-0649-2021-16-3-21-37.

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We consider the synthesis of a hybrid neural convolutional network with the modular topology-based architecture, which allows to arrange a parallel convolutional computing system to combine both the energy transfer and data processing, in order to simulate complex functions of natural biological neural populations. The system of interlayer neural commutation, based on the distributed resonance circuits with the layers of electromagnetic metamaterial between the inductive elements, is a base for simulation of the interaction between the astrocyte networks and the neural clusters responsible for information processing. Consequently, the data processing is considered both at the level of signal transmission through neural elements, and as interaction of artificial neurons and astrocytic networks ensuring their functioning. The resulting two-level neural system of data processing implements a set of measures to solve the issue based on the neural network committee. The specific arrangement of the neural network enables us to implement and configure the educational procedure using the properties absent in the neural networks consisting of neural populations only. The training of the convolutional network is based on a preliminary analysis of rhythmic activity, where artificial astrocytes play the main role of interneural switches. The analysis of the signals moving through the neural network enables us to adjust variable components to present information from training bunches in the available memory circuits in the most efficient way. Moreover, in the training process we observe the activity of neurons in various areas to evenly distribute the computational load on neural network modules to achieve maximum performance. The trained and formed convolutional network is used to solve the problem of determining the optimal path for the object moving due to the energy from the environment

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40

Dong, Xinrui, Yijia Zhang, Kuo Pang, Fei Chen, and Mingyu Lu. "Heterogeneous graph neural networks with denoising for graph embeddings." Knowledge-Based Systems 238 (February 2022): 107899. http://dx.doi.org/10.1016/j.knosys.2021.107899.

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41

Darshan, Ran, and Alexander Rivkind. "Learning to represent continuous variables in heterogeneous neural networks." Cell Reports 39, no. 1 (April 2022): 110612. http://dx.doi.org/10.1016/j.celrep.2022.110612.

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42

Heller, Guillaume, Eric Perrin, Valeriu Vrabie, Cedric Dusart, and Solen Le Roux. "Grafting Heterogeneous Neural Networks for a Hierarchical Object Classification." IEEE Access 10 (2022): 12927–40. http://dx.doi.org/10.1109/access.2022.3144579.

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43

Deng, Zhongying, Xiaojiang Peng, Zhifeng Li, and Yu Qiao. "Mutual Component Convolutional Neural Networks for Heterogeneous Face Recognition." IEEE Transactions on Image Processing 28, no. 6 (June 2019): 3102–14. http://dx.doi.org/10.1109/tip.2019.2894272.

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44

Riggan, Benjamin S., Christopher Reale, and Nasser M. Nasrabadi. "Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition." IEEE Access 3 (2015): 1620–32. http://dx.doi.org/10.1109/access.2015.2479620.

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45

Cho, Hyunjoong, Jinhyeok Jang, Chanhyeok Lee, and Seungjoon Yang. "Efficient architecture for deep neural networks with heterogeneous sensitivity." Neural Networks 134 (February 2021): 95–106. http://dx.doi.org/10.1016/j.neunet.2020.10.017.

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46

Li, Tao, Ting Wang, Guobao Zhang, and Shumin Fei. "Master–slave synchronization of heterogeneous dimensional delayed neural networks." Neurocomputing 205 (September 2016): 498–506. http://dx.doi.org/10.1016/j.neucom.2016.04.051.

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47

Christou, Vasileios, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas, and Gavin Brown. "Hybrid extreme learning machine approach for heterogeneous neural networks." Neurocomputing 361 (October 2019): 137–50. http://dx.doi.org/10.1016/j.neucom.2019.04.092.

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48

Knudstrup, Scott, Michal Zochowski, and Victoria Booth. "Synaptic loss and synaptic plasticity in heterogeneous neural networks." Journal of Complex Networks 4, no. 1 (April 23, 2015): 115–26. http://dx.doi.org/10.1093/comnet/cnv011.

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49

Yi-zhen, ZHENG, DAI Jian, ZHANG Tian, and XU Kun. "Multimodal feature fusion based on heterogeneous optical neural networks." Chinese Optics 16 (2023): 1–14. http://dx.doi.org/10.37188/co.2023-0036.

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50

Ahn, Seong-Jin, and Myoung-Ho Kim. "Link Prediction in Heterogeneous Information Networks Using Higher-Order Graph Neural Networks." KIISE Transactions on Computing Practices 28, no. 8 (August 31, 2022): 421–26. http://dx.doi.org/10.5626/ktcp.2022.28.8.421.

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