Published at : 31 Oct 2023
Volume : IJtech
Vol 14, No 6 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i6.6646
Hatem Fahd AlSelwi  Faculty of Engineering and Technology, Multimedia University, Malacca 75450, Malaysia 
Azlan Abd. Aziz  Faculty of Engineering and Technology, Multimedia University, Malacca 75450, Malaysia 
Fazly Bin Abas  Faculty of Engineering and Technology, Multimedia University, Malacca 75450, Malaysia 
Aminuddin Kayani  School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria 3001, Australia 
Noor Maizura Noor  Department of Computer Science, Faculty of Science and Technology, University Malaysia Terengganu Kuala Terengganu, 21030, Terengganu, Malaysia 
Siti Fatimah Abdul Razak   Faculty of Information Science and Technology, Multimedia University, Melaka, MALAYSIA 
Intelligent
Transportation Systems (ITS) are becoming increasingly important as traditional
traffic management systems struggle to handle the rapid growth of vehicles on
the road. Accurate traffic prediction is a critical component of ITS, as it can
help improve traffic management, avoid congested roads, and allocate resources
more efficiently for connected vehicles. However, modeling traffic in a large
and interconnected road network is challenging because of its complex
spatiotemporal data. While classical statistics and machine learning methods
have been used for traffic prediction, they have limited ability to handle
complex traffic data, leading to unsatisfactory accuracy. In recent years, deep
learning methods, such as Recurrent Neural Networks (RNNs) and Convolutional
Neural Networks (CNNs), have shown superior capabilities for traffic
prediction. However, most CNNbased models are built for Euclidean
gridstructured data, while traffic road network data are irregular and better
formatted as graphstructured data. Graph Convolutional Neural Networks (GCNs)
have emerged to extend convolution operations to more general graphstructured
data. This paper reviews recent developments in traffic prediction using deep
learning, focusing on GCNs as a promising technique for handling irregular,
graphstructured traffic data. We also propose a novel GCNbased method that
leverages attention mechanisms to capture both local and longrange
dependencies in traffic data with Kalman Filter, and we demonstrate its
effectiveness through experiments on realworld datasets where the model
achieved around 5% higher accuracy compared to the original model.
Deep leaning; Graph; Machine learning; Traffic prediction
In recent years, with the rapid
increase in the number of vehicles on the street, traditional traffic
management systems cannot keep up leading to many problems related to
congestion and the reliability of road networks. Researchers have been working
on integrating technologies from different domains such as connected devices
and sensors to improve transportation systems and build Intelligent
transportation systems (ITS)
In
intelligent transportation systems, traffic prediction is an integral part that
helps make
traffic management systems more efficient. In road networks, a congestion
problem in a single road could impact other roads in the network. Hence,
accurate traffic prediction is essential to traffic management systems.
Moreover, traffic prediction can help people avoid busy roads, but more
notably, it can help achieve more efficient resource allocation for connected
vehicles. For several years, researchers have used many time series analysis
methods for traffic prediction such as historical average (HA), autoregressive
integrated moving average (ARIMA) and machine learning methods to model large
traffic data such as knearest neighbors (KNN) and support vector machines
(SVM)
Recurrent neural network (RNN) and its variants, Gated
Recurrent Unit (GRU) and long shortterm memory networks (LSTM), were mainly
for natural language processing and due to their capabilities to learn from
long range temporal data, they were utilized to build traffic predictions
models(Hussain et al., 2021; Zhao et al., 2017).
Although traffic data is collected as temporal data,
traffic in one location is impacted by traffic in neighboring and connected
roads making it a challenge to capture spatial correlation. Convolutional
neural networks (CNN) were developed to capture the spatial features and their
correlation to gridstructured images
Traffic road network data, on the other hand, are
irregular, more complex, and better represented as graphstructured data.
Although, many researchers applied CNNbased models to graphstructured data,
it requires transforming the graph into the gridlike structure, which may not
fully capture the inherent relationships within the graph. The need to extend
deep neural networks to nonEuclidean domains motivated the work on geometric
deep learning, leading to the emergence of variants of graph neural networks
(GNNs)
However, GCN requires their entire graph structure for
training, which consumes a large amount of memory resources and fails to handle
the dynamic spatial correlations of traffic conditions. To tackle the above
challenges,
In this paper, we proposed a new model that integrates
Kalman Filter with ASTGCN to improve its accuracy. The main contributions of this paper are
summarized as follows:
We improved the accuracy of the ASTGCN model by using
Kalman Filter to fuse the data coming from different blocks. Specifically, the
spatial attention block and temporal attention block to capture the
correlations between them. We tested the proposed model on realworld highway
traffic datasets to verify that our improved model archives better results
compared to the original model and existing baselines.
The remainder of this paper is organized as follows. Section 2 presents a literature review on
traffic flow prediction. Section 3 gives details about the problem definition
and the model architecture. Section 4 presents and discusses the experimental
results. Finally, we conclude with Section 5.
2. Related Work
Traffic flow prediction is an
integral part of ITS applications and enables traffic management systems to
efficiently control traffic by making more informed decisions. Traffic
prediction methods have been through several stages of evolution. We can divide
them into three main categories: statistical analysis methods, traditional machine learning methods, and
deep learning methods. In statistical methods, the historical average (HA) is
the simplest model to predict future values using historical data typically
based on the average or mean of all or subsets of past observations.
HA offers a
simple and fast way to predict future values, but when dealing with complex
data that has many irregularities and trends it starts to lose accuracy
To tackle these issues, different variants were introduced.
Kohonen ARIMA was proposed to handle the nonlinear dependencies and
highdimensional data using the Kohonen SelfOrganizing Map with ARIMA (Connor, Martin, and Atlas, 1994).
In a work by Williams
et al., (2014), Williams, Durvasula, and Brown (1998), they
developed and tested seasonal ARIMA models and Winters exponential smoothing
models on two different datasets. In their work, both of their models achieved
promising results compared to HA models and generic ARIMA models. In another work
by
In order to take these patterns and behavior into
account, the researchers needed to divide traffic data into subsets or segments
and fit ARIMA models to each subset.
Kalman Filter has been widely used in various
applications such as sensor fusion and target tracking, given its ability in
prediction and measurements. Kalman Filter has been applied to traffic
prediction to estimate and predict future traffic values based on available
data(Ojeda, Kibangou, and DeWit, 2013; VanHinsbergen et al.,
2012; Okutani and Stephanedes, 1984).
Statistical methods often assume that the traffic data is
linear and stationary, which limits the model’s ability to capture complex and
nonlinear relationships in traffic data. To tackle these challenges, machine
learningbased traffic prediction models have emerged and received a lot of
attention from researchers. Among the first machine learning models used for
traffic prediction is the KNearest Neighbor algorithm (KNN) (Davis and Nihan, 1991).
In (Davis and Nihan, 1991) the authors
conclude that linear timeseries methods performed better than kNN, but
further research was needed to better understand which scenarios kNN is better
than conventional methods.
In recent years, some researchers utilized kNN models
for traffic predictions and were able to achieve promising results. Zhang et al.
(2013) proposed a kNN model for shortterm traffic flow
prediction in urban expressways that achieved a 90% accuracy. In another work
by Yang et al. (2019), they
proposed a kNN model for traffic flow prediction in road ports and used kDimension Tree (KD Tree) to reduce the time complexity
of neighbor searching.
Other machine learning includes support vector regression (SVR), and Bayesian
model. In a study by Wu, Ho, and Lee (2004), the authors applied SVR for traveltime
prediction using real highway traffic data.
The SVR model has been applied to traffic prediction in
several studies due to its generalization ability (Nidhi & Lobiyal, 2022). The Bayesian networks model can
take into account the causal probabilistic relationship between random
variables, which enables the modeling of complex systems and capturing
dependencies. Sun,
Zhang, and Yu (2006) proposed a traffic flows
prediction model based on a Bayesian network, and traffic flows among adjacent
road links are modeled as a Bayesian network (Sun, Zhang, and Yu, 2006).
In recent years, with the rapid development of sensors
and road networks, more and more data traffic data are generated with
increasing complexity. The use of traditional traffic prediction models becomes
inefficient and limiting, considering the complexity of data. It becomes clear
that more powerful computing and data processing technologies are required.
Furthermore, computers become more powerful with highly advanced computing
capabilities. This enabled the advancement of prediction models, specifically
deep learningbased models.
These deep learning models showed very good performance
in many fields. Therefore, several researchers focused on developing predictive
models that used a variety of deep learning methods. Recurrent Neural Networks
(RNN) and its variants were introduced into traffic flow prediction. Since
RNNbased models were built to model and capture sequences of data, they became
the first choice for timeseries prediction and classification. In
Other works used LSTM with other algorithms, such as
Principal Component Analysis (PCA), where PCA was applied to extract the main
trend data and then LSTM was applied to the residual data, which shows that
subtracting the main trend data gives better results compared to directly using
LSTM (Zhao
& Zhang, 2018). Though LSTM shows good results,
it still cannot fully capture the characteristics of traffic data, where the
traffic in one location can impact the traffic of several locations. This is
referred to as the spatial information or spatial dependencies that the generic
LSTM models cannot capture. By incorporating spatial correlations, it is
possible to capture both the temporal and spatial correlations and dependencies
between data points.
Due to their ability to learn the spatial features effectively,
many research works incorporated CNNs with LSTM. This enabled CNNLSTM models
to learn the spatial information by applying conventions over the input data
before feeding it to LSTM to learn the temporal information and the
correlations between them in an automatic and hierarchical manner. Zhao et al. (2021).
proposed a CNNLSTMbased prediction model using spatialtemporal trajectory
topology, which achieved 1%~2% accuracy compared to normal LSTM. As discussed
above, CNN is built to handle gridstructured data, which is not fully
compatible with road network data, which are irregular and better represented
as graph data. (Lu et al., 2020)
proposed a graph LSTM (GLSTM) model to capture spatialtemporal representations
in road traffic flow prediction, which can model complex traffic flow and
outperform LSTM and GRU models.
LSTM and its variants give promising results, but the
accuracy degrades with long sequences. In order to solve this problem, graph
convolution is used to handle traffic data more efficiently. There are two
methods of graph convolution spatial methods and spectral methods, where
spatial methods apply convolution filters on a graph’s nodes and their
neighbors
In their work, Yu, Yin, and Zhu (2018) proposed a graph complete
convolutional structure that effectively captures the spatiotemporal
correlations in traffic data. The proposed model was able to outperform LSTMbased
models as well as the GCGRU model proposed by (Guo et al., 2021).
However, these models do not consider the dynamic spatialtemporal correlations
of traffic data. To address the shortcomings of graph convolutions or other
models based on graph convolutions, a graph attention networks (GATs)
architecture that leverages masked selfattentional layers was introduced (Veli?kovi? et al., 2018).
To address the issues addressed above, Guo et al. (2019)
proposed an attentionbased spatialtemporal graph convolutional networks (ASTGCN) based on the graph
structure of the traffic network and the dynamic spatiotemporal information of
the traffic data. Based on the promising results of the ASTGCN model, we were
motivated to improve the performance of the models by changing the model’s
architecture and adding a Kalman Filter layer.
Attention Based SpatialTemporal Graph
Convolutional Networks
3.1. Problem
Definition
A traffic network is defined as an undirected graph G= (V,E,A), where V is a finite set of N nodes and E is a set of edges and the connectivity between the nodes is indicated as which denotes the adjacency matrix of the graph G. Where each node is a traffic detection sensor that generates the traffic flow in a certain road network, the measurements detected is denotated as F where they have the same sampling frequency.
Based on the graph
road network definition above, the traffic flow prediction problem can be
defined as follows:
Based on the above definitions, we will have which donates the values of all the features of all nodes at time t. Then denotes the value of all the features of all the nodes over time slices. Finally, is set to represent the traffic flow of node i at time t in the future.
Then, the problem the model needs to solve is given x, which donates the historical measurements of all the nodes on the traffic network over past time slices. The prediction problem is defined as of all the nodes on the whole traffic network over thnext T_{p}time slices, where denotes the future traffic flow of node i from
3.2. Model Architecture
Figure 1 displays the overall framework of the proposed model. It comprises three independent components, all designed with the same structure to model the recent, daily, periodic, and weeklyperiodic dependencies of the historical data.
Figure 1
The framework of ASTGCN. SAtt: Spatial Attention;
TAtt: Temporal Attention GCN: Graph Convolution; Conv: Convolution; FC: Fully
connected; ST block: SpatialTemporal block
Figure 2 shows how the Kalman Filter is
integrated into the original model, where we fuse the spatial attention output
with the original timeseries which ensures we have more information preserved
throughout the training process.
Figure 2
The integration of Kalman Filter with the model.
Figure 3 shows the traffic sensors distribution from the PEMS dataset;
the sensors data are collected from the PEMS dataset. The map is created using
the adjacency matrix for the graph data, which also shows the connections
between the sensors based on the road network.
Figure 3
Traffic sensors distribution map in the PEMS04 dataset.
Figure 4 shows the graph representation
of the sensor data with its nodes and edge connections. The graph shows how
dense is the connection between all of the nodes. These connections represent
the possible impact of the nodes on each other.
Figure 4
Traffic sensors distribution over a graph for both datasets.
Figure 5 shows the error matrix values
for the original model and the improved model. We can notice that the proposed
model with Kalman Filter shows smaller error values compared to the original
model. Which indicates an improvement when using Kalman Filter with ASTGCN
model.
Figure 5
Model prediction accuracy with
and without Kalman Filter.
Table 1 displays the average performance
of traffic prediction for various models. It is evident that the original
ASTGCN model outperforms the other models, we can notice that the ASTGCN
original model shows better performance compared to the other models. Moreover,
we can see that our proposed model shows better performance results compared to
the other models and the original model. We trained the original model and our
proposed model using PeMSD4 and PeMSD8 datasets for 40 Epoch.
Table 1 Average performance comparison of different
approaches on PeMSD4

PeMSD4 
PeMSD8  
Model 
RMSE 
MAE 
RMSE 
MAE 
HA 
54.14 
36.76 
44.03 
29.52 
ARIMA 
68.13 
32.11 
43.30 
24.04 
VAR 
51.73 
33.76 
31.21 
21.41 
LSTM 
45.82 
29.45 
36.96 
23.18 
GRU 
45.11 
28.65 
35.95 
22.20 
STGCN 
38.29 
25.15 
27.87 
18.88 
GLUSTGCN 
38.41 
27.28 
30.78 
20.99 
GeoMAN 
37.84 
23.64 
28.91 
17.84 
ASTGCN 
35.45 
22.55 
29.34 
19.13 
Our Model 
33.56 
21.14 
29.09 
18.59 
In this paper, we addressed the challenge of accurate traffic flow prediction
in large and interconnected road networks by proposing an attentionbased
spatialtemporal graph convolutional network (ASTGCN) with a Kalman filter.
Traditional statistical and machine learning methods have limitations in
handling the complex spatiotemporal data of traffic networks, leading to
unsatisfactory accuracy. To overcome these limitations, we leveraged graph
convolutional neural networks (GCNs), which extend the concept of convolution
to graphstructured data. Our proposed ASTGCN model integrates attention
mechanisms to capture both local and longrange dependencies in traffic data
with a Kalman filter to fuse data from different blocks and improve the model's
accuracy. Finally, our research demonstrates the potential of attentionbased
spatialtemporal graph convolutional networks with a Kalman filter for traffic
flow forecasting. Further research can explore additional enhancements and
applications of this model, such as realtime traffic prediction, adaptive
traffic management, and integration with emerging technologies like connected
and autonomous vehicles.
This work was supported by the Ministry of Higher
Education, Malaysia FRGS/1/2019/TK08/MMU/03/1 and TMRND Grant MMUE/190012
Cho, K., Van
Merriënboer, B., Bahdanau, D., Bengio, Y., 2014. On The Properties of Neural
Machine Translation: EncoderDecoder Approaches. Available
online at: http://arxiv.org/abs/1409.1259, Accessed on January 10, 2023
Connor, J.T.,
Martin, R.D., Atlas, L.E., 1994. Recurrent Neural Networks and Robust Time
Series Prediction. IEEE Transactions On Neural Networks, Volume 5(2), pp. 240–254
Davis, G.A., Nihan, N.L., 1991.
Nonparametric Regression And ShortTerm Freeway Traffic Forecasting. Journal
of Transportation Engineering, Volume 117(2), pp. 178–188
Du, X., Zhang,
H., VanNguyen, H., Han, Z., 2017. Stacked LSTM Deep Learning Model For Traffic
Prediction In VehicleToVehicle Communication. In: IEEE Vehicular
Technology Conference 2017, pp.1–5
Guo, K., Hu, Y.,
Qian, Z., Liu, H., Zhang, K., Sun, Y., Gao, J., Yin, B., 2021. Optimized Graph
Convolution Recurrent Neural Network for Traffic Prediction. IEEE
Transactions on Intelligent Transportation Systems, Volume 22(2), pp.
1138–1149
Guo, S., Lin,
Y., Feng, N., Song, C. and Wan, H., 2019. Attention Based SpatialTemporal
Graph Convolutional Networks For Traffic Flow Forecasting. In: Proceedings
of the AAAI Conference on Artificial Intelligence, Volume 33(1),
pp.922–929.
Hussain, B.,
Afzal, M.K., Ahmad, S., Mostafa, A.M., 2021. Intelligent Traffic Flow
Prediction Using Optimized GRU Model. IEEE Access, Volume 9, pp. 100736–100746
Lee, S., Fambro,
D.B., 1999. Application of Subset Autoregressive Integrated Moving Average
Model for ShortTerm Freeway Traffic Volume Forecasting. Transportation
Research Record, Volume 1678(1), pp.179–188
Liang, Y., Ke,
S., Zhang, J., Yi, X. and Zheng, Y., 2018. GeoMAN: Multilevel Attention
Networks for Geosensory Time Series Prediction. In: Proceedings of the
TwentySeventh International Joint Conference on Artificial Intelligence, Volume
2018, pp.3328–3434
Lopez Pinaya, W.
H., Vieira, S., GarciaDias, R., Mechelli, A., 2020. Convolutional Neural
Networks. In: A. Mechelli, S. Vieira (Eds.), Academic Press, Machine
Learning, pp. 173–191.
Lu, Z., Lv, W.,
Cao, Y., Xie, Z., Peng, H., Du, B., 2020. LSTM Variants Meet Graph Neural
Networks For Road Speed Prediction. Neurocomputing, Volume 400, pp.34–45
Mingheng, Z.,
Yaobao, Z., Ganglong, H., Gang, C., 2013. Accurate Multisteps Traffic Flow
Prediction Based on SVM. Mathematical Problems in Engineering, Volume
2013, p. 418303
Nidhi, N.,
Lobiyal, D.K., 2022. Traffic Flow Prediction using Support Vector Regression. International
Journal of Information Technology, Volume 14(2), pp. 619–626
Ojeda, L.L.,
Kibangou, A.Y. DeWit, C.C., 2013. Adaptive Kalman Filtering for MultiStep
Ahead Traffic Flow Prediction. In: 2013 American Control Conference.
pp.4724–4729
Okutani, I., Stephanedes, Y. J., 1984. Dynamic Prediction
of Traffic Volume Through Kalman Filtering Theory. Transportation Research
Part B: Methodological, Volume 18(1), pp. 1–11
Park, J., Li,
D., Murphey, Y.L., Kristinsson, J., McGee, R., Kuang, M., Phillips, T., 2011.
Real Time Vehicle Speed Prediction Using a Neural Network Traffic Model. In:
The 2011 International Joint Conference on Neural Networks. pp. 2991–2996
Smith, B.L.,
Demetsky, M.J., 1997. Traffic Flow Forecasting: Comparison of Modeling
Approaches. Journal of Transportation Engineering, Volume 123(4), pp.
261–266
Sun, S., Zhang,
C., Yu, G., 2006. A Bayesian Network
Approach To Traffic Flow Forecasting. IEEE Transactions on Intelligent
Transportation Systems, Volume 7(1), pp. 124–133
Sutskever, I.,
Vinyals, O., Le, Q.V., 2014. Sequence to Sequence Learning with Neural
Networks. Available online at: http://arxiv.org/abs/1409.3215, Accessed on January 10, 2023
VanHinsbergen,
C.P.I.J., Schreiter, T., Zuurbier, F.S., VanLint, J.W.C., VanZuylen, H.J.,
2012. Localized Extended Kalman Filter For Scalable RealTime Traffic State
Estimation. IEEE Transactions on Intelligent Transportation Systems, Volume
13(1), pp. 385–394
Veli?kovi?, P.,
Casanova, A., Liò, P., Cucurull, G., Romero, A., Bengio, Y., 2018. Graph
Attention Networks. In: 6^{th} International Conference on
Learning Representations, pp.1–12
Williams, B.M.,
Asce, M., Hoel, L.A., Asce, F., 2014. Modeling and Forecasting Vehicular
Traffic Flow as a Seasonal ARIMA Process?: Modeling and Forecasting Vehicular
Traffic Flow as a Seasonal ARIMA Process?: Theoretical Basis and Empirical
Results. Journal of Transportation Engineering, Volume 129(6), pp.
664–672
Williams, B.M.,
Durvasula, P.K., Brown, D.E., 1998. Urban Freeway Traffic Flow Prediction:
Application of Seasonal Autoregressive Integrated Moving Average And
Exponential Smoothing Models. Transportation Research Record, Volume
1644(1), pp. 132–141
Wu, C.H., Ho,
J.M., Lee, D.T., 2004. TravelTime Prediction With Support Vector Regression. IEEE
Transactions on Intelligent Transportation Systems, Volume 5(4), pp.
276–281
Wu, P., Huang,
Z., Pian, Y., Xu, L., Li, J., Chen, K., 2020. A Combined Deep Learning Method
with AttentionBased LSTM Model for ShortTerm Traffic Speed Forecasting. Journal of
Advanced Transportation, Volume 2020, pp.
1–15
Xiangxue, W.,
Lunhui, X., Kaixun, C., 2019. DataDriven ShortTerm Forecasting for Urban Road
Network Traffic Based on Data Processing and LSTMRNN. Arabian Journal for
Science and Engineering, Volume 44, pp. 3043–3060
Yang, L., Yang,
Q., Li, Y., Feng, Y., 2019. KNearest Neighbor Model Based ShortTerm Traffic
Flow Prediction Method. In: Proceedings  2019 18^{th} International
Symposium on Distributed Computing and Applications for Business Engineering
and Science, DCABES 2019. Institute of Electrical and Electronics Engineers
Inc. pp.27–30
Yao, H., Tang,
X., Wei, H., Zheng, G., Li, Z., 2019. Revisiting SpatialTemporal Similarity: A
Deep Learning Framework For Traffic Prediction. In: Proceedings of The AAAI
Conference on Artificial Intelligence, Volume 33(1), pp. 5668–5675
Yu, B., Yin, H.,
Zhu, Z., 2018. SpatioTemporal Graph Convolutional Networks: A Deep Learning
Framework For Traffic Forecasting. In: IJCAI International Joint
Conference on Artificial Intelligence, 2018July, pp.3634–3640.
Zhang, J.,
Zheng, Y., Qi, D., Li, R., Yi, X., 2016. DNNBased Prediction Model for
SpatioTemporal Data. In: Proceedings of the 24^{th} ACM SIGSPATIAL
International Conference On Advances In Geographic Information Systems, p.
92
Zhang, L., Liu,
Q., Yang, W., Wei, N., Dong, D., 2013. An Improved Knearest Neighbor Model for
Shortterm Traffic Flow Prediction. Procedia  Social and Behavioral
Sciences, Volume 96, pp. 653–662
Zhang, S., Guo,
Y., Zhao, P., Zheng, C., Chen, X., 2021. A GraphBased Temporal Attention
Framework for MultiSensor Traffic Flow Forecasting. IEEE Transactions on
Intelligent Transportation Systems, Volume 23(7), pp.1–16
Zhao, L., Song,
Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H., 2015. TGCN?: A
Temporal Graph Convolutional Network for Traffic Prediction, IEEE Transactions on Intelligent
Transportation Systems, Volume 14(8), pp. 1–11
Zhao, Z., Chen,
W., Wu, X., Chen, P.C.Y., Liu, J., 2017. LSTM Network?: a Deep Learning
Approach For ShortTerm Traffic Forecast.
IET Intelligent Transport Systems, Volume 11(2), pp. 68–75
Zhao, Z., Li,
Z., Li, F., Liu, Y., 2021. CNNLSTM Based Traffic Prediction Using
SpatialTemporal Features. Journal of Physics: Conference Series, Volume
2037(1), p. 012065
Zhao, Z., Zhang,
Y., 2018. A Traffic Flow Prediction Approach: LSTM with Detrending. In: Proceedings
of the 2018 IEEE International Conference on Progress in Informatics and
Computing, PIC 2018, pp. 101–105
Zhou, J., Cui,
G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Sun, M., 2020. Graph Neural Networks:
A Review of Methods and Applications. AI Open, Volume 1, pp. 57–81