=Paper=
{{Paper
|id=Vol-3426/paper15
|storemode=property
|title=Product Recommendation System Using Graph Neural Network
|pdfUrl=https://ceur-ws.org/Vol-3426/paper15.pdf
|volume=Vol-3426
|authors=Yuliana Lavryk,Yurii Kryvenchuk
|dblpUrl=https://dblp.org/rec/conf/momlet/LavrykK23
}}
==Product Recommendation System Using Graph Neural Network==
https://ceur-ws.org/Vol-3426/paper15.pdf
Product Recommendation System Using Graph Neural
Network
Yuliana Lavryk, Yurii Kryvenchuk
Lviv Polytechnic National University, Stepana Bandery Street 12, Lviv, 79013, Ukraine
Abstract
The use of graph neural networks (GNNs) in product recommendation systems has gained
attention in recent years due to its ability to capture complex relationships between
products and customers. This approach can incorporate both explicit and implicit feedback
from customers, as well as contextual information such as time and location. GNNs can
become a powerful tool for personalized and accurate recommendations. While there are
challenges associated with building GNN-based recommendation systems, the potential
benefits make it an exciting area of research and development for e-commerce businesses.
This paper explores the novelty and advantages of using GNNs in product recommendation
systems and discusses the challenges and future directions of this approach.
Keywords 1
Graph Neural Networks (GNN), Recommendation System (RS), Product, GraphSAGE
1. Introduction
Building product recommendation systems using GNNs is a relatively new approach compared to
traditional methods such as collaborative filtering and content-based filtering. Collaborative filtering
(CF) works by analyzing the past behavior of users, such as their previous purchases, ratings, or reviews,
to make recommendations for products that other similar users have liked or purchased. This approach
relies on finding similarities between users based on their past behavior, which can be challenging for
new users who have not provided any feedback yet [1]. Content-based filtering (CBF) works by
analyzing the attributes of the items themselves, such as their features, descriptions, or images, and
recommending items that are similar to those the user has previously shown an interest in. This approach
is more effective for new users as it relies on the features of the items rather than past behavior.
However, it can be limited by the availability and quality of item attributes [2]. Graph neural networks
are a type of neural network that can operate on graph data structures, such as social networks or product
co-purchasing networks. In the context of recommendation systems, GNNs can be used to learn
representations of users and items based on their relationships in the graph, and to make
recommendations by predicting the likelihood of a user interacting with a particular item in the future.
GNNs can combine information from both collaborative and content-based approaches and can be more
effective for handling complex interactions and relationships between users and items [3].
GNNs have the ability to model complex relationships between products and customers, such as the
influence of a customer's friends and family members on their purchasing behavior. This approach is
particularly useful when dealing with large datasets where the number of products and customers can
be overwhelming.
One of the novel aspects of using GNNs for product recommendation systems is the ability to
incorporate both explicit and implicit feedback from customers. Explicit feedback refers to customer
ratings and reviews, while implicit feedback refers to the customer's purchase history and browsing
1
MoMLeT+DS 2023: 5th International Workshop on Modern Machine Learning Technologies and Data Science, June 3, 2023, Lviv, Ukra ine
EMAIL: ylilav55547@gmail.com (Yu. Lavryk); yurkokryvenchuk@gmail.com (Yu. Kryvenchuk)
ORCID: 0000-0003-4223-3403 (Yu. Lavryk); 0000-0002-2504-5833 (Yu. Kryvenchuk)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
behavior. GNNs can incorporate both types of feedback to provide more accurate and personalized
recommendations.
Another novel aspect of using GNNs for product recommendation systems is the ability to
incorporate contextual information such as time and location. For example, a customer's purchasing
behavior may change depending on the time of day or their location. GNNs can capture these contextual
factors and use them to make more accurate recommendations.
Furthermore, GNNs can handle such a problem as lack of sufficient information about a new
customer or product to make recommendations. GNNs can leverage the information from other similar
customers or products in the graph to make recommendations for the new customer or product.
In summary, while collaborative filtering and content-based filtering are more traditional
approaches, GNNs can provide a more powerful and flexible solution for product recommendation
systems, especially in cases where there are complex interactions and relationships between users and
items. The use of GNNs in product recommendation systems represents a significant advancement in
the field of recommendation systems. The ability to capture complex relationships and incorporate
contextual information makes GNNs a powerful tool for effective and high-quality recommendations.
2. Literature Review
The literature on recommendation systems covers various approaches, including collaborative
filtering, content-based filtering, and hybrid models (Figure 1).
Figure 1: Common approaches to building recommendation system
More recently, the use of graph neural networks (GNNs) in recommendation systems has been
gaining attention due to their ability to capture complex relationships between entities in the graph.
A study by Ying et al. [4] proposed a GNN-based recommendation system called GraphRec. The
system uses a GNN to learn embeddings of users and products in a graph and uses the embeddings to
make personalized recommendations. GraphRec was shown to outperform state-of-the-art collaborative
filtering and content-based recommendation systems on several benchmark datasets.
Another study by Wang et al. [5] proposed a GNN-based recommendation system to learn
embeddings of items and users based on the graph structure and uses the embeddings to make
recommendations. System was shown to outperform traditional collaborative filtering and content-
based recommendation systems on several real-world datasets.
Furthermore, there have been studies on using GNNs for session-based recommendation systems,
where the goal is to recommend items to users based on their current session rather than their entire
history. A study by Wu et al. [6] proposed a session-based recommendation system called SR-GNN
that uses a GNN to model the sequence of user interactions as a graph and generates recommendations
based on the learned embeddings.
The authors of [7] propose a new model of a recommender system based on a dynamic graph neural
network (DGN). The advantages of such a model are the ability to take into account changes in user
behavior over time, as well as to process different types of data, including text, video and images.
However, this type of model requires a large amount of data to train and powerful resources to run,
which can often be limiting factors. The goal of my research is to build a model that will recognize deep
dependencies among smaller amounts of data, which will provide high quality of recommendations and
will not require resources such as DGN.
In the article [8], a new approach to the recommender system based on graph convolutional
embeddings (Graph Convolutional Embeddings, GCE) is proposed. The paper demonstrates the high
accuracy and efficiency of the GCE model compared to other methods used for recommender systems.
But there are the same limitations as when using DGN [7], when there is a need for a very large amount
of data for training and, accordingly, significant resources. Another disadvantage is possible limitations
and errors when processing graphs with a complex structure. This work is another confirmation that
there is a need to build a more flexible system that can work effectively without such a volume of data.
The authors of the study [9] proposed a new approach to the development of a recommender system
based on context-oriented graph neural networks. The context-oriented approach is based on the
analysis of more detailed factors regarding the user, which improves the quality of recommendations
and provides a more personalized approach to the user. Another advantage of this method is the ability
to adapt to different types of data. This article provides a detailed description of context-aware
recommender systems combined with GNNs that can be used to build the own hybrid system. At the
same time, this approach may require retraining the model when adding new data, as there is not enough
information to make a recommendation.
The work [10] describes the use of graph neural networks for recommender systems in electronic
commerce. The study authors describe the Item Relationship Graph Neural Network (IRGNN) model,
which uses graph neural networks to predict recommendations. The model uses information about the
relationship between products to improve the quality of recommendations. The main advantage of
IRGNN is that it can take into account complex dependencies between products, such as categories,
product similarities, etc. This allows the model to make more accurate recommendations, which
increases the efficiency of e-commerce. However, the authors did not investigate some key aspects of
IRGNN implementation, such as speed and scalability. It is also important to be aware that this model
can be more difficult to establish compared to more traditional recommendation methods. The work
demonstrates a high level of benefit of using recommender systems in e-commerce.
However, there are also challenges associated with building GNN-based recommendation systems,
such as scalability and the need for large amounts of data. A study by Monti et al. [11] proposed a
scalable GNN-based recommendation system that can handle large-scale graphs. The system uses a
variant of the GNN that allows efficient message passing and can be parallelized across multiple GPUs.
Overall, the literature suggests that GNN-based recommendation systems have shown promising
results in improving the accuracy and personalization of recommendations. However, there are still
challenges that need to be addressed, such as scalability and data sparsity.
3. Overview of Graph Neural Networks (GNNs)
Graph Neural Networks (GNNs) are a type of neural network designed to operate on graph-
structured data. A graph is a mathematical structure consisting of a set of nodes (also called vertices)
and a set of edges connecting pairs of nodes. GNNs are used to learn node and graph representations in
complex, large-scale networks such as social networks, citation networks, and molecular graphs [12].
GNNs are built upon the idea of message passing, where information is propagated from node to
node through the edges of a graph. Each node in the graph has a feature vector that represents its
properties, and each edge has a weight that represents the relationship between two connected nodes.
The goal of GNNs is to learn a representation of each node that captures its role in the graph, as well as
a representation of the entire graph. GNNs consist of several layers of neural networks, each of which
performs two main operations: message passing and node update. In the message passing step, the node
features are updated based on the features of its neighboring nodes and the edge weights between them.
In the node update step, the updated node features are aggregated to produce a new representation of
the node. The aggregation function can be as simple as taking the average of the node features or more
complex, such as a weighted sum or attention-based mechanism.
GNNs have been used in a wide range of applications, including recommendation systems, drug
discovery, social network analysis, and image recognition. One of the strengths of GNNs is their ability
to handle large-scale, complex networks with multiple types of nodes and edges. GNNs have also shown
to be effective in handling noisy and incomplete data and are able to learn representations that capture
the underlying structure and patterns in the data [13].
In recent years, there have been several advancements in the field of GNNs, including the
development of more efficient message passing algorithms, the integration of attention mechanisms,
and the use of GNNs in unsupervised and semi-supervised learning tasks. With continued research and
development, GNNs are expected to become an increasingly important tool in the analysis and
understanding of complex networks.
4. Challenges in building GNN-based Recommendation System
Building a GNN-based recommendation system can present several challenges, including:
Data sparsity: In recommendation systems, the user-item interaction data may be sparse, meaning
that many users have only interacted with a small number of items. This can make it difficult for the
GNN to learn accurate representations of users and items based on their interactions.
Cold-start problem: When a new item is introduced to the system, there may be no user interactions
with it, making it difficult for the GNN to make accurate recommendations. Similarly, when a new user
joins the system, there may be insufficient data to make accurate recommendations for them.
Scalability: The size of the graph can grow rapidly with the number of users and items in the system,
making it difficult to train the GNN efficiently. This can lead to longer training times and increased
computational resources.
Overfitting: GNNs can suffer from overfitting when the model is too complex or when there is
insufficient data to generalize accurately. This can result in poor performance when making
recommendations for new users or items.
Diversity: Recommendation systems may need to balance accuracy with diversity, ensuring that the
recommended items are not too similar to each other. GNNs may need to incorporate additional
diversity metrics to achieve this.
Interpretability: GNNs can be difficult to interpret, making it challenging to understand how the
model is making recommendations. This can be problematic for applications where interpretability is
critical, such as in healthcare or finance.
Overall, building a GNN-based recommendation system requires addressing these challenges to
ensure that the system is accurate, scalable, and interpretable.
5. Data for Recommendation System based on GNN
There are several sources of graph structure data that can be used to build a recommendation system.
The most popular and suitable for building recommendation systems based on GNN are Amazon data.
Some of the common sources include:
Amazon product metadata: This data includes information such as product categories, descriptions,
and attributes. It can be used to construct a graph that connects products based on their metadata.
Amazon customer reviews: This data includes product reviews and ratings by users. It can be used
to construct a bipartite graph that connects users and products based on their review history.
Amazon purchase history: This data includes information about which products were purchased by
which users. It can be used to construct a bipartite graph that connects users and products based on their
purchase history.
Amazon product co-occurrence: This data includes information about which products are frequently
purchased together. It can be used to construct a graph that connects products based on their co-
occurrence.
Amazon product browsing history: This data includes information about which products were
browsed by which users. It can be used to construct a bipartite graph that connects users and products
based on their browsing history [14].
It's worth noting that each of these data sources may require preprocessing and cleaning before they
can be used to construct a suitable graph for training a GNN-based recommendation system.
Additionally, the choice of graph structure data will depend on the specific requirements of the
recommendation system, as well as the availability and quality of the data.
6. Data Preprocessing for GNN-based Recommendation Systems
Data preprocessing is a critical step in building any machine learning model, including GNN-based
recommendation systems. In this section, the various data preprocessing steps involved in building a
GNN-based recommendation system will be discussed.
1. Data Cleaning and Transformation: The first step in data preprocessing is to clean and transform the
raw data into a format suitable for GNN-based recommendation systems. This involves removing
missing values, transforming categorical variables into numerical variables, and normalizing
numerical variables [15].
2. Graph Construction: GNN-based recommendation systems operate on graph-structured data, where
users and products are represented as nodes in the graph and their interactions as edges. The second
step in data preprocessing is to construct the graph from the cleaned data. The graph can be
constructed using different approaches, such as user-item bipartite graphs or user-user and item-item
co-occurrence graphs [16].
3. Node and Edge Feature Engineering: The next step is to engineer features for nodes and edges in the
graph. This involves defining features for nodes and edges based on the available data, such as user
demographics, product attributes, and interactions between users and products. The features can be
used to learn embeddings of nodes and edges using GNNs [17].
4. Train-Validation-Test Split: The next step is to split the graph into training, validation, and test sets.
The training set is used to learn the GNN model, the validation set is used to tune hyperparameters,
and the test set is used to evaluate the performance of the model [18].
5. Negative Sampling: In recommendation systems, the number of negative interactions (i.e.,
interactions between users and products that did not occur) is much larger than the number of
positive interactions. Negative sampling is a technique used to address this class imbalance by
randomly sampling negative interactions for training the model [19].
6. Data Augmentation: Data augmentation is a technique used to increase the size of the training data
by generating synthetic examples. In recommendation systems, data augmentation can be used to
simulate different user interactions, such as adding noise to existing interactions or creating new
interactions between similar products.[20].
Data preprocessing is a crucial step in building GNN-based recommendation systems. It involves
cleaning and transforming the raw data, constructing the graph, engineering features for nodes and
edges, splitting the graph into training, validation, and test sets, performing negative sampling, and
using data augmentation to increase the size of the training data.
7. GNN Structure for Recommendation System
The structure of a graph neural network (GNN) for a recommendation system can vary depending
on the specific requirements of the application. However, a typical GNN structure for recommendation
systems can be divided into the following components:
Input Layer: The input layer of the GNN takes as input the node features for both users and products.
These features can include categorical features such as product category, brand, and user location, as
well as numerical features such as product price and user age.
Convolutional Layers: The convolutional layers of the GNN perform message passing between
nodes in the graph. The message passed between nodes can be a function of the features of both nodes,
as well as the relationship between them in the graph. In the case of a recommendation system, the
convolutional layers can capture the similarity between users and products based on their past
interactions and features.
Pooling Layers: The pooling layers of the GNN aggregate information from the convolutional layers
across the graph. This can be done by computing the mean or max of the node features in a neighborhood
of nodes. The pooling layers can also be used to perform graph-level feature extraction.
Output Layer: The output layer of the GNN predicts the likelihood of a user interacting with a
particular product. This can be done by computing a similarity score between the user and the product
based on their learned representations. The output layer can also be used to generate a ranked list of
recommended products for a given user.
General structure of GNN is shown in Figure 2.
Figure 2: General structure of GNN. [21], via AI Summer. (https://theaisummer.com/Graph_Neural_
Networks/).
Overall, the structure of a GNN for recommendation systems involves learning representations of
users and products based on their interactions and features in a bipartite graph (Figure 3). This allows
for the generation of personalized recommendations for users based on their past behavior and
preferences.
Figure 3: Example of Bipartite Graph
8. GNN Model Architecture for Product Recommendation System
The GNN model architecture for product recommendation involves several components,
including graph construction, node and edge feature engineering, GNN layers, and output layer. In this
section, we will discuss each of these components in detail.
● Graph Construction: The first step in building a GNN-based recommendation system is to
construct the graph from the preprocessed data. The graph can be constructed using different
approaches, such as user-item bipartite graphs, user-user and item-item co-occurrence graphs, or
heterogeneous graphs that include multiple types of nodes and edges.
● Node and Edge Feature Engineering: The next step is to engineer features for nodes and edges
in the graph. This involves defining features for nodes and edges based on the available data, such as
user demographics, product attributes, and interactions between users and products. The features can
be used to learn embeddings of nodes and edges using GNNs.
● GNN Layers: The core of the GNN-based recommendation system is the GNN layers that
learns node embeddings by aggregating information from their neighboring nodes and edges in the
graph. The GNN layers typically consist of two steps: message passing and node update. In the message
passing step, each node sends a message to its neighboring nodes based on their edge features. In the
node update step, each node aggregates the received messages and updates its own embedding. This
process is repeated for multiple layers to capture increasingly complex relationships in the graph.
● Output Layer: The final step in the GNN model architecture is the output layer, which
generates the recommendations for each user based on their learned node embeddings. The output layer
can be implemented using various approaches, such as dot product, cosine similarity, or neural networks
that combine user and item embeddings.
● Several variations of the GNN model architecture have been proposed for product
recommendation systems. For example:
- GraphSAGE (Hamilton et al.) [22] learns a node's representation by aggregating information
from its neighbors, which can be useful for product recommendations in cases where there is limited
information about a particular item.
- PinSage (Wang et al.) [23] uses a graph neural network to learn embeddings of items and
users based on the graph structure. Additionally, there have been studies on using attention mechanisms
to improve the performance of GNN-based recommendation systems.
- Graph Convolutional Network (GCN) - one more popular graph neural network architecture
that has been applied to product recommendation systems. The basics are convolutional neural networks
which operate directly on graphs [24].
- Graph Attention Network (GAT) - another popular graph neural network architecture that
can be used for product recommendation systems. GATs use attention mechanisms to weight the
importance of different nodes in a graph, which can be useful for learning personalized
recommendations [25].
- Neural Graph Collaborative Filtering (NGCF) - This is a graph neural network architecture that has
been specifically designed for collaborative filtering tasks such as product recommendations. NGCF
uses a weighted sum of the embeddings of a user's neighbors to generate personalized
recommendations [23].
The GNN model architecture for product recommendation involves constructing the graph,
engineering node and edge features, using GNN layers to learn node embeddings, and using an output
layer to generate recommendations for each user. Several variations of the GNN model architecture
have been proposed, each with its strengths and limitations. In this work several different GNN models
will be trained and compared so that the general pipeline will look like shown in Figure 4.
Figure 4: Pipeline of building product recommendation system using GNN
Next, one of the final steps - model evaluation - will be described.
9. Evaluation of GNN-based Recommendation System
Evaluation of GNN-based recommendation systems is critical to determine their performance and
effectiveness in recommending products to users. In this section, we will discuss the metrics and
techniques used to evaluate the performance of GNN-based recommendation systems.
1. Evaluation Metrics: There are several metrics used to evaluate the performance of recommendation
systems, including precision, recall, F1-score, and mean average precision (MAP). Precision
measures the fraction of recommended products that are relevant to the user, while recall measures
the fraction of relevant products that are recommended. The F1-score is the harmonic mean of
precision and recall. MAP is a measure of the average precision at different levels of recall. Also,
one more wide-spread metric to evaluate the performance of a recommendation system is top-K
accuracy. It measures the percentage of test instances where the true positive recommendation is in
the top-K predicted recommendations [26]. These metrics can be used to evaluate the accuracy,
relevance, and diversity of recommendations generated by GNN-based recommendation systems.
2. Splitting Strategy: To evaluate the performance of GNN-based recommendation systems, the data
is typically split into training, validation, and test sets. The training set is used to train the GNN
model, the validation set is used to tune hyperparameters, and the test set is used to evaluate the
performance of the model on unseen data. Different splitting strategies can be used, such as random
splitting, temporal splitting, or user-based splitting, depending on the characteristics of the dataset.
3. Ablation Study: Ablation study is a technique used to evaluate the contribution of each component
of the GNN model to the overall performance. This involves training and testing the GNN model
with and without each component to determine its impact on the performance. Ablation study can
help identify which components are essential for the performance of the GNN-based
recommendation system [27].
Several studies have evaluated the performance of GNN-based recommendation systems using the
above techniques. For example, the study by Ying et al. [22] evaluated the performance of GraphSAGE
on the MovieLens dataset and compared it with baseline methods, while the study by Wang et al. [23]
evaluated the performance of PinSage on the Pinterest dataset and used ablation study to identify the
contribution of each component to the performance.
The evaluation of GNN-based recommendation systems involves using evaluation metrics, splitting
strategy, baseline methods, and ablation study to determine their performance and effectiveness. These
techniques can help identify the strengths and limitations of GNN-based recommendation systems and
guide further improvements in the model architecture.
10. Future Directions and Conclusion
In this research work, the use of graph neural networks (GNNs) in product recommendation systems
was explored . Discussed the background of recommendation systems, reviewed the literature on GNN-
based recommendation systems, and presented the data preprocessing, model architecture, and
evaluation techniques for GNN-based recommendation systems.
In summary, GNN-based recommendation systems offer several advantages over traditional
recommendation systems, including the ability to capture complex relationships among products and
users, and the ability to incorporate auxiliary information such as product attributes and user profiles.
However, there are still several areas for future research and improvement. One area is the
development of more sophisticated GNN architectures that can capture higher-order relationships
among products and users. Another area is the incorporation of temporal dynamics into GNN-based
recommendation systems, which can capture changes in user preferences over time.
Additionally, the scalability and efficiency of GNN-based recommendation systems need to be
improved to handle large-scale datasets with millions of products and users. Furthermore, the
interpretability of GNN-based recommendation systems needs to be improved to enable users to
understand how the recommendations are generated and provide feedback.
In conclusion, GNN-based recommendation systems have shown promise in improving the accuracy
and relevance of product recommendations. However, there is still much work to be done in developing
more sophisticated GNN architectures, incorporating temporal dynamics, improving scalability and
efficiency, and enhancing interpretability. This research work provides a useful starting point for future
research in this exciting and rapidly evolving field.
11. References
[1] P. Seeda, A Complete Guide To Recommender Systems — Tutorial with Sklearn, Surprise,
Keras, Recommenders, 2021. URL: https://towardsdatascience.com/a-complete-guide-to-
recommender-system-tutorial-with-sklearn-surprise-keras-recommender-5e52e8ceace1.
[2] P. Kordík, Machine Learning for Recommender systems — Part 1, 2019. URL:
https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-
algorithms-evaluation-and-cold-start-6f696683d0ed.
[3] S. Virinchi, Using graph neural networks to recommend related products, 2022. URL:
https://www.amazon.science/blog/using-graph-neural-networks-to-recommend-related-products.
[4] Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin, Graph Neural
Networks for Social Recommendation, World Wide Web Conference (WWW ’19), May 13–17,
New York, USA, 2019. doi: https://doi.org/10.1145/3308558.3313488.
[5] Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua, Unifying Knowledge
Graph Learning and Recommendation: Towards a Better Understanding of User Preferences,
World Wide Web Conference (WWW ’19), May 13–17, New York, USA, 2019. doi:
https://doi.org/10.1145/3308558.3313705.
[6] Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan, Session-based
Recommendation with Graph Neural Networks, 2019.
[7] Zhang M., Wu S., Yu X., Liu Q., Wang L., Dynamic Graph Neural Networks for Sequential
Recommendation, IEEE Transactions on Knowledge and Data Engineering, 2022. doi:
https://doi.org/10.48550/arXiv.2104.07368.
[8] Duran P. G., Karatzoglou A., Vitria J., Xin X., Arapakis I. Graph convolutional embeddings for
recommender systems, IEEE Access, 2021. doi: https://doi.org/10.48550/arXiv.2103.03587
[9] Li D., Gao Q., Session Recommendation Model Based on Context-Aware and Gated Graph Neural
Networks, Computational Intelligence and Neuroscience, 2021. doi:
https://doi.org/10.1155/2021/7266960
[10] Weiwen Liu, Yin Zhang; Jianling Wang, Yun He; James Caverlee, Patrick P. K. Chan, Daniel S.
Yeung, Item Relationship Graph Neural Networks for E-Commerce, 2021. doi:
10.1109/TNNLS.2021.3060872.
[11] Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., and Bronstein, M. M., Geometric deep
learning on graphs and manifolds using mixture model cnns, 2016.
[12] A. Pathak, Graph Neural Networks Explained in 5 Minutes, 2023. URL:
https://geekflare.com/graph-neural-networks/.
[13] P. Sharma, What are Graph Neural Networks, and how do they work, 2023. URL:
https://www.analyticsvidhya.com/blog/2022/03/what-are-graph-neural-networks-and-how-do-
they-work/.
[14] G. Valdata, Building a Recommender System for Amazon Products with Python, 2022. URL:
https://towardsdatascience.com/building-a-recommender-system-for-amazon-products-with-
python-8e0010ec772c.
[15] D. Kumar, Introduction to Data Preprocessing in Machine Learning, 2018. URL:
https://towardsdatascience.com/introduction-to-data-preprocessing-inmachine-learning-
a9fa83a5dc9d/.
[16] A. Pearce, A. Wiltschko, B. Sanchez-Lengeling, E. ReifA, Gentle Introduction to Graph Neural
Networks, 2021. URL:
https://distill.pub/2021/gnn-intro/.
[17] A. Ali Awan, A Comprehensive Introduction to Graph Neural Networks (GNNs), 2022. URL:
https://www.datacamp.com/tutorial/comprehensive-introduction-graph-neural-networks-gnns-
tutorial.
[18] S. Agrawal, How to split data into three sets (train, validation, and test) And why, 2021. URL:
https://towardsdatascience.com/how-to-split-data-into-three-sets-train-validation-and-test-and-
why-e50d22d3e54c.
[19] A. I. Herghelegiu and H. Lu, Improving Negative Sampling in Graph Neural Networks for
Predicting Drug-Drug Interactions, IEEE International Conference on Bioinformatics and
Biomedicine, 2021. doi: 10.1109/BIBM52615.2021.9669483.
[20] Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah, Data
Augmentation for Graph Neural Networks, 2020. doi: https://doi.org/10.48550/arXiv.2006.06830
[21] S. Karagiannakos, Graph Neural Networks - An overview, 2020. URL:
https://theaisummer.com/Graph_Neural_Networks/.
[22] William L. Hamilton, Rex Ying, Jure Leskovec, Inductive Representation Learning on Large
Graphs, 2018. doi: https://doi.org/10.48550/arXiv.1706.02216.
[23] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua, Neural Graph Collaborative
Filtering, 2020. doi: https://doi.org/10.1145/3331184.3331267.
[24] I. Mayachita, Understanding Graph Convolutional Networks for Node Classification, 2020. URL:
https://towardsdatascience.com/understanding-graph-convolutional-networks-for-node-
classification-a2bfdb7aba7b.
[25] Petar Velickovi, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua
Bengio, Graph Attention Networks, 2018. doi: https://doi.org/10.48550/arXiv.1710.10903
[26] Top-K Accuracy Score, 2023. URL: https://scikit-
learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html.
[27] Shimin Xiong, Shimin Xiong, Bin Li, Shiao Zhu, DCGNN: a single-stage 3D object detection
network based on density clustering and graph neural network, 2022.