Applying graph neural networks on heterogeneous nodes and edge features. Mar 31, 2025 · 2.
Applying graph neural networks on heterogeneous nodes and edge features. These graph-structured data are initially composed of nodes, relationships, and the accompanying information, which are Sep 14, 2021 · Most state-of-the-art Graph Neural Networks focus on node features in the learning process but ignore edge features. However, existing heterogeneous graph models often struggle to capture long-range Mar 26, 2024 · Heterogeneous graphs are especially important in our daily life, which describe objects and their connections through nodes and edges. Graph neural networks (GNNs) are particularly adept at learning graph representations due to their ability to handle graph topologies. For instance, as shown in Fig. . To address Abstract. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local Unfortunately, previous graph neural network anomaly models are unable to fully capture the rich information and produce high-performing detections on these graphs, as they mostly focus on homogeneous graphs and node attributes only. For this complex network structure, many heterogeneous graph neural networks have been designed, but the traditional heterogeneous graph neural network has several obvious shortcomings: (1) Models using meta-paths require selection of meta-paths, failing to Mar 15, 2024 · Specifically, they cannot entirely capture the attributes of higher order neighbors or only use the higher order homogeneous neighbors, thus disregarding the attributes of heterogeneous neighbors. Most state-of-the-art Graph Neural Networks focus on node features in the learning process but ignore edge features. Mar 7, 2024 · Fundamental component of graph neural networks that iteratively aggregates and updates the features from neighbouring nodes, enabling the propagation of information throughout the graph structure. Adjacency Heterogeneous Graph Neural Networks (HGNNs) are advanced deep learning methods widely applied for learning representations of heterogeneous graphs. This GCN permits iterative extraction of information from the neighbors of a node and considers at each step both the features of the node and the weights of the edges. However, the entities and their interactions in real world always have multiple types and naturally form the heterogeneous s using a heterogeneous Graph Neural Network encoder and task-specific decoders. Compared to traditional GNNs designed for homogeneous graphs, HGNNs address the challenge of incorporating rich semantic information encoded in the heterogeneous structure. To address these problems, we propose a higher order heterogeneous graph neural network based on heterogeneous node attribute enhancement (HOAE). Abstract While there have been many works on Graph Neural Networks (GNNs), most of these approaches only consider homogeneous-node graphs without edge features. Jan 1, 2020 · As a unique non-Euclidean data structure for machine learning, graph analysis focuses on tasks such as node classification, link prediction, and clustering. However, Graph Neural Networks, which mainly focus on Feb 4, 2025 · Graph neural networks empowered by the Transformer’s self-attention mechanism have arisen as a preferred solution for many graph classification and prediction tasks. 2. We propose the Edge Feature Empowered Jumping Knowledge Graph Attention Network (EJGAT) architecture to break this limitation. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity Feb 5, 2024 · GNNs process this graph structure to learn representations (embeddings) of nodes or entire graphs that capture both their features and the topology of their connections. Graph Neural Networks are Feb 1, 2025 · However, current approaches primarily focus on node features, often neglecting the valuable information in edge features. These models are built on the traditional spatial graph neural networks (GNNs) framework of neighborhood sampling, message passing, and aggregation. However, many interesting problems feature different types of nodes and Heterogeneous graphs come with different types of information attached to nodes and edges. 1 Heterogeneous Graph Neural Networks Heterogeneous graph neural networks (HGNNs) have emerged as a powerful tool for modeling complex networks with diverse node and edge types. However, existing HGNNs often fail to explicitly leverage relational information among nodes when utilizing the attribute information of nodes for graph representation learning, thus constraining their performance. The edge features, which usually play a similarly important role as the nodes, are often ignored or simplified by these models. They are being used on structured data like tables and time-series, raster data like images and sequential data like natural language sentences. Feb 15, 2025 · To bridge this gap, this paper investigates the representation learning on heterogeneous graphs and propose a novel model named Heterogeneous Graph Neural Network with Relation-aware Label Propagation (RLP-HGNN). Zhang et al. In this section, we'll build and train a Graph Neural Network specifically designed for node classification on a heterogeneous graph. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. However, the Apr 24, 2025 · The vectorized representation of a knowledge graph is essential for effectively utilizing its implicit knowledge. Jan 30, 2025 · Heterogeneous Graphs are important data sources due to their rich representation of knowledge, primarily based on node features and relationships. Dec 21, 2022 · Graph Neural Networks (GNNs) have achieved excellent performance of graph representation learning and attracted plenty of attentions in recent years. Heterogeneous graphs come with different types of information attached to nodes and edges. Jan 16, 2024 · GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. MAGNN [16] employs intra-metapath aggregation layers to amalgamate intermediate semantic nodes and inter-metapath aggregation layers to consolidate May 23, 2024 · Abstract Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing node classification on heterogeneous graphs (HGs). Our contribution is the Hodge-Laplacian heterogeneous graph Jun 24, 2024 · Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). In this study, we present an Feb 22, 2025 · Self-supervised heterogeneous graph neural networks have shown remarkable effectiveness in addressing the challenge of limited labeled data. Instead, a set of types need to be specified for nodes and edges, respectively, each having its own data Feb 15, 2024 · In brief, we first construct two contrastive graph views, one from a heterogeneous graph that focuses on capturing complementary information to enhance the node representations and one from an extracted homogeneous subgraph that focuses on applying different strategies to low- and high- degree nodes to eliminate the degree bias. Most of GNNs aim to learn embedding vectors of the homogeneous graph which only contains single type of nodes and edges. Feb 21, 2024 · What is Graph Representation? Graph representation is a way to encode graph structure and features for processing by neural networks. Jan 30, 2025 · In recent years, heterogeneous graph neural networks have attracted considerable attention for their powerful graph processing capabilities and effectiveness in handling multiple types of nodes and relationships. Meta-paths [24 Feb 1, 2025 · The utilization of deep neural network-based techniques for embedding heterogeneous graphs has proven successful in recent years. Consequently, researchers have embraced these networks, applying them in various domains. Node-centric approaches are suboptimal in edge-sensitive graphs since edge features are not adequately utilized. In addition, considering the limited resources of the edge node, such as smartphones, it is impossible to train a precise model locally. However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the Sep 7, 2021 · Most state-of-the-art Graph Neural Networks focus on node features in the learning process but ignore edge features. Graph Neural Networks have gained significant attention for processing such complex data structures, delivering impressive results across various tasks like node classification. Originally proposed in Exploiting Edge Features for Graph Neural Networks by Gong and Cheng from the University of Kentucky in CVPR 2019, Enhanced Graph Neural Network (EGNN) (or Edge GNN or Edge GraphSAGE) refers to an extension of the GraphSAGE model that incorporates information from both nodes and edges in the graph. Recently, employing graph neural networks (GNNs) to heterogeneous graphs, known as heterogeneous graph neural networks (HGNNs) which aim to learn embedding in low-dimensional space while preserving heterogeneous structure and semantic for downstream tasks Jan 11, 2024 · The representation of heterogeneous graph nodes has become a hot research topic due to its diverse applications. Abstract—In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. Instead, a set of types need to be specified for nodes and edges, respectively, each having its own data Oct 8, 2023 · Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features Dec 21, 2022 · Existing survey papers of heterogeneous graph representation learning summarize all possible embedding techniques for graphs and make insufficient analysis for deep neural network models. ABSTRACT TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. Abstract Heterogeneous Graph Neural Networks (HGNNs) are a class of powerful deep learning methods widely used to learn representations of heterogeneous graphs. Mar 1, 2024 · The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. a. k. However, existing heterogeneous graph neural networks (HGNNS) largely rely on meta-paths to capture semantic information of Oct 21, 2022 · A simple graph for a transaction network for anti money laundering (AML) represents banks’ customers as nodes and transactions as edges. However, most of these models concentrate on only node features during the learning process. More recently, graph neural networks have emerged in addition, in order to apply established deep Feb 1, 2024 · Graph neural networks have demonstrated significant power in learning graph representations for homogeneous networks. Recent GNNs, including GCN [9], GraphSAGE [10] and GAT Dec 1, 2023 · Heterogeneous Graph Neural Network (HGNN) has shown a great promising in embedding complex structural and semantic information of heterogeneous graph. For instance, HAN [15] utilizes a hierarchical attention framework to derive embeddings for heterogeneous graphs. However, real-world network data can often be denoted by heterogeneous networks with different types of nodes and edges, such as social, traffic, and molecular networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. Broadly, HGNNs can be categorized into two May 1, 2021 · There needs to be a balance between data transmission and user privacy. Nov 4, 2023 · Heterogeneous graph neural network (HGNN) models, capable of learning low-dimensional dense vectors from heterogeneous graphs for downstream graph-mining tasks, have attracted increasing attention in recent years. However, this task is challenging in how to design a framework to ponder on the roles of nodes and relations in heterogeneous graphs in a collaborative manner. Applying a single message passing function across all edge types implicitly treats them as identical, which is often an oversimplification. Here are some of the most commonly used graph representations for Deep Learning. Edge is weighted in GAT, but edge attribute is not covered. Mar 1, 2025 · The variety of nodes and edges, which renders it difficult to encode semantically rich heterogeneous information into an accurate low-dimensional representation, poses a tremendous challenge for traditional graph neural networks. In this book, "Graph Neural Networks," we will delve into the core principles of graph theory and learn how to create custom datasets from raw or tabular data. Many graph neural network (GNN) models have been proposed to apply deep learning techniques to graph structures. After that, a Heterogeneous Graph Neural Network (H-GNN) is used to leverage implicit messages from neighbor nodes and edges propagating among nodes in heterogeneous graphs. To address this issue, we propose a multi-graph aggregated graph Sep 3, 2024 · Graph attention networks (GATs) are a promising subcategory of graph neural networks (GNNs). Nov 24, 2024 · After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (GCN) on the fused network and incorporates node features on graph-structured data for a node Jul 23, 2025 · Graph Convolutional Networks (GCNs): The first model in the GNN family, which messages the passing approach to forming node representations. Feb 1, 2024 · Characterized by diverse node and relation types, heterogeneous graphs have led to the development of heterogeneous graph neural networks, which possess the remarkable ability of modeling such heterogeneity. This chapter guides you through adapting GNNs for heterogeneous graphs, which contain diverse node and edge types, using approaches such as Relational GCN (RGCN) and Heterogeneous Attention Network (HAN Apr 1, 2024 · They propose a temporal transaction aggregation graph network and fuse topological interactions of transaction features into the feature representation by aggregating edge representations with temporal attributes around nodes, and finally combine the transaction features with the information and structural features obtained from the graph To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. Instead, a set of types need to be specified for nodes and edges, respectively, each having its own data Apr 16, 2025 · Heterogeneous graph neural networks (HGNNs) have gained significant attention in deep learning due to their superior capability in processing heterogeneous graph data. Jan 13, 2025 · Additionally, we identify the busbar information asymmetry problem that the popular homogeneous graph representation suffers from, and propose a heterogeneous graph representation to resolve it. We'll leverage the concepts discussed earlier, particularly focusing on handling multiple node and edge types. Mar 19, 2024 · Abstract Lots of neural network architectures have been proposed to deal with learning tasks on graph-structured data. Node-centric approaches are suboptimal in Mar 1, 2023 · A heterogeneous graph is defined as a directed graph G V, E consisting of different types of nodes and edges. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, triangles, and k-simplices, enabling the definition of graph-structured data on any k-simplices. However, although some researchers have proposed methods for heterogeneous graphs, they merely focus on node features while neglecting the In line with this, the present paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network constructed from real-world bank transactions and business role data belonging to DNB, Norway’s largest bank. It is common for these graphs to have significant data gaps, particularly in the nodes. Despite their efficacy, these networks are often hampered by their quadratic computational complexity and large model size, which pose significant challenges during graph training and inference. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type specific node features and different relations with type-specific edge features. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. ipynb 304-352 Hierarchical Attention Network (HAN) The Hierarchical Attention Network is specifically designed for heterogeneous graphs and employs a two-level attention Sep 17, 2024 · Heterogeneous Graph Neural Networks (HGNNs) have exhibited powerful performance in heterogeneous graph learning by aggregating information from various types of nodes and edges. This approach enables reusing architectures designed for homogeneous graphs. To better integrate these three aspects, a new semi-supervised graph neural network is proposed in this paper, called the CRHGNN (C entrality-based R elation aware Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification, link prediction, and graph classification. The developed explainer provides comprehensive explanations across multiple graphs. We train both homogeneous and heterogeneous GNNs and fully connected neural networks (FCNN) baselines on an imitation learning task. Following the previous Building upon the foundational architectures and training solutions from previous chapters, we now shift focus to applying Graph Neural Networks to more complex graph structures and tasks. However, they face challenges such as over-smoothing and non-robustness. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). However, many popular HGNNs fail to capture the meaningful characteristics to distinguish heterogeneous nodes, known as the semantic confusion problem. 3 days ago · Explore the fundamentals of Graph Neural Networks (GNNs) with this comprehensive beginner's guide covering concepts, applications, and practical implementation. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today’s information ecosystems. However, like GNNs, HGNNs face challenges in capturing high-order neighbor information without oversmoothing or Aug 5, 2024 · Heterogeneous graph neural networks have attracted considerable attention for their proficiency in handling intricate heterogeneous structures. ABSTRACT Graph Neural Networks (GNNs), originally proposed for node classi-fication, have also motivated many recent works on edge prediction (a. Abstract Heterogeneous graphs (HGs) also called heterogeneous information net-works (HINs) have become ubiquitous in real-world scenarios. Apr 25, 2023 · Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. Instead, a set of types need to be specified for nodes and edges, respectively, each having its own data Mar 1, 2025 · The variety of nodes and edges, which renders it difficult to encode semantically rich heterogeneous information into an accurate low-dimensional representation, poses a tremendous challenge for traditional graph neural networks. Graph neural networks (GNN) are neural network models that cap-ture the structure of the graph by message passing between the nodes in a graph. As a result, different types of nodes and edges can enhance their embedding through mutual integration and pro-motion. However, most existing HGNN models rely on meta−paths for feature extraction, which can only utilize part of the data from the graph The to_hetero function creates a separate instance of each GNN layer for each edge type, then aggregates the outputs. Abstract—Graph neural networks (GNNs) have proven effec-tive in capturing relationships among nodes in a graph. Jun 22, 2024 · Anomaly Detection on Bipartite Graphs with PyTorch Geometric Bipartite heterogeneous networks are present in many real-world systems. However, most existing methods model semantic relationships in heterogeneous graphs by manually defining meta-paths, inadvertently overlooking the inherent incompleteness of such graphs. May 2, 2024 · To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. For these models, metapath-based methods have been widely adopted. Sources: Chapter12/chapter12. Despite the fast development of HGNNs, they still face some challenges such as over-smoothing, and non-robustness. This paper combines existing deep learning techniques to study heterogeneous Feb 28, 2025 · Abstract Heterogeneous graphs, with diverse node and edge types, are prevalent in real-world scenarios. Mar 4, 2025 · Abstract Graph Neural Networks (GNNs) are specialized machine learning models designed for working with graph-structured data. φ: V → A is a node type mapping function, which indicates that each node v ∈ V belongs to a specific entity type in the entity type set A: φ v ∈ A. In practice, many datasets exhibit heterogeneous graph structures containing diverse types of nodes or edges. Apr 14, 2025 · HAN [5]: It is a semi-supervised graph neural network designed for heterogeneous graphs that converts heterogeneous graphs into multiple isomorphic graphs based on meta-path and uses a hierarchical attention mechanism to aggregate neighboring node features, which in turn generates node representations. However, limited work has been carried out on multiplex heterogeneous networks where multiple relations exist between the same pair of nodes, which is more realistic in real-world Oct 10, 2025 · The review maps the evolution of computational drug repositioning methods from feature-based and network techniques to advanced graph neural networks and generative AI. 1, an academic citation network (ACM) contains three distinct types of nodes: author, paper, and subject; and two pairs of edge connections: write/written and include/included between author-paper (A-P) and subject-paper (S-P) respectively. They directly operate on graphs, treating nodes as data points and edges as connections. regation from neighborhood allowing weighting a neighbor node according to node features. Hence, we propose to train a general mobile app recommendation model with sufficient data, then leverage edge nodes to do the inference. In order to exploit all the informati n given by these graphs, we propose to Graph neural networks (GNN) are neural network models that cap-ture the structure of the graph by message passing between the nodes in a graph. To address this prob-lem, we present the Edge-Featured Oct 28, 2024 · GNNs 101 Neural networks (NNs), in various flavors, have become the de-facto standard in pretty much every subfield of machine learning nowadays. They introduce the attention mechanism to GNNs, allowing them to dynamically weigh the importance of neighboring nodes during the aggregation process, which helps capture complex relationships and dependencies in graph-structured data. Heterogeneous graphs, on the other hand, contain multiple different node and edge types, which allows them to describe and capture a more complex system. Mar 11, 2024 · Graph analysis algorithms and machine learning techniques detect suspicious transactions that lead to phishing in large transaction networks. We will explore key graph neural network architectures to grasp essential concepts like graph convolution and self-attention. Thus, a single node or edge feature tensor cannot hold all node or edge features of the whole graph, due to differences in type and dimensionality. It has broad application prospects in social networks, computer vision [8-10], and other fields. Standard Graph Neural Networks, designed primarily for homogeneous graphs (single node and edge type), struggle to effectively capture the rich semantics embedded within these diverse relationships. , link prediction). However, current contrastive learning methods face limitations in leveraging neighborhood information for each node. However, most existing metapath-based HGNN models either discard intermediate nodes within a metapath, resulting in Mar 31, 2025 · 2. GraphSage learns representations for nodes, invariant in a fixed-size sample Nov 24, 2024 · After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (GCN) on the fused network and incorporates node features on graph-structured data for a node Sep 7, 2018 · Edge features contain important information about graphs. For example, heterogeneous graph with multiple node types and edge types. Jun 1, 2024 · Abstract Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. Heterogeneous graphs have been widely adopted to model complex systems for various ML tasks. However, extant approaches can only give consideration partly to three aspects: node structure, semantics and features. In this paper, we build a new framework for a family of new graph neural network models that can May 23, 2024 · Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing node classification on heterogeneous graphs (HGs). However, many interesting problems feature different types of nodes and edge features. g. On the other hand Nov 13, 2024 · With the strong capability of heterogeneous graphs in accurately modeling various types of nodes and their interactions, they have gradually become a research hotspot, promoting the rapid development of the field of heterogeneous graph neural networks (HGNNs). Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. This adaptability enables GATs to learn local patterns while Abstract—In light of the success of graph neural networks (GNNs), recent years have seen significant developments in mod-eling graph-structured data. Graphs embed the node’s data and also the relationship between the data points. Jan 1, 2025 · Edges and nodes form the core elements of heterogeneous graphs (HGs). However, current state-of-the-art neural network models designed for graph learning, e. Apr 8, 2024 · However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. GNNs excel at propagating information between connected nodes, enabling them to capture both local and global context. While there have been many works on Graph Neural Networks (GNNs), most of these approaches only consider homogeneous-node graphs without edge features. However, these GRAPH NEURAL NETWORKS GRAPH-STRUCTURED DATA Graphs represent relational data Entities Nodes: Node features: Relations Edges: Edge features: Edge Representations Adjacency Matrices Incidence Matrices optimized for sparse adjacency structure Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The first way falls into applying Graph Neural Networks (GNNs) for representation learning on graphs. Edge en- hanced graph neural network (EGNN) [25] addressed continuous multi-dimen Feb 15, 2024 · In brief, we first construct two contrastive graph views, one from a heterogeneous graph that focuses on capturing complementary information to enhance the node representations and one from an extracted homogeneous subgraph that focuses on applying different strategies to low- and high- degree nodes to eliminate the degree bias. To represent these connections between the nodes, graph representation is required. • It features a critical, quantitative comparison of methodologies, assessing trade-offs in scalability, data efficiency, and interpretability using appropriate performance May 1, 2024 · Graph representation learning based on Graph Neural Networks is used to automatically learn useful feature representations of heterogeneous graphs that capture the structural information and features of the nodes and edges. However, it is not clear how to augment the heterogeneous graphs without Dec 22, 2022 · A single node or edge feature tensor in a heterogenous graph cannot hold all node or edge features of the whole graph, due to differences in type and dimensionality. However, edge features also contain essential information in real-world, such as financial graphs. [13] propose the Heterogeneous Graph Neural Network (HetGNN), which innovatively combines a random walk with a restart for sampling heterogeneous neighbours and a two-module neural network to jointly encode and aggregate diverse node features, addressing both structural and content heterogeneity in graphs. In this paper, we present edge-featured graph attention A heterogeneous hypergraph neural network is an important neural network model that can effectively handle situations where nodes and edges in heterogeneous information networks have different attributes and meanings. In addition to enabling machine learning researchers and advanced developers, TF-GNN ofers low-code solutions to empower the broader developer community in graph Abstract—Heterogeneous Graph Neural Networks (HGNNs) have shown remarkable success in learning from real-world graph data by capturing complex heterogeneous characteristics like various semantic and relational information across different node and edge types. We sum-marize the relevant existing methods in the following three major ways. 2 Use case Multiple use cases can be formulated from SupplyGraph, and different graph types can be formulated according to the use cases. Based on the example of a power grid, we show that GNNs can learn to model heterogeneous nodes with little additional information given, but that It is shown that GNNs can learn to model heterogeneous nodes with little additional information given, but that special-purpose network architectures can improve upon that. To handle the heterogeneity, we design a relation-aware label propagation to obtain pseudo-labels of nodes in heterogeneous graphs. Previous studies have shown that these problems can be reduced by using gradient regularization methods. Due to its convincing performance, GNN has become a widely applied graph analysis method recently. May 7, 2025 · In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing graph-structured data across multiple domains, including social networks [1], [2], recommendation systems [3], [4], protein-molecular networks [5], and Knowledge Graphs [6], [7], [8]. In this report, the author selected a demanding forecasting use case, which is straightforward and one of the most typical examples of supply chain planning. Many derived GNN models have been proposed, including graph convolution, graph attention, and heterogeneous GNN, depending on the graph data used and the application. Some approaches utilize the local information of the target node, ignoring useful signals from deeper neighborhoods. However, GNN-based approaches face two main challenges: first, they fail to differentiate between the types of adjacent nodes during the Existing survey papers of heterogeneous graph representation learning summarize all possible embedding techniques for graphs and make insuficient analysis for deep neural network models. Existing methods can mitigate these issues by applying gradient regularization to one of the three information dimensions: node, edge, or propagation message. However, existing methods lack of elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as May 28, 2023 · The aforementioned graph neural network methods perform much better on downstream tasks than traditional heterogeneous graph embedding methods because they take into account both the graph's structure information and fully utilized nodes' features. igpx0 1nxikx g8 o4 nn4omz osq vja 375qmxa ljjab bxcc