HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

Zhong, Hongwei; Wang, Mingyang; Zhang, Xinyue

doi:10.3390/electronics12092124

Cited by 2 publications

(2 citation statements)

References 29 publications

(33 reference statements)

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“…In [20], node embedding was performed using Transformer. First, in the process of generating node embeddings using Transformer, the target node is used as a query and the neighboring nodes are used as keys to calculate the attachment weights.…”

Section: Related Workmentioning

confidence: 99%

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Choi,

Buu

2024

Electronics

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Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.

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Section: Related Workmentioning

confidence: 99%

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Choi,

Buu

2024

Electronics

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“…With the rise of graph neural networks in many fields related to molecular structure, image processing, etc. [15][16][17], it has also given rise to thoughts about its application of scheduling problems. Zeng et al [18] first applied graph representation learning to scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

Combining Reinforcement Learning Algorithms with Graph Neural Networks to Solve Dynamic Job Shop Scheduling Problems

Zhong

Jiao

2023

Processes

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Smart factories have attracted a lot of attention from scholars for intelligent scheduling problems due to the complexity and dynamics of their production processes. The dynamic job shop scheduling problem (DJSP), as one of the intelligent scheduling problems, aims to make an optimized scheduling decision sequence based on the real-time dynamic job shop environment. The traditional reinforcement learning (RL) method converts the scheduling problem with a Markov process and combines its own reward method to obtain scheduling sequences in different real-time shop states. However, the definition of shop states often relies on the scheduling experience of the model constructor, which undoubtedly affects the optimization capability of the reinforcement learning model. In this paper, we combine graph neural network (GNN) and deep reinforcement learning (DRL) algorithm to solve DJSP. An agent model from job shop state analysis graph to scheduling rules is constructed, thus avoiding the problem that traditional reinforcement learning methods rely on scheduling experience to artificially set the state feature vectors. In addition, a new reward function is defined, and the experimental results prove that our proposed reward method is more effective. The effectiveness and feasibility of our model is demonstrated by comparing with general deep reinforcement learning algorithms on minimizing the earlier and later completion time, which also lays the foundation for solving the DJSP later.

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HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

Cited by 2 publications

References 29 publications

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Combining Reinforcement Learning Algorithms with Graph Neural Networks to Solve Dynamic Job Shop Scheduling Problems

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