# Process Discovery for Event Logs with Multi-Occurrence Event Types
## 2 Related Works
### Graph Neural Networks in Process Mining  
As neural network technology proves its efficiency in many application areas, there is active research on the development of process discovery applying neural networks. The main strength of the neural network technology over the automaton and pattern-matching approaches is that it can also process complex structures and noisy datasets. To analyze interactions and dependencies within event-based process flows, researchers have explored various approaches, including both graph neural network (GNN) [47] models and alternative machine learning techniques. GNNs have proven effective for link prediction in event sequence analysis, where they capture complex graph-based dependencies. For instance, [PERSON] and [PERSON]'s work on semi-supervised GNNs [48] laid the groundwork for using these networks to infer missing links, leveraging node features and graph structures. Their approach achieved notable accuracy in static graphs but faced challenges in adapting to dynamic, trace-based datasets, where graph structures emerge over time. [PERSON] et al. 's GraphSAGE model introduced an efficient node-embedding technique via sampling [49], which is particularly useful in event traces with sparse connections between events. Compared to [PERSON] and [PERSON]'s approach, GraphSAGE is more adaptable to dynamic graphs due to its inductive nature. However, its reliance on sampling can lead to information loss in rare but significant transitions. [PERSON] et al. 's graph attention networks (GATs) assign varying importance to edges, enhancing the interpretability of high-probability links [50]. Unlike GraphSAGE, which emphasizes embedding efficiency, GATs provide edge-level attention, improving interpretability. However, their reliance on predefined edge weights makes them less effective in trace-based datasets with uncertain or evolving connections. Pattern-matching techniques have also been used to identify recurring structures or motifs within event sequences [16]. These methods excel in rigid environments with well-defined rules but struggle to adapt to dynamic datasets. In contrast, deep learning methods like GNNs provide greater adaptability in scenarios with evolving patterns. Sequence-based models, such as long short-term memory networks (LSTMs), excel at capturing temporal dependencies within sequential data [23]. However, they focus primarily on the temporal order of events, often overlooking the relational structures critical for graphs. Compared to GNNs, LSTMs are less effective in capturing the interplay of relational and temporal dependencies in event-based datasets. DeepWalk, a random-walk-based model, generates low-dimensional embeddings through graph connectivity [51]. While effective for static graphs, DeepWalk struggles with dynamically evolving structures, as its embeddings rely on fixed connectivity patterns. Statistical methods, such as those by [PERSON] et al. [52], provide insights into graph properties using metrics like clustering coefficients and edge density. Although these methods enhance interpretability, they lack predictive power compared to GNN-based techniques. When comparing these methods, GNNs stand out for their ability to model relational dependencies, with GraphSAGE offering scalability and GATs enhancing interpretability. LSTMs are strong in temporal modeling but fall short in capturing structural relationships, while pattern-matching techniques and statistical methods provide useful insights but lack adaptability. DeepWalk bridges connectivity-based learning and dimensionality reduction but struggles with dynamic graphs. Graph neural networks (GNNs) offer a mechanism to embed graphs explicitly [53]. These networks process graph data to produce vector representations, which can be used for tasks such as classification. GNNs achieve this by learning graph embeddings guided by specific training objectives. A key challenge in GNNs is adapting the concept of convolution to graph structures, inspired by traditional convolutional neural networks.