## 4 Experimental Results
### Addressing RQ1: Predicting Participant Behavior Using K-Core and Activity Metrics  
The purpose of this section is to analyze the network complexity of participants in GitHub projects using k-core decomposition and to predict long-term contribution activity based on this analysis. Specifically, we aim to examine how k values are distributed according to the duration of project activity and how this network complexity impacts the longevity of the project. The k-core value represents the centrality of a participant in a project, and a higher k-core value indicates stronger connectivity among participants. Figure 4: Feature Importance Based on SHAP Values for XGBoost Model. In this study, we categorized projects into five groups based on their duration and analyzed the distribution of k values to explore how a project's network structure influences its success and sustainability. Figure 5 shows the distribution of maximum k values for participants in each group of GitHub projects. The k-value ranges in the Sankey diagram were determined based on the distribution shown in the boxplot. As a result, projects in Group 1 tend to have lower k values, with most of these projects having weak connections among participants. In contrast, projects in Group 5 tend to exhibit higher k values, indicating that projects maintained over a longer period have more complex participant networks. This suggests that for a project to be successful in the long term, core participants must actively participate and occupy central roles within the network. Thus, as k values increase, participants tend to collaborate more closely within the project, playing a significant role in its long-term success and maintenance. The fact that long-term projects in Group 5 have higher k values underscores the importance of core participants with high network centrality in ensuring the sustainability of the project. Next, we analyze the impact of the k value on participant activity duration using SHAP values. Figure 6 shows the correlation between k values and their corresponding SHAP values, indicating that as the k value increases, participants are more likely to play a crucial role in the project and remain active over a longer period. SHAP analysis of k values clearly demonstrates that the network centrality of participants directly influences their activity duration in the project. Earlier, we examined the correlation between k values and activity duration. In this paragraph, we analyze how the group to which a participant belongs and their activity level (act_level) influence the participant's activity duration. Figure 5: K-Core Value Distribution by Project Group. Figure 7a illustrates the Partial Dependence Plot (PDP) for the group variable, which categorizes repositories based on their activity duration. The figure reveals that repositories with a duration of less than 12 months (groups 1 and 2) are associated with predominantly negative SHAP values. This indicates that participants in shorter-lived repositories are more likely to have shorter participation durations. In contrast, repositories with a duration of 18 months or longer (groups 4 and 5) exhibit positive SHAP values, suggesting that contributors to these projects are more likely to engage for extended periods. Additionally, contributors with higher k-core values, who play central roles in their networks, tend to be more concentrated in repositories belonging to these longer-duration groups. This finding underscores the importance of sustained project longevity in fostering long-term contributor engagement and highlights the pivotal role of core contributors in maintaining these projects. Figure 7b visualizes the relationship between activity level (act_level) and SHAP values. In this context, a lower act_level corresponds to more frequent activity. The plot shows that as act_level decreases, SHAP values increase significantly, implying that contributors with higher activity frequencies are more likely to participate for longer periods.