AK
A. Kārkliņš
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Accurately predicting future interactions in temporal networks is essential for diverse applications, including disease modeling and transparent policy-making under regulatory requirements. While much research has focused on topological or temporal link prediction, relatively few studies address temporal weighted link prediction, where both the presence of future links and their intensity are critical. This paper develops an interpretable approach for forecasting weighted links over time, using contact networks that are aggregated into temporal weighted snapshots. By adapting the Self-Driven (SD) and Self-Cross-Driven (SCD) models introduced in Zou et al. (2023), we design prediction methods that incorporate memory decay while preserving explanatory clarity—avoiding the “black-box” nature common to deep learning.
We benchmark four methods - Baseline, SD, SCD, and an extended SCD* - across a range of physical (face-to-face) and virtual (online communication) contact networks, each exhibiting unique structural and dynamic properties when aggregated into temporal weighted networks. Our results show that the SCD model achieves a 32.86% reduction in Mean Squared Error (MSE) over the Baseline, while SD yields a 19.31% improvement. Moving from SD to SCD confers an additional 15.87% decrease in MSE, underscoring the benefits of incorporating both temporal and structural information. Although SCD* introduces further complexity, it did not show any consistent improvements in predictive performance across datasets.
Additional evaluations using the Area Under the Precision-Recall Curve (AUPRC) highlight dataset-specific variability in capturing active links. Correlation analysis reveals that MSE scales with average link weight distributions, whereas AUPRC correlates strongly with the proportion of active links per network snapshot. These findings emphasize that incorporating decay and structural context, in an interpretable manner, significantly enhances predictive accuracy, although parameter tuning remains crucial for different network topologies and interaction patterns. ...
We benchmark four methods - Baseline, SD, SCD, and an extended SCD* - across a range of physical (face-to-face) and virtual (online communication) contact networks, each exhibiting unique structural and dynamic properties when aggregated into temporal weighted networks. Our results show that the SCD model achieves a 32.86% reduction in Mean Squared Error (MSE) over the Baseline, while SD yields a 19.31% improvement. Moving from SD to SCD confers an additional 15.87% decrease in MSE, underscoring the benefits of incorporating both temporal and structural information. Although SCD* introduces further complexity, it did not show any consistent improvements in predictive performance across datasets.
Additional evaluations using the Area Under the Precision-Recall Curve (AUPRC) highlight dataset-specific variability in capturing active links. Correlation analysis reveals that MSE scales with average link weight distributions, whereas AUPRC correlates strongly with the proportion of active links per network snapshot. These findings emphasize that incorporating decay and structural context, in an interpretable manner, significantly enhances predictive accuracy, although parameter tuning remains crucial for different network topologies and interaction patterns. ...
Accurately predicting future interactions in temporal networks is essential for diverse applications, including disease modeling and transparent policy-making under regulatory requirements. While much research has focused on topological or temporal link prediction, relatively few studies address temporal weighted link prediction, where both the presence of future links and their intensity are critical. This paper develops an interpretable approach for forecasting weighted links over time, using contact networks that are aggregated into temporal weighted snapshots. By adapting the Self-Driven (SD) and Self-Cross-Driven (SCD) models introduced in Zou et al. (2023), we design prediction methods that incorporate memory decay while preserving explanatory clarity—avoiding the “black-box” nature common to deep learning.
We benchmark four methods - Baseline, SD, SCD, and an extended SCD* - across a range of physical (face-to-face) and virtual (online communication) contact networks, each exhibiting unique structural and dynamic properties when aggregated into temporal weighted networks. Our results show that the SCD model achieves a 32.86% reduction in Mean Squared Error (MSE) over the Baseline, while SD yields a 19.31% improvement. Moving from SD to SCD confers an additional 15.87% decrease in MSE, underscoring the benefits of incorporating both temporal and structural information. Although SCD* introduces further complexity, it did not show any consistent improvements in predictive performance across datasets.
Additional evaluations using the Area Under the Precision-Recall Curve (AUPRC) highlight dataset-specific variability in capturing active links. Correlation analysis reveals that MSE scales with average link weight distributions, whereas AUPRC correlates strongly with the proportion of active links per network snapshot. These findings emphasize that incorporating decay and structural context, in an interpretable manner, significantly enhances predictive accuracy, although parameter tuning remains crucial for different network topologies and interaction patterns.
We benchmark four methods - Baseline, SD, SCD, and an extended SCD* - across a range of physical (face-to-face) and virtual (online communication) contact networks, each exhibiting unique structural and dynamic properties when aggregated into temporal weighted networks. Our results show that the SCD model achieves a 32.86% reduction in Mean Squared Error (MSE) over the Baseline, while SD yields a 19.31% improvement. Moving from SD to SCD confers an additional 15.87% decrease in MSE, underscoring the benefits of incorporating both temporal and structural information. Although SCD* introduces further complexity, it did not show any consistent improvements in predictive performance across datasets.
Additional evaluations using the Area Under the Precision-Recall Curve (AUPRC) highlight dataset-specific variability in capturing active links. Correlation analysis reveals that MSE scales with average link weight distributions, whereas AUPRC correlates strongly with the proportion of active links per network snapshot. These findings emphasize that incorporating decay and structural context, in an interpretable manner, significantly enhances predictive accuracy, although parameter tuning remains crucial for different network topologies and interaction patterns.
The aim of this research is to discuss if it is possible or feasible enough to detect Mind-wandering of individuals using their hand and body movements from video recordings. The basis for this research is “Mementos”[9] data set, containing over 2000 recordings of people watching music videos. During experiment videos from data set were used to create software, that would automatically determine Mind-wandering. Results of the study have shown that body and hand movements are useful for detection of the phenomenon, however are not self-sufficient indicators for reliable detection. This study has found a reproducible methodology for automatic detection of Mind-wandering from pre-recorded videos, which is contributing towards exploration of this complex phenomenon in the field of Computer Science.
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The aim of this research is to discuss if it is possible or feasible enough to detect Mind-wandering of individuals using their hand and body movements from video recordings. The basis for this research is “Mementos”[9] data set, containing over 2000 recordings of people watching music videos. During experiment videos from data set were used to create software, that would automatically determine Mind-wandering. Results of the study have shown that body and hand movements are useful for detection of the phenomenon, however are not self-sufficient indicators for reliable detection. This study has found a reproducible methodology for automatic detection of Mind-wandering from pre-recorded videos, which is contributing towards exploration of this complex phenomenon in the field of Computer Science.