.png){kind=icon}
.png){kind=icon}
Return
Huge-graph-based risk communities mining and prediction: a smart campus security protection system toward effective students management
.png){kind=icon}
PRE
AI
T
DOI：10.1007/s13748-026-00428-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a scalable graph-based framework for assessing and clustering student risk profiles using campus-wide behavioral datasets. Developed for university security systems, the approach identifies risk-aligned student communities through hierarchical behavioral analysis. Activity features-such as late-night library access or facility usage frequency-are enriched with weakly supervised learning to capture diverse risk signals. A geometric feature selection strategy removes weak indicators, yielding refined behavioral representations. These are mapped into a latent space where students are modeled as probabilistic distributions over abstract risk factors, enabling precise threat differentiation. A weighted similarity graph supports institution-scale community detection via multi-resolution clustering, uncovering shared behavioral patterns such as irregular attendance or unusual social interactions. A prediction module integrates individual profiles with community trends to generate targeted alerts, strengthening early-warning capabilities. Tested on over 50,000 students, the framework achieved 89% accuracy, demonstrating scalability, robustness, and compliance with privacy standards.
Keywords:
Risk communities
Campus security
Behavioral analysis
Predictive intervention1
Jensen-Shannon divergence
Journal
P
IF:
2.4
Papers:
44
Citations:
0
Organization
Cited Papers