Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN.

Temporal Intention Localization (TIL) is crucial for video surveillance, focusing on identifying varying levels of suspicious intention to enhance security monitoring. However, existing discrete classification methods fail to capture the continuous progression of suspicious intentions, limiting early intervention and explainability. In this paper, we reconceptualize hidden intention modeling by shifting from discrete classification to continuous regression and propose Suspicion Progression Analysis Network (SPAN), which capture the fluctuations and progression of hidden intentions over time. Specifically, when analyzing the temporal progression of suspicion, we discover that suspicion exhibits long-term dependency and cumulative effects across extended sequences, characteristics significantly similar to the settings in Temporal Point Process (TPP) theory. Based on these insights, we formalize a suspicion score formula that models continuous changes while accounting for temporal characteristics. We also propose Suspicion Coefficient Modulation to adjust suspicion coefficients using multimodal information, reflecting different effects of suspicious actions. Notably, we introduce a Concept-Anchored Mapping method to quantify associations between suspicious actions and predefined intention concepts, enabling understanding of not just actions occurring but also their potential underlying intentions. Extensive experiments on the HAI dataset show that SPAN significantly outperforms existing methods, reducing MSE by 19.8% and improving average mAP by 1.78%,. Notably, SPAN achieves a 2.74% mAP gain in low-frequency cases, indicating superior capability in capturing subtle behavioral changes.Compared to discrete classification systems, out continuous suspicion modeling method enables earlier detection and more proactive interventions, substantially enhancing both system explainability and practical utility in security applications.