Private conversations in social Virtual Reality (VR) environments lack the nuanced cues of physical interactions, potentially diminishing the sense of privacy and social presence. This paper introduces Whisper, a novel multisensory interaction technique designed to enhance private conversations for social VR applications. We first conducted a formative study (N=20) to understand private conversation demands, limitations of existing methods, and user expectations in social VR. Informed by these insights, Whisper incorporates visual (avatar proximity, gestures and illumination), auditory (voice conversation), and tactile (simulated airflow) elements to simulate the act of whispering, providing users with an intuitive and immersive method of private communication. The technique also features a contextual record to maintain conversation continuity. We evaluated Whisper through a comparative user study (N=24) in party and classroom scenarios. Results demonstrate that Whisper significantly outperforms existing methods in sense of privacy, mode distinguishability, intimacy, perceptual realism, and social presence.

Recently, polynomial graph filter learning (PGFL) has demonstrated promising performance for modeling graph signals in Graph Neural Networks (GNNs) on both homophilic and heterophilic graphs, owning to their flexibility and expressiveness. In this work, we conduct a novel preliminary study to explore the potential and limitations of PGFL, revealing a severe overfitting issue. To improve the effectiveness of PGFL, we propose Auto-Polynomial, a novel and general automated PGFL framework that efficiently learns better filters capable of adapting to various complex graph signals. Comprehensive experiments and ablation studies demonstrate significant and consistent performance improvements on both homophilic and heterophilic graphs across multiple learning settings considering various labeling ratios, which unleashes the potential of PGFL.