Information diffusion prediction is crucial for understanding social network dynamics, yet existing methods often neglect user participation uncertainty. This oversight typically stems from an implicit participation homogeneity assumption, which treats all observed interactions as equally reliable propagation signals, leading to fragile inferred topologies and uncertainty contamination. To address this, we propose SIEVE, a novel framework employing two synergistic strategies. First, robust node representations are learned via controllable uncertainty injection coupled with associated contrastive learning, mitigating topological fragility. Second, an uncertainty-aware directed graph aggregation mechanism is introduced, which dynamically constructs asymmetric aggregation topologies with adaptive weighting, thereby suppressing uncertainty contamination. Experiments on four public datasets demonstrate that SIEVE significantly outperforms state-of-the-art methods, offering valuable insights for designing robust information diffusion prediction models.

The significant societal impact of online public opinion has spurred extensive research into the underlying mechanisms of information diffusion. While existing diffusion prediction models have effectively leveraged network structure, they often lack a deep understanding of the intrinsic patterns governing information dissemination, thus limiting their predictive power. To address this critical gap, we introduce PMRCA, a novel information diffusion prediction model inspired by social psychology and driven by fundamental propagation patterns. PMRCA posits that two key patterns underpin topic propagation: multisource resonance, where consensus emerges from consistent narratives across multiple sources, and cognitive adaptation, where information is tailored to align with audience cognitive features. Correspondingly, PMRCA incorporates two dedicated learning tasks: structural contrastive learning based on node homogeneity to capture multisource resonance, and clustering contrastive learning based on cognitive clusters to model cognitive adaptation. Extensive experiments on four real-world datasets demonstrate that PMRCA significantly outperforms existing mainstream models. Importantly, our pattern-driven approach is model-agnostic, allowing it to be flexibly applied to various diffusion prediction scenarios and enhance performance.

Software defect prediction technology aids the reliability assurance team in identifying defect-prone code and assists the team in reasonably allocating limited testing resources. Recently, researchers assumed that the topological associations among code fragments could be harnessed to construct defect prediction models. Nevertheless, existing graph-based methods only concentrate on features of single-view association, which fail to fully capture the rich information hidden in the code. In addition, software defects may involve multiple code fragments simultaneously, but traditional binary graph structures are insufficient for representing these multivariate associations. To address these two challenges, this article proposes a multiview hypergraph representation learning approach (MVHR-DP) to amplify the potency of code features in defect prediction. MVHR-DP initiates by creating hypergraph structures for each code view, which are then amalgamated into a comprehensive fusion hypergraph. Following this, a hypergraph neural network is established to extract code features from multiple views and intricate associations, thereby enhancing the comprehensiveness of representation in the modeling data. Empirical study shows that the prediction model utilizing features generated by MVHR-DP exhibits superior area under the curve (AUC), F-measure, and matthews correlation coefficient (MCC) results compared to baseline approaches across within-project, cross-version, and cross-project prediction tasks.

Defect prediction technology helps software quality assurance teams understand the distribution of software defects, which can assist them to allocate testing and verification resources appropriately. Current visualization-based software defect prediction methods lack spatial and global information of code images during the feature extraction process. To solve the problem of incomplete information, this paper proposes a Convolutional Neural Network with Global Self-Attention (CNN-GSA). The method converts codes into corresponding images and uses an improved convolutional neural network, which combines channel attention, spatial attention, and self-attention mechanisms in a global attention layer, to extract defect-related structural and semantic features in code images. Empirical study shows that the model built with the features generated by CNN-GSA can achieve better F-measure results in defect prediction tasks.