AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies and robustly captures the nonlinear interplay between mobility and sociality.
A DHISL network that first captures individual spatiotemporal characteristics through feature-guided representation initialization, and then adopts a dual-branch framework that introduces dual constraints in a multi-channel disentangled space to achieve intra-channel consistency and inter-channel exclusivity.
Early and reliable rumor detection on social media requires joint modeling of multimodal semantics, user–post interactions, propagation structures, and temporal evolution. Existing methods usually enhance multimodal fusion, propagation modeling, or robustness separately, but they often rely on static feature fusion, homogeneous propagation structures, or weak temporal reasoning, which limits their effectiveness in early-stage and cross-event scenarios. To address these limitations, we propose THGNN-MRD, a temporal heterogeneous graph neural network for multimodal rumor detection. The core idea is to formulate rumor detection as dynamic event-level reasoning over a sequence of temporal heterogeneous graph snapshots. Specifically, each event is represented by posts, comments, users, and visual semantic nodes; relation-aware and time-aware bidirectional message passing is performed within each snapshot; and a Mamba state-space encoder is used to model cross-snapshot propagation evolution. To improve discriminability and robustness, THGNN-MRD further integrates gated multi-view fusion, cross-modal contrastive learning, temporal continuity regularization, and domain-adversarial optimization into a unified end-to-end framework. Experiments on four benchmark datasets, including Weibo, Twitter, FakeSV, and FVC, show that THGNN-MRD consistently outperforms representative state-of-the-art methods. It improves the F1-score by 1.2, 1.0, 1.0, and 1.4 percentage points over the strongest competing methods on the four datasets, respectively. Additional early-detection, ablation, sensitivity, efficiency, and gate-behavior analyses further confirm the effectiveness and practical applicability of the proposed framework. These results suggest that modeling rumors as evolving multimodal social events provides a principled and effective solution for trustworthy rumor detection.
With the increasing heterogeneity of social networks and online interaction systems, generalist graph anomaly detection (GAD) has become essential for identifying abnormal and fraudulent behaviors in complex environments. However, most existing GAD approaches rely heavily on domain-specific semantic alignment, which substantially restricts their ability to learn transferable node representations and often leads to poor generalization on unseen graph domains. To address this challenge, we propose HIerarchical Interaction MOdeling for zero-shot generalist GAD (termed HIMO-GAD). HIMO-GAD enables anomaly detection across diverse graph domains without retraining or access to target-domain supervision by modeling the evolutionary trajectories of node representations across hierarchical structural depths, thereby capturing interaction patterns that exhibit strong cross-domain stability. Specifically, HIMO-GAD integrates two core components: (1) a Dynamic Interaction Modeling Module that characterizes cross-layer interaction evolution to extract transferable representations, and (2) an Anomaly-Aware Regulation Mechanism that combines gradient immunity and centralization regularization to suppress overfitting and stabilize cross-domain generalization. Extensive experiments on multiple real-world graph datasets demonstrate that HIMO-GAD consistently outperforms state-of-the-art baselines in strict zero-shot settings, achieving up to a 10% improvement in key evaluation metrics and exhibiting strong generalization across heterogeneous graph domains.
A novel theoretical model, namely the multiway autoregressive (MARS) model, which characterizes multiple evolutionary paths by capturing dependencies within and across two core factors underlying diverse evolutionary mechanisms is proposed, which develops a general DGNN framework, a multiway autoregressive network (MAN).
Social recommendation alleviates data sparsity and cold-start issues by leveraging user social networks. However, real-world social graphs are often contaminated with noisy relations, such as weak or spurious links, which are amplified during graph propagation and degrade user representation learning. To address this issue, a core-interest-guided conditional diffusion denoising approach is proposed, where user core interests extracted from historical interactions are used as semantic conditions to progressively refine noisy social representations in the latent space. In addition, real-world recommendation is influenced not only by individual behaviors and social relations but also by global contextual factors, such as popularity trends and environmental biases. Effectively integrating these heterogeneous signals remains challenging. To this end, a multi-source semantic fusion module is developed, which employs soft-threshold multi-head attention and dynamic residual gating to adaptively align and fuse heterogeneous information. Based on these designs, a unified framework, termed CDD-MSF, is proposed. Extensive experiments on three real-world datasets demonstrate that CDD-MSF consistently outperforms state-of-the-art baselines in terms of Recall@K and NDCG@K, and exhibits strong robustness and generalization under highly sparse and noisy conditions.
A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.