LEMD: Latent Environment Extrapolation and Message Disentanglement for Dynamic Graph Under Distribution Shift

Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion

This work proposes Graph Diffusion Counterfactual Explanation via Inversion (GDCE-I), a discrete denoising diffusion model with a novel discrete inversion scheme that enables distribution-aware edits leveraging the whole domain edit space and qualitatively shows that GDCE-I attains interpretable in-distribution solutio...

Rethinking Generalization in Graphs: A Hierarchical Interaction Perspective for Generalist 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 su...

Disentangled Hypergraph Network with Implicit Structure Learning for Mobility Social Relationship Inference

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.

Bridging the Gap Between Plausibility and Admissibility: Constraint-Aware Flow Maps for Dynamic Graph Systems

Robust Cross-Domain Sequential Recommendation Driven by Causal Disentanglement and Counterfactual Generation

Sequential recommendation systems are confronted with the dual challenges of data sparsity and domain bias. Especially in cross-domain scenarios, user interests are deeply coupled with domain-specific noise, which severely restricts recommendation performance. Targeted at improving the robustness of cross-domain sequen...

Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models

While highly stochastic DLM loss landscapes naturally resist gradient-based adversarial suffixes, they provide no guaranteed defense against natural noise, proving that everyday robustness is weight-dependent rather than inherently architectural.