16 accepted papers
Graph Masked AutoEncoders (GMAEs) have achieved notable success in diverse downstream tasks. However, their superior performance usually relies on reliable and unperturbed input graphs. In real-world scenarios, graphs are vulnerable to adversarial attacks (e.g., perturbations on node features or top
Graph Neural Networks (GNNs) have demonstrated impressive success across a range of graph-based tasks. However, their performance in node classification typically relies on enough high-quality labeled data which are difficult to obtain in practice. Self-training emerges as a promising solution to ta
Graph Structure Learning (GSL) aims to simultaneously enhance the original graph and the performance of Graph Neural Networks. However, existing GSL methods for node classification fail to consider neighborhood label dependencies during training, which limits their ability to refine the graph struct
Inferring latent social ties from large-scale spatiotemporal mobility traces is a foundational AI task with broad applicability. Existing hypergraph-based methods often model higher-order relations by treating hyperedges as static snapshots, thus failing to capture the temporal dynamics and co-evolu
Anomaly detection in dynamic graphs aims to capture the dynamic evolution characteristics of graphs, and then identify abnormal behaviors that deviate from normal patterns. However, previous studies fail to decouple periodic and bursty information during the time encoding process, which hinders thei
Dynamic Graph Neural Networks (DyGNNs) are facing challenges with distribution shifts between training and test data that are similar but not identical. Existing DyGNNs for out-of-distribution scenarios primarily focus on discovering invariant patterns in the spatial domain, overlooking its impact o
Multiplex heterogeneous networks are common in real-world scenarios, where entities interact through diverse types of relations across multiple semantic layers. Recent advances in multiplex heterogeneous graph neural networks have achieved remarkable results by incorporating node and relation types
Leveraging review texts to mine deep user preferences is vital for recommendation. However, existing methods neglect the positive-negative counteraction and rely on noisy hard sentiment thresholds. Furthermore, the feature density asymmetry causes dense semantic features to overwhelm sparse collabor
Sequential recommendation models analyze user historical behavior sequences to capture temporal dependencies and the dynamic evolution of interests, enabling accurate predictions of future behaviors. However, there are still two critical challenges that remain unsolved: i) Inadequate temporal modeli
Graph Transformer has shown great promise in capturing the dynamics of user preferences for sequential recommendations. However, the self-attention mechanism within its structure is of quadratic complexity, posing challenges for deployment on devices with limited resources. To this end, we propose a
Class imbalance is a widespread problem in graph-structured data. The existing studies tailored for class-imbalanced graphs are typically categorized into generative and re-weighting methods. However, the former merely focuses on quantity balance rather than learning balance. The latter performs the
Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the…
Integrating dynamics into graph neural networks (GNNs) provides deeper insights into the evolution of dynamic graphs, thereby enhancing the temporal representation in real-world dynamic network problems. Existing methods extracting critical information from dynamic graphs face two key challenges, ei
Graph Masked AutoEncoder (GMAE) has recently attracted vast interest in handling graph-related tasks by adopting the 'masking-reconstruction' learning paradigm. Most existing GMAE-based methods adhere to the homophily assumption, i.e., connected nodes share the same attributes or labels. However, th…
3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it faces significant challenges including spatially varying noise and anisotropic resolution, where the axial resolution la…
Automatic cell tracking for time-lapse images becomes more and more important for live cell studies because the manual tracking is extremely time consuming. In this paper, we proposed a method for C.elegans cell tracking based on probabilistic relaxation labeling (PRL). The experiment results obtain…