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In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks.
However, scaling them to large graphs is challenging due to the high computational and storage costs of repeated feature propagation and non-linear transformation during training.
One commonly employed approach to address this challenge is model-simplification, which only executes the $\textbf{P}$ropagation ($\textbf{P}$) once in the pre-processing, and  
$\textbf{C}$ombine ($\textbf{C}$) these receptive fields in different ways and then feed them into a simple model for better performance.
Despite their high predictive performance and scalability, these methods still face two limitations.
First, existing approaches mainly focus on exploring different $\textbf{C}$ methods from the model perspective, neglecting the crucial problem of performance degradation with increasing $\textbf{P}$ depth from the data-centric perspective, known as the over-smoothing problem.
Second, pre-processing overhead takes up most of the end-to-end processing time, especially for large-scale graphs.
To address these limitations, we present random walk with noise masking (RMask), a plug-and-play module compatible with the existing model-simplification works. 
This module enables the exploration of deeper GNNs while preserving their scalability.
Unlike the previous model-simplification works, we focus on continuous $\textbf{P}$ and found that the noise existing inside each $\textbf{P}$ is the cause of the over-smoothing issue, and use the efficient masking mechanism to eliminate them.
Experimental results on six real-world datasets demonstrate that model-simplification works equipped with RMask yield superior performance compared to their original version and can make a good trade-off between accuracy and efficiency.

AAAI 2025

Towards Scalable and Deep Graph Neural Networks via Noise Masking

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Multimodal aspect-based sentiment analysis (MABSA) integrates text and images to perform fine-grained sentiment analysis on specific aspects, enhancing the understanding of user opinions in various applications. Existing methods use modality alignment for information interaction and fusion between images and text, but an inherent gap between these two modalities necessitates a more direct bridging mechanism to effectively connect image understanding with text content. For this, we propose the Descriptions Enhanced Question-Answering Framework (DEQA), which generates descriptions of images using GPT-4, leveraging the multimodal large language model to provide more direct semantic context of images. In DEQA, to help the model better understand the task's purpose, we frame MABSA as a multi-turn question-answering problem to add semantic guidance and hints. We input text, image, and description into separate experts in various combinations, allowing each expert to focus on different features and thereby improving the comprehensive utilization of input information. By integrating these expert outputs within a multi-turn question-answering format, we employ a multi-expert ensemble decision-making approach to produce the final prediction results. Experimental results on two widely-used datasets demonstrate that our method achieves state-of-the-art performance. Furthermore, our framework substantially outperforms GPT-4o and other multimodal large language models, showcasing its superior effectiveness in multimodal sentiment analysis.

DEQA: Descriptions Enhanced Question-Answering Framework for Multimodal Aspect-Based Sentiment Analysis

Federated Learning (FL) enables collaborative learning from distributed data while preserving the privacy of participating clients. While supervised federated learning with labeled data has made notable strides and achieved success, federated semi-supervised learning (FSSL) lags in its progress. Existing works for FSSL heavily rely on fully-labeled clients, while ignoring the distribution of pseudo-labels generated from skewed unlabeled data. In this work, we offer empirical and theoretical insights into the challenges encountered when applying conventional semi-supervised algorithms in the federated regime. Specifically, we highlight how the inherent data heterogeneity in FSSL can exacerbate issues within the pseudo-labeling process. Motivated by these observations, we propose federated learning with progressive distribution matching (FedPDM) to regularize the distribution of pseudo-labels, aiming to progressively reshape it to align with the ground-truth distribution. The matching problem could be formulated as an optimal transport (OT) problem and efficiently solved by Sinkhorn-Knopp iteration. Through extensive experiments, we demonstrate the superiority of FedPDM on a variety of models and datasets compared with prior arts for FSSL.

Progressive Distribution Matching for Federated Semi-Supervised Learning

In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend  "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss individual fairness. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.

Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness

Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to the contrasting modeling paradigms of discrete sequences. We introduce \textbf{FoldTokenizer} to represent protein sequence-structure as discrete symbols. This approach involves projecting residue types and structures into a discrete space, guided by a reconstruction loss for information preservation. We name the learned discrete symbols as \textbf{FoldToken}, and the sequence of FoldTokens serves as a new protein language, transforming the protein sequence-structure into a unified modality. We apply the created protein language on general backbone inpainting task, building the first GPT-style model (\textbf{FoldGPT}) for sequence-structure co-generation with promising results. Key to our success is the substantial enhancement of the vector quantization module, Soft Conditional Vector Quantization (\textbf{SoftCVQ}). Code will be released upon acceptance.

FoldToken: Learning Protein Language via Vector Quantization and Beyond

In the age of data and AI, study or forecasting relations among entities have paramount importance. But most often we forget that real-world relations are directional, for example, buyer-seller, sender-receiver, etc. In this paper, we provide a comprehensive solution to the problem of forecasting directional relations, in a very challenging setting, where relations are higher order i.e., directed hyperedges in a hypergraph. This problem is, so far, not addressed in the literature. This challenge here is that the number of possible hyperedges is exponential in the number of nodes at each event time. In this paper we propose a sequential generative process that divides the forecasting into multiple stages, each conditioned upon all the previous, thereby reducing the search space involved in finding the type of hyperedge occurring. The first is a temporal point process-based node event forecasting module that identifies the subset of nodes involved in an event. The second is a candidate generation module that forecasts hyperedge sizes and adjacency vectors for the nodes observing events. The third module is a directed hyperedge predictor that identifies the truth by searching over the set of candidate hyperedges. To demonstrate the efficiency of our model, we curated five datasets and conducted an extensive empirical study to evaluate each of the downstream tasks. Our model gave 32\% and 41\%  performance gain compared to the state-of-the-art pairwise and hyperedge event forecasting models, respectively, for the event type prediction.

Neural Temporal Point Processes for Forecasting Directional Relations in Evolving Hypergraphs

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning.  To address this gap, our study introduces the concept of *causal epistemic consistency*, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating intermediates with nuanced differences in causal reasoning. We propose a suite of novel metrics -- intensity ranking concordance, cross-group position agreement, and intra-group clustering -- to evaluate LLMs on this front. Through extensive empirical studies on 21 high-profile LLMs, including GPT-4, Claude3, and LLaMA3-70B, we have favoring evidence that current models struggle to maintain epistemic consistency in identifying the polarity and intensity of intermediates in causal reasoning. Additionally, we explore the potential of using internal token probabilities as an auxiliary tool to maintain causal epistemic consistency.  In summary, our study bridges a critical gap in AI research by investigating the self-consistency over fine-grained intermediates involved in causal reasoning.

Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties 
such as drug-like properties (e.g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites. 
To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of 
our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules.
Our model ensures explicit control over these molecular attributes while maintaining equivariance of coordinate representation and invariance of data likelihood. 
Furthermore, we introduce a novel alignment-based coordinate loss to adapt equivariant networks for auto-regressive de-novo 3D molecule generation from scratch.
Extensive experiments validate our model's effectiveness on property-guided and context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets.

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation

Can LLMs continually improve their previous outputs for better results? An affirmative answer would require LLMs to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first introduce a unified framework that allows us to compare the generative and discriminative capability of any model on any task. Then, in our resulting experimental analysis of several LLMs, we do not observe the performance of those models on discrimination to be reliably better than generation. We hope these findings inform the growing literature on self-improvement AI systems.

SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses

We propose Intra and Inter Parser-Prompted Transformers (PPTformer) that explore useful features from visual foundation models for image restoration. Specifically, PPTformer contains two parts: an Image Restoration Network (IRNet) for restoring images from degraded observations and a Parser-Prompted Feature Generation Network (PPFGNet) for providing IRNet with reliable parser information to boost restoration. To enhance the integration of the parser within IRNet, we propose Intra Parser-Prompted Attention (IntraPPA) and Inter Parser-Prompted Attention (InterPPA) to implicitly and explicitly learn useful parser features to facilitate restoration. The IntraPPA re-considers cross attention between parser and restoration features, enabling implicit perception of the parser from a long-range and intra-layer perspective. Conversely, the InterPPA initially fuses restoration features with those of the parser, followed by formulating these fused features within an attention mechanism to explicitly perceive parser information. Further, we propose a parser-prompted feed-forward network to guide restoration within pixel-wise gating modulation. Experimental results show that PPTformer achieves state-of-the-art performance on image deraining, defocus deblurring, desnowing, and low-light enhancement.

Intra and Inter Parser-Prompted Transformers for Effective Image Restoration

Partial label learning (PLL) addresses situations where each training example is associated with a set of candidate labels, among which only one corresponds to the true class label. As the candidate labels often come from crowdsourced workers, their generation is inherently dependent on the features of the instance. Existing PLL methods primarily aim to resolve these ambiguous labels to enhance classification accuracy, overlooking the opportunity to use this feature dependency for causal representation learning. This focus on accuracy can make PLL systems vulnerable to stylistic variations and shifts in domain. In this paper, we explore the learning of causal representations within an instance-dependent PLL framework, introducing a new approach that uncovers identifiable latent representations. By separating content from style in the identified causal representation, we introduce CausalPLL+, an algorithm for instance-dependent PLL based on causal representation. Our algorithm performs exceptionally well in terms of both classification accuracy and generalization robustness. Qualitative and quantitative experiments on instance-dependent PLL benchmarks and domain generalization tasks verify the effectiveness of our approach.

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