GenAI4ELab/papercli-papers huggingface dataset:
Out-of-Distribution Generalization on Graphs via Progressive Inference
Authors: Yiming Xu; Bin Shi; Zhen Peng; Huixiang Liu; Bo Dong; Chen Chen
Venue: AAAI 2025 (main)

Abstract: The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap between the causal parts learned by existing methods and the ground-truth, leading to undesirable performance. In response to the above issues, this paper presents GPro, a model that learns graph causal invariance with progressive inference. Specifically, the complicated graph causal invariant learning is decomposed into multiple intermediate inference steps from easy to hard, and the perception of GPro is continuously strengthened through a progressive inference process to extract causal features that are stable to distribution shifts. We also enlarge the training distribution by creating counterfactual samples to enhance the capability of the GPro in capturing the causal invariant parts. Extensive experiments demonstrate that our proposed GPro outperforms the state-of-the-art methods by 4.91% on average. For datasets with more severe distribution shifts, the performance improvement can be up to 6.86%.
Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective
Authors: Yiming Xu; Zhen Peng; Bin Shi; Xu Hua; Bo Dong; Song Wang; Chen Chen
Venue: AAAI 2025 (main)

Abstract: The superiority of graph contrastive learning (GCL) has prompted its application to anomaly detection tasks for more powerful risk warning systems. Unfortunately, existing GCL-based models tend to excessively prioritize overall detection performance while neglecting robustness to structural imbalance, which can be problematic for many real-world networks following power-law degree distributions. Particularly, GCL-based methods may fail to capture tail anomalies (abnormal nodes with low degrees). This raises concerns about the security and robustness of current anomaly detection algorithms and therefore hinders their applicability in a variety of realistic high-risk scenarios. To the best of our knowledge, research on the robustness of graph anomaly detection to structural imbalance has received little scrutiny. To address the above issues, this paper presents a novel GCL-based framework named AD-GCL. It devises the neighbor pruning strategy to filter noisy edges for head nodes and facilitate the detection of genuine tail nodes by aligning from head nodes to forged tail nodes. Moreover, AD-GCL actively explores potential neighbors to enlarge the receptive field of tail nodes through anomaly-guided neighbor completion. We further introduce intra- and inter-view consistency loss of the original and augmentation graph for enhanced representation. The performance evaluation of the whole, head, and tail nodes on multiple datasets validates the comprehensive superiority of the proposed AD-GCL in detecting both head anomalies and tail anomalies.
Reverse Region-to-Entity Annotation for Pixel-Level Visual Entity Linking
Authors: Zhengfei Xu; Sijia Zhao; Yanchao Hao; Xiaolong Liu; Lili Li; Yuyang Yin; Bo Li; Xi Chen; Xin Xin
Venue: AAAI 2025 (main)

Abstract: Visual Entity Linking (VEL) is a crucial task for achieving fine-grained visual understanding, matching objects within images (visual mentions) to entities in a knowledge base. Previous VEL tasks rely on textual inputs, but writing queries for complex scenes can be challenging. Visual inputs like clicks or bounding boxes offer a more convenient alternative. Therefore, we propose a new task, Pixel-Level Visual Entity Linking (PL-VEL), which uses pixel masks from visual inputs to refer to objects, supplementing reference methods for VEL. To facilitate research on this task, we have constructed the MaskOVEN-Wiki dataset through an entirely automatic reverse region-entity annotation framework. This dataset contains over 5 million annotations aligning pixel-level regions with entity-level labels, which will advance visual understanding towards fine-grained. Moreover, as pixel masks correspond to semantic regions in an image, we enhance previous patch-interacted attention with region-interacted attention by a visual semantic tokenization approach. Manual evaluation results indicate that the reverse annotation framework achieved a 94.8% annotation success rate. Experimental results show that models trained on this dataset improved accuracy by 18 points compared to zero-shot models. Additionally, the semantic tokenization method achieved a 5-point accuracy improvement over the trained baseline.
Federated Graph Condensation with Information Bottleneck Principles
Authors: Bo Yan; Sihao He; Cheng Yang; Shang Liu; Yang Cao; Chuan Shi
Venue: AAAI 2025 (main)

Abstract: Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks. However, existing GC methods rely on centralized data storage, which is unfeasible for real-world decentralized data distribution, and overlook data holders' privacy-preserving requirements. To bridge this gap, we propose and study the novel problem of federated graph condensation (FGC) for graph neural networks (GNNs). Specifically, we first propose a general framework for FGC, where we decouple the typical gradient matching process for GC into client-side gradient calculation and server-side gradient matching, integrating knowledge from multiple clients' subgraphs into one smaller condensed graph. Nevertheless, our empirical studies show that under the federated setting, the condensed graph will consistently leak data membership privacy, i.e., the condensed graph during federated training can be utilized to steal training data under the membership inference attack (MIA). To tackle this issue, we innovatively incorporate information bottleneck principles into the FGC, which only needs to extract partial node features in one local pre-training step and utilize the features during federated training. Theoretical and experimental analyses demonstrate that our framework consistently protects membership privacy during training. Meanwhile, it can achieve comparable and even superior performance against existing centralized GC and federated graph learning (FGL) methods.
Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data
Authors: Ziang Yan; Xingyu Zhao; Hanqing Ma; Wei Chen; Jianpeng Qi; Yanwei Yu; Junyu Dong
Venue: AAAI 2025 (main)

Abstract: With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data quality, high sparsity, and noise interference, which hinder existing methods from extracting cross-platform user information. To address these issues, we propose a Correlation-Attention Masked Transformer for User Identity Link age Network (MT-Link), a transformer-based framework to enhance model performance by learning spatio-temporal co-occurrence patterns of cross-platform users. Our model effectively captures spatio-temporal co-occurrence in cross-platform user check-in sequences. It employs a correlation attention mechanism to detect the spatio-temporal co-occurrence between user check-in sequences. Guided by attention weight maps, the model focuses on co-occurrence points while filtering out noise, ultimately improving classification performance. Experimental results show that our model significantly outperforms state-of-the-art baselines by 12.92%-17.76% and 5.80%-8.38% improvements in terms of Macro-F1 and Area Under Curve (AUC).
Co-Progression Knowledge Distillation with Knowledge Prototype for Industrial Anomaly Detection
Authors: Bokang Yang; Zhe Zhang; Jie Ma
Venue: AAAI 2025 (main)

Abstract: Unsupervised anomaly detection has emerged as a powerful technique for identifying abnormal patterns in images without relying on pre-labeled defective samples. Many unsupervised methods use pre-trained feature extractors from large datasets, with knowledge distillation between teacher and student models being a leading technique. However, due to the similar structures of teacher and student, these methods face challenges like excessive specialization and inadequate generalization, reducing detection performance. In this paper, we introduce a Co-Progression Knowledge Distillation (CPKD) framework, enabling bidirectional learning between teacher and student models. This innovative framework enables concurrent evolution of both models, fostering mutual improvement and enhanced adaptability. To maintain system stability and prevent overspecialization, we introduce a knowledge prototype as a regulatory mechanism for the teacher's learning process. Our method effectively addresses key challenges in anomaly detection, including insufficient learning and overadaptation, by striking a balance between acquiring new knowledge and preserving core competencies. We demonstrate significant improvements in detection accuracy, achieving SOTA performance on the MVTec dataset.
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning
Authors: Hui-Yue Yang; Hui Chen; Ao Wang; Kai Chen; Zijia Lin; Yongliang Tang; Pengcheng Gao; Yuming Quan; Jungong Han; Guiguang Ding
Venue: AAAI 2025 (main)

Abstract: Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Parameter-Efficient Fine-Tuning (PEFT) offers a promising solution, but it may yield suboptimal performance by not adequately addressing the perception challenges during adaptation to anomaly images. In this paper, we propose a novel Self-Perception Tuning (SPT) method, aiming to enhance SAM's perception capability for anomaly segmentation. The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process. Additionally, a visual-relation-aware adapter is introduced to improve the perception of discriminative relational information for mask generation. Extensive experimental results on several benchmark datasets demonstrate that our SPT method can significantly outperform baseline methods, validating its effectiveness.
Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer
Authors: Jinyu Yang; Ruijia Wang; Cheng Yang; Bo Yan; Qimin Zhou; Yang Juan; Chuan Shi
Venue: AAAI 2025 (main)

Abstract: Heterogeneous graphs (HGs) that contain various node and edge types are ubiquitous in real-world scenarios. Considering the common label sparsity problem in HGs, some researchers propose to pretrain on source HGs to extract general knowledge and then fine-tune on a target HG for knowledge transfer. However, existing methods often assume that source and target HGs share a single heterogeneity, meaning that they have the same types of nodes and edges, which contradicts the real-world scenarios requiring cross-heterogeneity transfer. Although a recent study has made some preliminary attempts in cross-heterogeneity learning, its definition of general knowledge heavily rely on human knowledge, which lacks flexibility and further leads to a suboptimal transfer. To address the problem, we propose a novel Language Model-enhanced Cross-Heterogeneity learning model, namely LMCH. Specifically, we first design a metapath-based corpus construction method to unify HG representations as languages. The corpora of source HGs are then used to fine-tune a pretrained Language Model (LM), enabling the LM to autonomously extract general knowledge across different HGs. Furthermore, to fully utilize the extensive unlabeled nodes in a few-labeled target HG, we propose an iterative training pipeline with the help of an extra Graph Neural Network (GNN) predictor, enhanced by LM-GNN contrastive alignment at the end of each iteration. Extensive experiments on four real-world datasets have demonstrated the superior performance of LMCH over state-of-the-art methods.
Curriculum Conditioned Diffusion for Multimodal Recommendation
Authors: Yimeng Yang; Haokai Ma; Lei Meng; Shuo Xu; Ruobing Xie; Xiangxu Meng
Venue: AAAI 2025 (main)

Abstract: Multimodal recommendation (MMRec) aims to integrate multimodal information of items to address the inherent data sparsity issue in collaborative-based recommendation. Traditional MMRec methods typically capture the structure-level item representations from the observed user behaviors within the multimodal graph, overlooking the potential impact of negative instances for personalized preference understanding. In light of the outstanding generative ability and step-by-step inference characteristic of Diffusion Models (DMs), we propose a Curriculum Conditioned Diffusion framework for Multimodal Recommendation (CCDRec), which precisely excavates the modality-aware distribution-level correlation among multi-modalities and elegantly integrates the reverse phase of DMs into negative sampling to highlight the most suitable instances in a curricular manner. Specifically, CCDRec proposes the Diffusion-controlled Multimodal Aligning module (DMA) to align multimodal knowledge with collaborative signals by capturing the fine-grained relationships among multi-modalities in the probabilistic distribution space. Furthermore, CCDRec designs the Negative-sensitive Diffusive Inferring module (NDI) to progressively synthesize the negative sample pool with diverse hardness to support the following knowledge-aware negative sampling. To gradually ramp up the training complexity, CCDRec further introduces a Curricular Negative Sampler (CNS) to tally the curriculum learning paradigm with the reverse phase of DMA, thereby adaptively sampling the gold-standard negative instances to enhance optimization. Extensive experiments on three datasets with four diverse backbones demonstrate the effectiveness and robustness of our CCDRec. The visualization analyses also clarify the underlying mechanism of our DMA in multimodal representation alignment and CNS in curricular negative discovery. The code and the corresponding dataset will be uploaded in the Appendix.
Erase Then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
Authors: Zhe-Rui Yang; Jindong Han; Chang-Dong Wang; Hao Liu
Venue: AAAI 2025 (main)

Abstract: Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs, particularly with large-scale graphs. To address these challenges, we propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning while preserving the model utility. Specifically, we first build a theoretical foundation showing that masking parameters critical for unlearned samples enables effective unlearning. Building on this insight, the Erase stage strategically edits model parameters to eliminate the impact of unlearned samples and their propagated influence on intercorrelated nodes. To further ensure the GNN's utility, the Rectify stage devises a gradient approximation method to estimate the model's gradient on the remaining dataset, which is then used to enhance model performance. Overall, ETR achieves graph unlearning without additional training or full training data access, significantly reducing computational overhead and preserving data privacy. Extensive experiments on seven public datasets demonstrate the consistent superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness, establishing it as a promising solution for real-world graph unlearning challenges.
Disentangled Modeling of Preferences and Social Influence for Group Recommendation
Authors: Guangze Ye; Wen Wu; Guoqing Wang; Xi Chen; Hong Zheng; Liang He
Venue: AAAI 2025 (main)

Abstract: The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregating group preferences. Actually, social influence is also an important factor in modeling users' contributions to the final group decision. However, existing methods either neglect the social influence of individual members or bundle preferences and social influence together as a unified representation. As a result, these models emphasize the preferences of the majority within the group rather than the actual interaction items, which we refer to as the preference bias issue in GR. Moreover, the self-supervised learning (SSL) strategies they designed to address the issue of group data sparsity fail to account for users' contextual social weights when regulating group representations, leading to suboptimal results. To tackle these issues, we propose a novel model based on Disentangled Modeling of Preferences and Social Influence for Group Recommendation (DisRec). Concretely, we first design a user-level disentangling network to disentangle the preferences and social influence of group members with separate embedding propagation schemes based on (hyper)graph convolution networks. We then introduce a social-based contrastive learning strategy, selectively excluding user nodes based on their social importance to enhance group representations and alleviate the group-level data sparsity issue. The experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two real-world datasets.
Disentangling Tabular Data Towards Better One-Class Anomaly Detection
Authors: Jianan Ye; Zhaorui Tan; Yijie Hu; Xi Yang; Guangliang Cheng; Kaizhu Huang
Venue: AAAI 2025 (main)

Abstract: Tabular anomaly detection under the one-class classification setting poses a significant challenge, as it involves accurately conceptualizing "normal" derived exclusively from a single category to discern anomalies from normal data variations. Capturing the intrinsic correlation among attributes within normal samples presents one promising method for learning the concept. To do so, the most recent effort relies on a learnable mask strategy with a reconstruction task. However, this wisdom may suffer from the risk of producing uniform masks, i.e., essentially nothing is masked, leading to less effective correlation learning. To address this issue, we presume that attributes related to others in normal samples can be divided into two non-overlapping and correlated subsets, defined as CorrSets, to capture the intrinsic correlation effectively. Accordingly, we introduce an innovative method that disentangles CorrSets from normal tabular data. To our knowledge, this is a pioneering effort to apply the concept of disentanglement for one-class anomaly detection on tabular data. Extensive experiments on 20 tabular datasets show that our method substantially outperforms the state-of-the-art methods and leads to an average performance improvement of 6.1% on AUC-PR and 2.1% on AUC-ROC.
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Authors: Yuyang Ye; Zhi Zheng; Yishan Shen; Tianshu Wang; Hengruo Zhang; Peijun Zhu; Runlong Yu; Kai Zhang; Hui Xiong
Venue: AAAI 2025 (main)

Abstract: Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).  Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques.  This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text modality information. Moreover, although Multimodal Large Language Models (MLLMs) capable of processing multi-modal inputs have emerged, how to equip MLLMs with multi-modal recommendation capabilities remains largely unexplored. To this end, in this paper, we propose the Multimodal Large Language Model-enhanced Sequential Multimodal Recommendation (MLLM-MSR) model. To capture the dynamic user preference, we design a two-stage user preference summarization method. Specifically, we first utilize an MLLM-based item-summarizer to extract image feature given an item and convert the image into text. Then, we employ a recurrent user preference summarization generation paradigm to capture the dynamic changes in user preferences based on an LLM-based user-summarizer. Finally, to enable the MLLM for multi-modal recommendation task, we propose to fine-tune a MLLM-based recommender using Supervised Fine-Tuning (SFT) techniques. Extensive evaluations across various datasets validate the effectiveness of MLLM-MSR, showcasing its superior ability to capture and adapt to the evolving dynamics of user preferences.
Diffusion-based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection
Authors: Suhee Yoon; Sanghyu Yoon; Ye Seul Sim; Sungik Choi; Kyungeun Lee; Hye-Seung Cho; Hankook Lee; Woohyung Lim
Venue: AAAI 2025 (main)

Abstract: Out-of-distribution (OOD) detection, determining whether a given sample is part of the in-distribution (ID) or not, has been newly explored by a generative model-based outlier synthesizing approach, especially with diffusion models. Nonetheless, existing diffusion models often produce outliers that are considerably distant from the ID in pixel-space, showing limited efficacy for capturing subtle distinctions between ID and OOD. To address these issues, we propose a novel framework, Semantic Outlier generation via Nuisance Awareness (SONA), which directly utilizes informative pixel-space ID images in diffusion models. Thereby, the generated outliers achieve two crucial properties: (i) they closely resemble the ID mainly in nuisances, while (ii) represent discriminative semantic information. To facilitate the separate effect on semantics and nuisances, we introduce SONA guidance, providing region-specific guidance. Extensive experiments demonstrate the effectiveness of our framework, achieving an impressive AUROC of 87% on near-OOD datasets, which surpasses the performance of baseline methods by a significant margin of approximately 6%.