GenAI4ELab/papercli-papers huggingface dataset:
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation
Authors: Hourun Li; Yifan Wang; Zhiping Xiao; Jia Yang; Changling Zhou; Ming Zhang; Wei Ju
Venue: AAAI 2025 (main)

Abstract: Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains.Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components.
Contrastive Representation for Interactive Recommendation
Authors: Jingyu Li; Zhiyong Feng; Dongxiao He; Hongqi Chen; Qinghang Gao; Guoli Wu
Venue: AAAI 2025 (main)

Abstract: Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perfect for IR. Due to the large action space and sample inefficiency problem, training DRL recommender agents is challenging. The key point is that useful features cannot be extracted as high-quality representations for the recommender agent to optimize its policy. To tackle this problem, we propose Contrastive Representation for Interactive Recommendation (CRIR). CRIR efficiently extracts latent, high-level preference ranking features from explicit interaction, and leverages the features to enhance users’ representation. Specifically, the CRIR provides representation through one representation network, and refines it through our proposed Preference Ranking Contrastive Learning (PRCL). The key insight of PRCL is that it can perform contrastive learning without relying on computations involving high-level representations or large potential action sets. Furthermore, we also propose a data exploiting mechanism and an agent training mechanism to better adapt CRIR to the DRL backbone. Extensive experiments have been carried out to show our method's superior improvement on the sample efficiency while training an DRL-based IR agent.
Disentangled Contrastive Bundle Recommendation with Conditional Diffusion
Authors: Jiuqiang Li
Venue: AAAI 2025 (main)

Abstract: Bundle recommendation aims to improve user experience by suggesting complementary items that users are likely to purchase together. Although recent advances in recommendation systems have shown promise, there are still significant challenges: i) The dynamic nature of user preferences and interactions introduces noise that can distort the effectiveness of recommendations. ii) Existing methods frequently exhibit limited robustness when addressing the sparsity of user interactions with bundles in real-world scenarios. To tackle these issues, we introduce a disentangled contrastive bundle recommendation (DCBR) framework with conditional diffusion. First, we propose a conditional bundle diffusion model for denoising the user-bundle interaction graph, introducing a bundle latent consistency constraint during the optimization process to mitigate the degradation of original interaction information. Subsequently, we design a triple-view denoised graph learning module to obtain effective representations from multiple views. Furthermore, we present a dual-level disentangled contrastive learning paradigm, which addresses the latent relationships at two levels: between views (inter-view) and within each view (intra-view). By maximizing the consistency between positive samples in these contrastive views, we generate disentangled contrastive signals, overcoming interaction sparsity and alleviating noise issues. Our experimental evaluations on three benchmark datasets reveal that DCBR significantly outperforms state-of-the-art methods.
Public Opinion Field Effect and Hawkes Process Join Hands for Information Popularity Prediction
Authors: Junliang Li; Yajun Yang; Yujia Zhang; Qinghua Hu; Alan Zhao; Hong Gao
Venue: AAAI 2025 (main)

Abstract: Information popularity prediction, aiming to predict the growth of user participation in a trending topic diffusion, is a fundamental task in social networks. Existing methods often treat information diffusion as a single independent process, ignoring the ``public opinion field effect'' where multiple trending topics coexist and compete for user attention simultaneously. Inspired by Hawkes theory, we propose a novel Hawkes-process-based learning model for information popularity prediction, which takes into account both the temporal correlation among users' propagation behaviors in several topics diffusion and public opinion field effect in social networks. We first propose an improved neural Hawkes process to capture comprehensive propagation law from multiple dimensions and then propose a novel public opinion field paradigm based on the improved Hawkes process and cascade structure. We design a novel learning framework incorporating the public opinion field paradigm to extract high-quality representations for information popularity prediction. Extensive experiments on four real-world datasets validate that our model significantly outperforms the state-of-the-art competitors.
Self-Explainable Graph Transformer for Link Sign Prediction
Authors: Lu Li; Jiale Liu; Xingyu Ji; Maojun Wang; Zeyu Zhang
Venue: AAAI 2025 (main)

Abstract: Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions in critical scenarios that require understanding the rationale behind predictions. To the best of our knowledge, there is currently no research work on the explainability of the SGNN models. Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks. Since post-hoc explanations are not derived directly from the models, they may be biased and misrepresent the true explanations. Therefore, in this paper we introduce a Self-Explainable Signed Graph transformer (SE-SGformer) framework, which can not only outputs explainable information while ensuring high prediction accuracy. Specifically, we propose a new Transformer architecture for signed graphs and theoretically demonstrate that using positional encoding based on signed random walks has greater expressive power than current SGNN methods and other positional encoding graph Transformer-based approaches. We construct a novel explainable decision process by discovering the K-nearest (farthest) positive (negative) neighbors of a node to replace the neural network-based decoder for predicting edge signs. These K positive (negative) neighbors represent crucial information about the formation of positive (negative) edges between nodes and thus can serve as important explanatory information in the decision-making process. We conducted experiments on several real-world datasets to validate the effectiveness of SE-SGformer, which outperforms the state-of-the-art methods by improving 2.2% prediction accuracy and 73.1% explainablity accuracy in the best-case scenario.
Adaptive Dataset Quantization
Authors: Muquan Li; Dongyang Zhang; Qiang Dong; Xiurui Xie; Ke Qin
Venue: AAAI 2025 (main)

Abstract: Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data storage burdens on limited hardware resources, numerous dataset compression methods such as dataset distillation (DD) and coreset selection have emerged to obtain a compact but informative dataset through synthesis or selection for efficient training. However, DD involves an expensive optimization procedure and exhibits limited generalization across unseen architectures, while coreset selection is limited by its low data keep ratio and reliance on heuristics, hindering its practicality and feasibility. To address these limitations, we introduce a newly versatile framework for dataset compression, namely Adaptive Dataset Quantization (ADQ). Specifically, we first identify the sub-optimal performance of naive Dataset Quantization (DQ), which relies on uniform sampling and overlooks the varying importance of each generated bin. Subsequently, we propose a novel adaptive sampling strategy through the evaluation of generated bins' representativeness score, diversity score and importance score, where the former two scores are quantified by the texture level and contrastive learning-based techniques, respectively. Extensive experiments demonstrate that our method not only exhibits superior generalization capability across different architectures, but also attains state-of-the-art results.
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Authors: Muzhi Li; Cehao Yang; Chengjin Xu; Zixing Song; Xuhui Jiang; Jian Guo; Ho-fung Leung; Irwin King
Venue: AAAI 2025 (main)

Abstract: Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on the existence and quality of reasoning paths, which limits their general applicability in different scenarios. In addition, we observe that latent type constraints and neighboring facts inherent in KGs are also vital in inferring missing triples. To effectively utilize all useful information in KGs, we introduce CATS, a novel context-aware inductive KGC solution. With sufficient guidance from proper prompts and supervised fine-tuning, CATS activates the strong semantic understanding and reasoning capabilities of large language models to assess the existence of query triples, which consist of two modules. First, the type-aware reasoning module evaluates whether the candidate entity matches the latent entity type as required by the query relation. Then, the subgraph reasoning module selects relevant reasoning paths and neighboring facts, and evaluates their correlation to the query triple. Experiment results on three widely used datasets demonstrate that CATS significantly outperforms state-of-the-art methods in 16 out of 18 transductive, inductive, and few-shot settings with an average absolute MRR improvement of 7.2%.
Context-aware Graph Neural Network for Graph-based Fraud Detection with Extremely Limited Labels
Authors: Pengbo Li; Hang Yu; Xiangfeng Luo
Venue: AAAI 2025 (main)

Abstract: Graph-based fraud detection is crucial in identifying illegal activities in social networks, finance, and other sectors. Despite recent progress in this area, most of current researches typically require a large amount of annotated data to demonstrate its benefits. In practice, obtaining sufficient high-quality annotated data is challenging, limiting the effectiveness of model training. Therefore, leveraging extremely limited label information is crucial to enhance model performance. We propose a context-aware graph neural network (CGNN) to address this. CGNN performs category semantic decomposition on the contextual neighbor features of the center node to enrich the category semantics. In the neighbor message aggregation stage, the denoising attention mechanism enables the center node to adaptively aggregate heterophilic and homophilic information from neighbors. Particularly for unlabeled data, feature augmentation within the category subspace and consistency regularization driven by entropy minimization ensure that such data can further enhance model performance under explicit semantic guidance. We demonstrate on four real-world datasets that CGNN significantly outperforms other baseline methods with extremely limited labels.
Structure Balance and Gradient Matching-Based Signed Graph Condensation
Authors: Rong Li; Long Xu; Songbai Liu; Junkai Ji; Lingjie Li; Qiuzhen Lin; Lijia Ma
Venue: AAAI 2025 (main)

Abstract: Training graph neural networks (GNNs) for graph representation has received increasing concerns due to its outstanding performance in the link prediction and node classification tasks, but it incurs much time and storage for tackling large-scale graphs. To alleviate this issue, graph condensation has been emerged to condense the large graph into a small but highly-informative graph, while achieving comparable performance of GNNs trained on the small graph and large graph. However, existing works mainly focus on the gradient or distribution matching under GNN training trajectories to condense simple link structures, while overlooking the structure matching for condensing signed graph that exists conflict links and structural balance among nodes. To bridge this gap, we propose a novel Structure Balance and Gradient Matching-Based Signed Graph Condensation (SGSGC) method for condensing signed graph with node attributes, conflict links and structural balance into informative smaller ones. Specifically, we first propose a structure-balanced matching to match the structural balance between the original and condensed signed graph, and then combine it with the gradient matching to condense signed graph for the link sign prediction task, while preserving both conflicting link structures and node attributes. Moreover, we use the feature smoothing and the graph sparsification technique to improve the robustness for the GNN training, respectively. Finally, a bi-level optimization technique is proposed to simultaneously find the optimal node attributes and conflict structure of the condensed graph. Experiments on six datasets demonstrate that SGSGC achieves excellent performance. On Epinions, 94% test accuracy of training on the original signed graph, while reducing their graph size by 99.95% - 99.99%, and there exist 2.24% – 6.26% accuracy improvements for link sign prediction compared to the state-of-the-arts.
SSL-STMFormer Self-Supervised Learning Spatio-Temporal Entanglement Transformer for Traffic Flow Prediction
Authors: Zetao Li; Zheng Hu; Peng Han; Yu Gu; Shimin Cai
Venue: AAAI 2025 (main)

Abstract: Traffic flow prediction remains a critical issue in intelligent transport systems. Despite significant efforts in traffic flow modeling, existing approaches exhibit several notable limitations: (i) Most models fail to capture traffic flow similarities over long distances and extended periods; (ii) They struggle to account for spatio-temporal heterogeneity induced by varying traffic flow patterns; (iii) Due to their static modeling approach, they struggle to effectively capture the intricate spatio-temporal entanglement. To address these challenges, we propose a traffic flow prediction framework based on self-supervised learning spatio-temporal entanglement transformer(SSL-STMFormer). This framework adopts a self-supervised learning paradigm, leveraging a transformer architecture that captures richer spatio-temporal information to better represent traffic flow patterns. Specifically, a temporal attention module and a spatial attention module are employed to capture the spatio-temporal dependencies of traffic dynamics, respectively, and spatio-temporal entanglement-aware methods are introduced to allow the model to perceive spatio-temporal entanglement and thus better modelling of real traffic environments. Furthermore, to achieve adaptive spatio-temporal self-supervised learning, adaptive data augmentation is applied to the input traffic flow data, and the traffic flow prediction task is enhanced with temporal heterogeneity module and spatial heterogeneity module. Extensive experimental evaluations conducted on six publicly available real-world transportation datasets demonstrate that our method achieves substantial improvements across these datasets.
Enhancing Healthcare Recommendations: A Privacy-Protective and Interpretable Cross-Domain Framework
Authors: Xun Liang; Zhiying Li; Hongxun Jiang
Venue: AAAI 2025 (main)

Abstract: Cross-domain recommendations in healthcare services differ from traditional ones in electronic commerce due to the need for heightened medical privacy protection for a small group of users, while ensuring the majority, who may lack sufficient medical knowledge, can understand the recommendations. To recommend doctors who provide online consultations to health video viewers and enable multimodal cross-domain recommendations from short video platforms (source domain) to online healthcare communities (target domain), this paper introduces a framework based on the User-Centric Synthetic Data Architect (UCSDA) and Pre-trained Large Language Model (PtLLM). UCSDA employs a user-centric, advanced selection-synthesis mechanism to filter users' cold interaction items and synthesize noise items, reducing privacy leakage risk. PtLLM focuses on necessary patient and doctor IDs during the recommendation decision process to generate explanations. The model's effectiveness and scalability were validated using three public datasets and a healthcare cross-domain recommendation dataset. In addition to traditional evaluation metrics, strong privacy metrics and the unique sentence ratio were used to assess privacy protection and interpretability. We also compared the characteristics of privacy protection and interpretability between e-commerce and healthcare recommendation scenarios.
Iterative Sparse Attention for Long-sequence Recommendation
Authors: Guanyu Lin; Jinwei Luo; Yinfeng Li; Chen Gao; Qun Luo; Depeng Jin
Venue: AAAI 2025 (main)

Abstract: Longer historical behaviors often improve recommendation accuracy but bring efficient problems. As sequences get longer, the following two main challenges have not been addressed: (1) efficient modeling under increasing sequence length and (2) interest drifting within historical items. In this paper, we propose Iterative Sparse Attention for Long-sequence Recommendation (ISA) with Sparse Attention Layer and Iterative Attention Layer to efficiently capture sequential pattern and expand the receptive field of each historical items. We take the pioneering step to address the efficient and interest drifting challenges for the long-sequence recommendation simultaneously. The theoretical analysis illustrates that our proposed iterative method can approximate full attention efficiently. Experiments on two real-world datasets show the superiority of our proposed method against state-of-the-art baselines.
Exploring the Relationship Between Samples and Masks for Robust Defect Localization
Authors: Jiang Lin; Hui Xue; Fanxiu Sun; Yaping Yan
Venue: AAAI 2025 (main)

Abstract: Defect detection aims to detect and localize regions out of the normal distribution. The previous approaches often explicitly incorporate the defect detection concept, such as by utilizing self-supervised ground truth or manually defined feature comparison. The aforementioned processes involve modeling the distribution of normal samples, and they rely on the modeled normality for accurate inference. This reliance may hinder their ability to generalize to unseen test scenarios or the test set that deviates from the training distribution. In this paper, we propose a one-stage framework that detects defective patterns directly without the modeling process. This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic correction mechanism that allows the network to self-correct. In training, explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Experimental results show that the proposed method performs superior in comparison with the previous SOTA methods in various test scenarios.
Unified Graph Neural Networks Pre-training for Multi-domain Graphs
Authors: Mingkai Lin; Xiaobin Hong; Wenzhong Li; Sanglu Lu
Venue: AAAI 2025 (main)

Abstract: Graph Neural Networks (GNNs) have proven effective and typically benefit from pre-training on accessible graphs to enhance performance on tasks with limited labeled data. However, existing GNNs are constrained by the ``one-domain-one-model'' limitation, which restricts their effectiveness across diverse graph domains. In this paper, we tackle this problem by developing a method called Multi-Domain Pre-training for a Unified GNN Model (MDP-GNN). This method is based on the philosophical notion that everything is interconnected, suggesting that a latent meta-domain exists to encompass the diverse graph domains and their interconnections. MDP-GNN seeks to identify and utilize this meta-domain to train a unified GNN model through three core strategies. Firstly, it integrates node feature semantics from different domains to create unified representations. Secondly, it employs a bi-level learning strategy to build a domain-synthesized network that identifies latent connections to facilitate cross-domain knowledge transfer. Thirdly, it uses Wasserstein distance to map diverse domains into the common meta-domain for graph distribution alignment. We validate the effectiveness of MDP-GNN through theoretical analysis and extensive experiments on four real-world graph datasets, showing its superiority in enhancing GNN performance across diverse domains.