GenAI4ELab/papercli-papers huggingface dataset:
A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Authors: Di Jin; Jun Yang; Xiaobao Wang; Junwei Zhang; Shuqi Li; Dongxiao He
Venue: IJCAI 2025 (main)

Abstract: As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Authors: Di Jin; Jingyi Cao; Xiaobao Wang; Bingdao Feng; Dongxiao He; Longbiao Wang; Jianwu Dang
Venue: IJCAI 2025 (main)

Abstract: Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.
Prototype-based Optimal Transport for Out-of-Distribution Detection
Authors: Ao Ke; Wenlong Chen; Chuanwen Feng; Yukun Cao; Xike Xie; S. Kevin Zhou; Lei Feng
Venue: IJCAI 2025 (main)

Abstract: Detecting Out-of-Distribution (OOD) inputs is crucial for improving the reliability of deep neural networks in the real-world deployment. In this paper, inspired by the inherent distribution shift between in-distribution (ID) and OOD data, we propose a novel method that leverages optimal transport to measure the distribution discrepancy between test inputs and ID prototypes. The resulting transport costs are used to quantify the individual contribution of each test input to the overall discrepancy, serving as a desirable measure for OOD detection. To address the issue that solely relying on the transport costs to ID prototypes is inadequate for identifying OOD inputs closer to ID data, we generate virtual outliers to approximate the OOD region via linear extrapolation. By combining the transport costs to ID prototypes with the costs to virtual outliers, the detection of OOD data near ID data is emphasized, thereby enhancing the distinction between ID and OOD inputs. Extensive evaluations demonstrate the superiority of our method over state-of-the-art methods.
Balancing User-Item Structure and Interaction with Large Language Models and Optimal Transport for Multimedia Recommendation
Authors: Haodong Li; Lianyong Qi; Weiming Liu; Xiaolong Xu; Wanchun Dou; Yang Cao; Xuyun Zhang; Amin Beheshti; Xiaokang Zhou
Venue: IJCAI 2025 (main)

Abstract: The rapid growth of multimedia content has driven the development of recommender systems. Most previous work focuses on uncovering latent relationships among items to learn better representations. However, this approach does not sufficiently account for user affinities, potentially leading to an imbalance in the structure modeling of users and items. Moreover, the sparsity and imbalance of user-item interactions further hinder effective representation learning. To address these challenges, we propose a framework called BLAST, which balances structures and interactions via large language models and optimal transport for multimodal recommendation. Specifically, we utilize large language models to summarize side information and generate user profiles. Based on these profiles, we design an intra- and inter-entity structure balancing module to capture item-item and user-user relationships, integrating these affinities into the final representations. Furthermore, we impose constraints on negative sample selection, augment the training data with false negative items and the optimal transport algorithm, thereby leading to smoother interactions. We evaluate BLAST on three real-world datasets, and the results demonstrate that our method significantly outperforms state-of-the-art baselines, which validates the superiority and effectiveness of BLAST.
Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks
Authors: Jiafan Li; Jiaqi Zhu; Liang Chang; Yilin Li; Miaomiao Li; Yang Wang; Yi Yang; Hongan Wang
Venue: IJCAI 2025 (main)

Abstract: Nowadays, numerous online platforms can be described as multi-modal heterogeneous networks (MMHNs), such as Douban's movie networks and Amazon's product review networks. Accurately categorizing nodes within these networks is crucial for analyzing the corresponding entities, which requires effective representation learning on nodes. However, existing multi-modal fusion methods often adopt either early fusion strategies which may lose the unique characteristics of individual modalities, or late fusion approaches overlooking the cross-modal guidance in GNN-based information propagation. In this paper, we propose a novel model for node classification in MMHNs, named Heterogeneous Graph Neural Network with Inter-Modal Attention (HGNN-IMA). It learns node representations by capturing the mutual influence of multiple modalities during the information propagation process, within the framework of heterogeneous graph transformer. Specifically, a nested inter-modal attention mechanism is integrated into the inter-node attention to achieve adaptive multi-modal fusion, and modality alignment is also taken into account to encourage the propagation among nodes with consistent similarities across all modalities. Moreover, an attention loss is augmented to mitigate the impact of missing modalities. Extensive experiments validate the superiority of the model in the node classification task, providing an innovative view to handle multi-modal data, especially when accompanied with network structures. The full version including Appendix is available at http://arxiv.org/abs/2505.07895.
App2Exa: Accelerating Exact kNN Search via Dynamic Cache-Guided Approximation
Authors: Ke Li; Leong Hou U; Shuo Shang
Venue: IJCAI 2025 (main)

Abstract: The k-nearest neighbor (kNN) query is a cornerstone of similarity-based applications across various domains. While prior work has enhanced kNN search efficiency, it typically focuses on approximate methods for high-dimensional data or exact methods for low-dimensional data, often assuming static query and data distributions. This creates a significant gap in accelerating exact kNN search for low-to-medium dimensional data with dynamic query distributions. To fill this gap, we propose App2Exa, a cache-guided framework that integrates approximate and exact kNN search. App2Exa utilizes a dynamically maintained cache graph index to retrieve approximate results, which subsequently guide exact search using a VP-Tree with a best-first strategy. A benefit-driven caching mechanism further optimizes performance by prioritizing vectors based on frequency, recency, and computational cost. Experimental results demonstrate that App2Exa significantly boosts efficiency, providing a robust and scalable solution for evolving query patterns and enabling exact kNN search to support higher dimensionality more effectively.
TOTF: Missing-Aware Encoders for Clustering on Multi-View Incomplete Attributed Graphs
Authors: Mengyao Li; Xu Zhou; Jiapeng Zhang; Zhibang Yang; Cen Chen; Kenli Li
Venue: IJCAI 2025 (main)

Abstract: As the network data in real life become multi-modal and multi-relational, multi-view attributed graphs have garnered significant attention. Numerous methods have achieved excellent performance in multi-view attributed graph clustering; however, they cannot efficiently handle incomplete attribute scenarios, which are prevalent in many real-life applications. Inspired by this, we investigate the problem of multi-view incomplete attributed graph clustering for the first time. In particular, the TOTF (Train Once Then Freeze) framework is designed to train missing-aware encoders that capture view-specific information while ignoring the impact of incomplete attributes, and then employs frozen encoders to uncover common information driven by clustering. After that, we propose a correlation strength-aware graph neural network on the basis of the inherent relationships among attributes to enhance accuracy. It is proven theoretically that traditional Generative Adversarial Networks (GANs) are unable to generate the unique real distribution. To address this issue, we further introduce the missing-position reminder mechanism into our intra-view adversarial games for better clustering results. Extensive experimental results demonstrate that our method achieves up to a 17% improvement in accuracy over the state-of-the-art methods. The source code is available at https://anonymous.4open.science/r/TOTF-main.
Decision-Aware Preference Modeling for Multi-Behavior Recommendation
Authors: Qingfeng Li; Wei Liu; Zaiqiao Meng; Jian Yin
Venue: IJCAI 2025 (main)

Abstract: In recommender systems, multi-behavior methods have demonstrated significant effectiveness in addressing issues such as data sparsity—challenges commonly encountered by traditional single-behavior recommendation methods. These methods typically infer user preferences from various auxiliary behaviors and apply them to recommendations for the target behavior. However, existing methods face challenges in uncovering the interaction patterns for different behaviors from multi-behavior implicit feedback, as users exhibit varying preference strengths for different items across behaviors. To address this issue, this paper introduces a novel approach, Decision-Aware Preference Modeling (DAPM), for multi-behavior recommendation. We first construct a behavior-agnostic graph to learn comprehensive representations that are not affected by behavior factors, complementing the behavior-specific representations. Subsequently, we introduce an innovative contrastive learning paradigm that emphasizes inter-behavior consistency and intra-behavior uniformity to alleviate the “false repulsion” problem in traditional contrastive learning. Furthermore, we propose a multi-behavior hinge loss with boundary constraints to explicitly model users' decision boundaries across different behaviors, thereby enhancing the model’s ability to accurately capture users' inconsistent preference intensities. Extensive experiments on three real-world datasets demonstrate the consistent improvements achieved by DAPM over thirteen state-of-the-art baselines. We release our code at https://github.com/Breeze-del/DAPM.
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems
Authors: Runze Li; Di Jin; Xiaobao Wang; Dongxiao He; Bingdao Feng; Zhen Wang
Venue: IJCAI 2025 (main)

Abstract: Graph recommendation systems have been widely studied due to their ability to effectively capture the complex interactions between users and items. However, these systems also exhibit certain vulnerabilities when faced with attacks. The prevailing shilling attack methods typically manipulate recommendation results by injecting a large number of fake nodes and edges. However, such attack strategies face two primary challenges: low stealth and high destructiveness. To address these challenges, this paper proposes a novel graph backdoor attack method that aims to enhance the exposure of target items to the target user in a covert manner, without affecting other unrelated nodes. Specifically, we design a single-node trigger generator, which can effectively expose multiple target items to the target user by inserting only one fake user node. Additionally, we introduce constraint conditions between the target nodes and irrelevant nodes to mitigate the impact of fake nodes on the recommendation system's performance. Experimental results show that the exposure of the target items reaches no less than 50% in 99% of the target users, while the impact on the recommendation system's performance is controlled within approximately 5%.
Beyond Individual and Point: Next POI Recommendation via Region-aware Dynamic Hypergraph with Dual-level Modeling
Authors: Xixi Li; Zhuo Gu; Rui Yao; Yong Zhou; Hancheng Zhu; Jiaqi Zhao; Wen-liang Du
Venue: IJCAI 2025 (main)

Abstract: Next POI recommendation contributes to the prosperity of various intelligent location-based services. Existing studies focus on exploring sequential patterns and POI interactions using sequential and graph-based methods to enhance recommendation performance. However, they don't effectively exploit geographical information. In addition, methods that focus on modeling mobility patterns using individual limited data may suffer from data sparsity and the information cocoons problem. Moreover, most graph structures focus on adjacent nodes, failing to capture potential high-order associations among POIs. To address these challenges, we propose the Region-aware dynamic Hypergraph learning method with Dual-level interaction Modeling (ReHDM), which exploits users' dynamic mobility beyond individual and point. Specifically, ReHDM utilizes regional encoding to mine the potential spatial relationships among POIs with coarse-grained geographical information. By incorporating POI-level and trajectory-level associations within a hypergraph convolutional network, ReHDM comprehensively captures cross-user collaborative information. Furthermore, ReHDM captures not only dependencies among POIs within each trajectory for a single user, but also the high-order collaborative information across individual user trajectories and associated users' trajectories. Experimental results on three public datasets demonstrate the superiority of ReHDM to the state-of-the-art.
Preference Identification by Interaction Overlap for Bundle Recommendation
Authors: Fei-Yao Liang; Wu-Dong Xi; Xing-Xing Xing; Wei Wan; Chang-Dong Wang; Hui-Yu Zhou
Venue: IJCAI 2025 (main)

Abstract: In the digital age, recommendation systems are crucial for enhancing user experiences, with bundle recommendations playing a key role by integrating complementary products. However, existing methods fail to accurately identify user preferences for specific items within bundles, making it difficult to design bundles containing more items of interest to users. Additionally, these methods do not leverage similar preferences among users of the same category, resulting in unstable and incomplete preference expressions. To address these issues, we propose Preference Identification by Interaction Overlap for Bundle Recommendation (PIIO). The data augmentation module analyzes the overlap between bundle-item inclusions and user-item interactions to calculate the interaction probability of non-interacted bundles, selecting the bundle with the highest probability as a positive sample to enrich user-bundle interactions and uncover user preferences for items within bundles. The preference aggregation module utilizes the overlap in user-item interactions to select similar users, aggregates preferences using an autoencoder, and constructs comprehensive preference profiles. The optimization module predicts user-bundle matching scores based on a user interest boundary loss function. The proposed PIIO model is applied to two bundle recommendation datasets, and experiments demonstrate the effectiveness of the PIIO model, surpassing state-of-the-art models.
Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective
Authors: Chang Liu; Hai Huang; Xingquan Zuo
Venue: IJCAI 2025 (main)

Abstract: The robustness of Graph Neural Networks (GNNs) has become an increasingly important topic due to their expanding range of applications. Various attack methods have been proposed to explore the vulnerabilities of GNNs, ranging from Graph Modification Attacks (GMA) to the more practical and flexible Graph Injection Attacks (GIA). However, existing methods face two key challenges: (i) their reliance on surrogate models, which often leads to reduced attack effectiveness due to structural differences and prior biases, and (ii) existing GIA methods often sacrifice attack success rates in undefended settings to bypass certain defense models, thereby limiting their overall effectiveness. To overcome these limitations, we propose QUGIA, a Query-based and Unnoticeable Graph Injection Attack. QUGIA injects nodes by first selecting edges based on victim node connections and then generating node features using a Bayesian framework. This ensures that the injected nodes are similar to the original graph nodes, implicitly preserving homophily and making the attack more unnoticeable. Unlike previous methods, QUGIA does not rely on surrogate models, thereby avoiding performance degradation and achieving better generalization. Extensive experiments on six real-world datasets with diverse characteristics demonstrate that QUGIA achieves unnoticeable attacks and outperforms state-of-the-art attackers. Our code is available at: https://anonymous.4open.science/r/QUGIA-588E/.
Flow Matching Based Sequential Recommender Model
Authors: Feng Liu; Lixin Zou; Xiangyu	 Zhao; Min Tang; Liming Dong; Dan Luo; Xiangyang Luo; Chenliang Li
Venue: IJCAI 2025 (main)

Abstract: Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model that employs a straight flow trajectory and a modified loss tailored for the recommendation task. Additionally, from the diffusion-model perspective, we integrate a reconstruction loss to improve robustness against noise perturbations, thereby retaining user preferences during the forward process. In the reverse process, we employ a deterministic reverse sampler, specifically an ODE-based updating function, to eliminate unnecessary randomness, thereby ensuring that the generated recommendations closely align with user needs. Extensive evaluations on four benchmark datasets reveal that FMRec achieves an average improvement of 6.53% over state-of-the-art methods. The replication code is available at https://github.com/FengLiu-1/FMRec.
Heterogeneous Temporal Hypergraph Neural Network
Authors: Huan Liu; Pengfei Jiao; Mengzhou Gao; Chaochao Chen; Di Jin
Venue: IJCAI 2025 (main)

Abstract: Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex heterogeneous temporal graphs (HTGs) have been proposed and have achieved successful applications in various fields. However, most existing GRL methods mainly focus on preserving the low-order topology information while ignoring higher-order group interaction relationships, which are more consistent with real-world networks. In addition, most existing hypergraph methods can only model static homogeneous graphs, limiting their ability to model high-order interactions in HTGs. Therefore, to simultaneously enable the GRL model to capture high-order interaction relationships in HTGs, we first propose a formal definition of heterogeneous temporal hypergraphs and P-uniform heterogeneous hyperedge construction algorithm that does not rely on additional information. Then, a novel Heterogeneous Temporal HyperGraph Neural network (HTHGN), is proposed to fully capture higher-order interactions in HTGs. HTHGN contains a hierarchical attention mechanism module that simultaneously performs temporal message-passing between heterogeneous nodes and hyperedges to capture rich semantics in a wider receptive field brought by hyperedges. Furthermore, HTHGN performs contrastive learning by maximizing the consistency between low-order correlated heterogeneous node pairs on HTG to avoid the low-order structural ambiguity issue. Detailed experimental results on three real-world HTG datasets verify the effectiveness of the proposed HTHGN for modeling high-order interactions in HTGs and demonstrate significant performance improvements.
Sharpness-aware Zeroth-order Optimization for Graph Transformers
Authors: Yang Liu; Chuan Zhou; Yuhan Lin; Shuai Zhang; Yang Gao; Zhao Li; Shirui Pan
Venue: IJCAI 2025 (main)

Abstract: Graph Transformers (GTs) have emerged as powerful tools for handling graph-structured data through global attention mechanisms. While GTs can effectively capture long-range dependencies, they introduce difficulties in optimization due to their complex, non-differentiable operators, which cannot be directly handled by standard gradient-based optimizers (such as Adam or AdamW). To investigate the above issues, this work adopts the line of Zeroth-Order Optimization (ZOO) technique. However, direct integration of ZOO incurs considerable challenges due to the sharp loss landscape and steep gradients within the GT parameter space. Under the above observations, we propose a Sharpness-aware Zeroth-order Optimizer (SZO) that combines Sharpness-Aware Minimization (SAM) technique facilitating convergence within a flatter neighborhood, and leverages parallel computing for efficient gradient estimation. Theoretically, we provide a comprehensive analysis of the optimizer from both convergence and generalization perspectives. Empirically, we conduct extensive experiments on various classical GTs across a wide range of benchmark datasets, which underscore the superior performance of SZO over the state-of-the-art optimizers.