OpenRaiser/Intern-Atlas huggingface dataset:
DO: An Efficient Deep Reinforcement Learning Approach for Optimal Route with Collective Spatial Keywords
Authors: Jiajia Li, Jiming Dong, Lei Li, Yu Yang, Xin Wang, Mengxuan Zhang
Venue: CIKM
Year: 2025

Given a source, destination, and required keywords, the Optimal Route with Collective Spatial Keywords ( ORCSK ) query aims to find the shortest route covering all keywords. Existing Point of Interest (POI) candidate set-based and path expansion-based methods frequently produce inferior route quality or excessive time overhead, particularly under large-scale query keywords. To address this challenge, we introduce the DO framework, which pioneers the employ Deep Reinforcement Learning for the ORCSK. Specifically, DO first integrates the spatial index with the H2H index to generate and refine high-quality candidate sets. Subsequently, DO utilizes a Transformer-based model to determine the optimal route from the sets. To effectively combine spatial distance and POI attributes, we propose a novel dual-cross encoder architecture. Furthermore, leveraging this architecture, we introduce a multi-route generating strategy, exploiting parallel computing to enhance route quality. Our experiments on real-life road networks demonstrate superior route quality and response time compared to the state-of-the-art method, with an average improvement of 1-2 orders of magnitude in response time, and maintain high efficiency even under large-scale query keywords or dynamic POI attributes scenarios.
Sparse Autoencoders in Collaborative Filtering Enhanced LLM-based Recommender Systems
Authors: Xinyu He, Jose Sepulveda, Fei Wang, Hanghang Tong
Venue: CIKM
Year: 2025

Large language models (LLM) have demonstrated remarkable capability in recommendation tasks. Recently, efforts have been made to further enhance LLM performance with collaborative knowledge learned from traditional recommender systems. One approach is to inject learned embeddings into LLM prompts through a trainable projector, yet these embeddings could carry noisy or irrelevant information. In this paper, we propose using sparse autoencoders to improve input prompts. We show that sparse autoencoders can learn highly interpretable embeddings and extract key collaborative features in the case of recommender systems. With the help of sparse autoencoders, we are able to extract collaborative features to augment input prompts. By capturing TopK features of each item, we mitigate noisy information from item embeddings, therefore sparse autoencoders can also help with denoising embeddings in prompts. We develop two methods that utilize sparse autoencoders to augment or denoise input prompts. We evaluate the proposed methods on three real-world datasets and both show promising performance improvements.
Sequential Difference Maximization: Generating Adversarial Examples via Multi-Stage Optimization
Authors: Xinlei Liu, Tao Hu, Peng Yi, Weitao Han, Jichao Xie, Baolin Li
Venue: CIKM
Year: 2025

Efficient adversarial attack methods are critical for assessing the robustness of computer vision models. In this paper, we reconstruct the optimization objective for generating adversarial examples as ''maximizing the difference between the non-true labels' probability upper bound and the true label's probability,'' and propose a gradient-based attack method termed Sequential Difference Maximization (SDM). SDM establishes a three-layer optimization framework of ''cycle-stage-step.'' The processes between cycles and between iterative steps are respectively identical, while optimization stages differ in terms of loss functions: in the initial stage, the negative probability of the true label is used as the loss function to compress the solution space; in subsequent stages, we introduce the Directional Probability Difference Ratio (DPDR) loss function to gradually increase the non-true labels' probability upper bound by compressing the irrelevant labels' probabilities. Experiments demonstrate that compared with previous SOTA methods, SDM not only exhibits stronger attack performance but also achieves higher attack cost-effectiveness. Additionally, SDM can be combined with adversarial training methods to enhance their defensive effects. The code is available at https://github.com/X-L-Liu/SDM.
Publicly Verifiable and Fault-Tolerant Privacy-Preserving Aggregation for Federated Learning
Authors: Guohao Li, Qi Jiang, Lu Zhou, Li Yang
Venue: CIKM
Year: 2025

Publicly verifiable privacy-preserving aggregation is widely regarded as an effective approach to protect user privacy and ensure the integrity of the aggregated model published by the aggregator in Federated Learning (FL). State-of-the-art solutions either fail to guarantee unforgeability when the aggregator colludes with malicious users or require costly cryptographic operations during the online aggregation phase and lack fault tolerance. In this work, we propose eVTPA, the first online-efficient, publicly verifiable, and fault-tolerant privacy-preserving aggregation protocol considering malicious users and aggregators for FL. We introduce a novel collusion-resistant symmetric masking technique to conceal users' local gradients while ensuring the correctness of the aggregated model through a publicly verifiable aggregation signature algorithm. To improve the efficiency of online signature generation, we design a specialized precomputation-based acceleration method and leverage the randomness of masking to enable batch processing. Furthermore, eVTPA adopts a dynamic mask update mechanism that tolerates user dropouts without affecting the validation of the aggregated model. Security analysis shows that eVTPA meets FL's confidentiality, integrity, and authenticity requirements. Experimental results demonstrate that our scheme maintains model classification accuracy while achieving at least a 7.85× faster online aggregation than related solutions at the same security level.
AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots
Authors: Xinjie Zhao, Moritz Blum, Fan Gao, Yingjian Chen, Boming Yang, Luis Marquez-Carpintero, Monica Pina-Navarro, Yanran Fu, So Morikawa, Yusuke Iwasawa, Yutaka Matsuo, Chanjun Park, Irene Li
Venue: CIKM
Year: 2025

AGENTiGraph is a user-friendly, agent-driven system that enables intuitive interaction and management of domain-specific data through the manipulation of knowledge graphs in natural language. It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. The flexible design of AGENTiGraph, including intent classification, task planning, and automatic knowledge integration, ensures seamless reasoning between diverse tasks. Evaluated on a 3,500-query benchmark within an educational scenario, the system outperforms strong zero-shot baselines (achieving 95.12% classification accuracy, 90.45% execution success), indicating potential scalability to compliance-critical or multi-step queries in legal and medical domains, e.g., incorporating new statutes or research on the fly. Our open-source demo offers a powerful new paradigm for multi-turn enterprise knowledge management that bridges LLMs and structured graphs.
Higher-Order Information Matters: A Representation Learning Approach for Social Bot Detection
Authors: Min Gao, Qiang Duan, Boen Liu, Yu Xiao, Xin Wang, Yang Chen
Venue: CIKM
Year: 2025

Detecting social bots is crucial for mitigating the spread of misinformation and preserving online conversation authenticity. State-of-the-art solutions typically leverage graph neural networks (GNNs) to model user representations from social relationships and metadata. However, these approaches overlook two key factors: the similarity of a user and her neighbors, as well as the coordinated behaviors of social bots, resulting in a suboptimal detection performance. To address these issues, we propose HyperScan, a novel representation learning method for social bot detection. Specifically, we introduce three effective learners to capture pair-wise, hop-wise, and group-wise relations. HyperScan learns pair-wise user representations based on social relations and user features. It then enhances user representations by building hop-wise interactions across the learned pair-wise user representations for capturing the structure-level proximity information. Subsequently, it models user representations by constructing higher-order (group-wise) relations derived from user profiles, tweets, and social relations to capture the feature-level proximity knowledge. By leveraging hop-wise interactions and higher-order relations, HyperScan significantly improves bot detection performance. Our extensive experiments demonstrate that HyperScan outperforms state-of-the-art methods on three benchmark datasets. Additional studies validate the robustness and effectiveness of each component of HyperScan.
Target Item-oriented Conditional Diffusion Differential Transformer for Next-Item Prediction
Authors: Xiaoqing Chen, Zitao Xu, Weike Pan, Zhong Ming
Venue: CIKM
Year: 2025

Sequential recommendation (SR) aims to capture users' dynamic preferences based on their historical interactions and provide personalized next-item prediction. Multi-behavior SR (MBSR) further considers behavior types of user-item interactions, which can reveal diverse user interests and alleviate the data sparsity issue w.r.t. the target purchase behaviors. Most existing MBSR approaches ignore the importance of target items closely related to user interests. Moreover, they often suffer from the problem of limited vector representation capability. To tackle the above two challenges, we propose a novel solution called target item-oriented conditional diffusion differential Transformer (ICDDT). Specifically, our ICDDT introduces distribution representations via the diffusion model, allowing effective utilization of target item information during training to better capture user preferences. Firstly, our ICDDT achieves a more appropriate behavior-aware step selection in the diffusion phase by distinguishing the sampling distributions of diffusion steps w.r.t. behavior types. Secondly, our ICDDT introduces three conditions of interaction sequences, target behaviors and diffusion steps into the reverse phase to guide the training of the differential Transformer-based approximator, generating denoised target item representations as user personalized interests. Finally, our ICDDT sets an inference step truncation factor to fit the diffusion step sampling distributions and accelerate the inference process. We conduct extensive experiments on two real-world datasets, where the results show that our ICDDT significantly outperforms all baselines on all metrics. The datasets, source codes and scripts are available at https://github.com/Erin-Gr/ICDDT.
Geometric Heterogeneous Graph Neural Network for Protein-Ligand Binding Affinity Prediction
Authors: Feng Huang, Yuhang Xia, Ziyan Wang, Liuqing Yang, Wen Zhang
Venue: CIKM
Year: 2025

Accurately predicting protein-ligand binding affinity (PLA) remains a critical challenge in structure-based drug discovery. Recent advances have focused on using geometry-aware graph neural networks to model the three-dimensional (3D) structure of protein-ligand complexes for PLA prediction. However, they still achieve suboptimal performance due to two potential issues. 1) Representing the protein-ligand complex as a homogeneous graph ignores the inherent difference between intra- and intermolecular interactions, limiting the expressive ability of the models. 2) Given that the geometric complementarity between the ligand and protein binding pocket serves as a fundamental determinant of binding strength, the incomplete exploitation of geometric information constrains the predictive performance. In this study, we propose a novel Geometric Heterogeneous Graph Neural Network (GeoHGN) for PLA prediction. Specifically, we consider complete geometries to characterize the directions of edges in coordinate space through quantum inspired basis functions. To sufficiently incorporate the 3D information and heterogeneous topology of the complexes, we elaborately design a novel heterogeneous directional message passing mechanism (HDMP), which enables the propagation and aggregation of messages from intra- and intermolecular neighbors along with the directional information of linked edges. Extensive benchmarking experiments demonstrate the superiority of GeoHGN in predicting PLA.
DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection
Authors: Jinkyu Kim, Hyungjung Yi, Mogan Gim, Donghee Choi, Jaewoo Kang
Venue: CIKM
Year: 2025

We propose DeepAries, a novel deep reinforcement learning framework for dynamic portfolio management that jointly optimizes the timing and allocation of rebalancing decisions. Unlike prior reinforcement learning methods that employ fixed rebalancing intervals regardless of market conditions, DeepAries adaptively selects optimal rebalancing intervals along with portfolio weights to reduce unnecessary transaction costs and maximize risk-adjusted returns. Our framework integrates a Transformer-based state encoder, which effectively captures complex long-term market dependencies, with Proximal Policy Optimization (PPO) to generate simultaneous discrete (rebalancing intervals) and continuous (asset allocations) actions. Extensive experiments on multiple real-world financial markets demonstrate that DeepAries significantly outperforms traditional fixed-frequency and full-rebalancing strategies in terms of risk-adjusted returns, transaction costs, and drawdowns. Additionally, we provide a live demo of DeepAries at https://deep-aries.github.io/, along with the source code and dataset at https://github.com/dmis-lab/DeepAries, illustrating DeepAries' capability to produce interpretable rebalancing and allocation decisions aligned with shifting market regimes. Overall, DeepAries introduces an innovative paradigm for adaptive and practical portfolio management by integrating both timing and allocation into a unified decision-making process.
Autonomous Reasoning-Retrieval for Large Language Model Based Recommendation
Authors: Bowen Zheng, Xiaolei Wang, Enze Liu, Xi Wang, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen
Venue: CIKM
Year: 2025

Recently, large language models (LLMs) have been introduced into recommender systems (RSs) as recommendation backbones or to enhance traditional recommendation models (TRMs). However, existing LLM-based RSs fail to fully leverage the complementary strengths of LLMs (e.g., world knowledge and reasoning capabilities) and TRMs (e.g., recommendation-specific knowledge and computational efficiency), resulting in shallow exploration of the item space. To address this limitation, we propose DeepRec, a novel LLM-based RS approach that facilitates autonomous multi-turn interactions between LLMs and TRMs for deep item space exploration. In each interaction turn, LLMs reason over user preferences and collaborate with TRMs to retrieve candidate items. After multi-turn interaction, LLMs rank the aggregated candidates to generate the final recommendations. We utilize reinforcement learning (RL) for optimization and introduce novel contributions in three key aspects: recommendation model based data rollout, recommendation-oriented hierarchical rewards, and a two-stage RL training strategy. For data rollout, we design a preference-aware TRM, with which LLMs interact to construct trajectory data. For reward design, we propose a hierarchical reward function that comprises both process-level and outcome-level rewards to optimize the interaction process and recommendation quality, respectively. For RL training, our two-stage RL strategy first guides LLMs to learn effective interactions with TRMs, followed by recommendation-oriented RL for performance enhancement. Experiments on public datasets show that DeepRec substantially outperforms both traditional and existing LLM-based baselines, establishing a new paradigm for deep exploration in recommender systems.
Revisiting the Inner Product Method: Optimizing Sparse Matrix Multiplication via Set Intersection
Authors: Zheng Hu, Boyu Yang, Weiguo Zheng
Venue: CIKM
Year: 2025

Sparse matrices are extensively used to model interactions between entities and facilitate computations in neural networks. Sparse Matrix Multiplication (SpGEMM) serves as a fundamental operation in graph algorithms, social network analysis, and deep learning, attracting considerable research interest. Among the four primary paradigms for defining sparse matrix multiplication, the Inner Product (IP) method most closely aligns with the standard definition of matrix multiplication. However, due to its limited data reuse and reliance on index matching, the IP method has been rarely explored in the literature. This paper investigates the strong connection between SpGEMM and set intersection computation, introducing a hybrid sparse matrix multiplication algorithm that builds upon the numerical computation of the IP method. By leveraging the IP method's advantages-such as minimal intermediate results and high flexibility-our approach effectively enhances computational efficiency. Experimental evaluations on benchmark datasets demonstrate the superiority of the proposed algorithm, particularly in scenarios where the resulting matrix exhibits high sparsity. Furthermore, our method proves effective in several applications, including self-transpose multiplication and sparse matrix multiplications in graph neural networks.
Aggregated Gradients-based Adaptive Learning Rate Design in Federated Learning
Authors: Wenhao Yuan, Xuehe Wang
Venue: CIKM
Year: 2025

Federated Learning (FL) has emerged as a crucial distributed training paradigm, enabling discrete devices to collaboratively train a shared model while leveraging their locally stored private data. However, the non-independent-and-identically-distributed (Non-IID) data on heterogeneous clients may significantly impede training efficacy. In our study, we present a novel algorithm designed to alleviate client drifting on Non-IID data and enhance model performance, termed FedAgile (Aggregated Gradients-based AdaptIve LEarning Rate Design in FEDerated Learning), which designs the adaptive learning rate by introducing an aggregated gradient term to accelerate model convergence and mean-field terms to approximate the average local information over time. We refine the learning rate based on Jensen-Shannon (JS) Distance to enhance the generalization capability. Through rigorous theoretical analysis, we establish the existence and convergence analysis of the mean-field terms, which can be efficiently calculated via our proposed iterative algorithm with linear computational complexity. Further, we provide a robust upper bound on the convergence of FedAgile and prove that our algorithm achieves the linear convergence rate of Õ(T-1 ). The extensive experimental results on real-world datasets substantiate the superiority of our proposed FedAgile in comparison with existing state-of-the-art FL strategies, which can be easily incorporated with existing methods to further enhance model performance.
HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks
Authors: Qiqi Wang, Guanjin Wang, Yihong Pan, Zhipeng Lin, Huijia Li, Qian Liu, Kaiqi Zhao
Venue: CIKM
Year: 2025

Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.
Rethinking Lipschitzness Data-free Backdoor Defense
Authors: Xinyi Wang, Zhiyu Zhu, Zhibo Jin, Huaming Chen, Teng Joon Lim
Venue: CIKM
Year: 2025

Deep Neural Networks (DNNs) have demonstrated remarkable success across various applications, yet some studies reveal their vulnerability to backdoor attacks, where attackers manipulate models under specific conditions using triggers. It significantly compromise the model integrity. Addressing this critical security issue requires robust defence mechanisms to ensure the reliability of DNN models. However, most existing defence mechanisms heavily rely on specialized defence datasets, which are often difficult to obtain due to data privacy and security concerns. This highlights the urgent need for effective data-free defence strategies. In this work, we propose Lipschitzness Precise Pruning (LPP), a novel data-free backdoor defence algorithm that leverages the properties of Lipschitz function to detect and mitigate backdoor vulnerabilities by pruning neurons with strong backdoor correlations while fine-tuning unaffected neurons. Our approach optimizes the computation of the Lipschitz constant using dot product properties, allowing for efficient and precise identification of compromised neurons without the need of clean defence data. This method addresses the limitations of existing data-free defences and extends the scope of backdoor mitigation to include fully connected layers, ensuring comprehensive protection of DNN models. As our approach does not require data exchange, it can be implemented efficiently and effectively in diverse environments. Extensive experiments demonstrate that LPP outperforms state-of-the-art defence approaches without the need for additional defence datasets. We release our code at: https://github.com/LMBTough/LPP
Understanding the Embedding Models on Hyper-relational Knowledge Graph
Authors: Yubo Wang, Shimin Di, Zhili Wang, Haoyang Li, Fei Teng, Hao Xin, Lei Chen
Venue: CIKM
Year: 2025

Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qualifiers. As a result, researchers have attempted to adapt classical Knowledge Graph Embedding (KGE) models for HKGs by designing extra qualifier processing modules. However, it remains unclear whether the superior performance of Hyper-relational KGE (HKGE) models arises from their base KGE model or the specially designed extension module. In this paper, we data-wise convert HKGs to KG format using decomposition methods and then evaluate several classical KGE models' performance on HKGs. Our results show that some KGE models achieve comparable performance to HKGE models. Upon further analysis, we find that the decomposition methods alter the original HKG topology and fail to fully preserve HKG information. Moreover, we observe that current HKGE models are either insufficient in capturing the graph's long-range dependency or struggle to integrate main-triple and qualifier information due to the information compression issue. To further justify our findings and provide a direction for HKGE research, we propose FormerGNN, which employs a qualifier integrator to preserve the original HKG topology, a GNN-based graph encoder to capture the graph's long-range dependencies, and an improved approach for integrating main-triple and qualifier information to mitigate compression issues. Our experimental results demonstrate that FormerGNN outperforms existing HKGE models.