OpenRaiser/Intern-Atlas huggingface dataset:
Addressing the Distortion of Community Representations in Anomaly Detection on Attributed Networks
Authors: Enbo He, Yitong Hao, Yue Zhang, Guisheng Yin, Lina Yao
Venue: CIKM
Year: 2025

Anomaly detection on attributed networks, especially in the unsupervised scenario, has garnered significant attention. And the Contrastive Learning (CL)-based methods have emerged as one of the state-of-the-art approaches for this task. However, existing CL-based methods face a critical challenge: anomalous nodes infiltrate the sampled local communities, leading to the distortion of community representation which fundamentally limits the discriminative ability. Our theoretical analysis reveals that this distortion is caused by two main mechanisms: the cross contamination and the aggregation bias. And the key oversight is to treat all community members equally and ignore the relative reliability of nodes. To address these issue, we propose a CL-based ANomaly detectIon Method on Attributed networks targeted at mitigating community distortions to enhance anomaly discrimination (ANIMA for short), which incorporates a Truncation-Restriction community encoder (TRC-Encoder) with an elaborate heuristic prior instruction to detect and suppress anomalous contributions during community representation learning. Comprehensive experiments on 7 datasets demonstrate that ANIMA outperforms 10 SOTA methods by 2.25-8.8% AUC, validating the effectiveness of our approach in mitigating community distortions and enhancing anomaly discrimination.
EEG-FSL: An EEG-Based Few-Shot Learning Framework for Music Recommendation
Authors: Ming He, Wenbo Luo, Yongjie Zheng, Junkai Zhang, Xiaolei Gao
Venue: CIKM
Year: 2025

Brain-computer interface based on electroencephalogram (EEG) has demonstrated significant potential for capturing users' implicit preferences, offering an innovative technique for music recommendation. However, we face two key challenges: (1) ineffective distinction of complex neural patterns in EEG signals, and (2) the cold-start problem, due to limited user EEG samples. To address these issues, we present EEG-FSL, a novel framework that integrates model-agnostic meta-learning (MAML) with dual-path neural feature extraction for music recommendation. EEG-FSL applies an attention-enhanced EEG encoder to extract meaningful patterns from brain signals through complementary pathways: one pathway retains temporal and phase information, while the other focuses on extracting common frequency-domain features. Furthermore, we utilize contrastive learning to explore the intrinsic structure of the data, significantly improving the model's feature differentiation ability. Additionally, we propose a meta-learning method which allows EEG-FSL to quickly adapt to new users using only a small number of EEG samples, effectively solving cold-start problem. Extensive experiments are conducted on a real-world dataset demonstrate the effectiveness of the proposed method. Specially, in few-shot scenarios, compared to the best baseline, our approach improves mean squared error in score prediction by 8.4% and classification accuracy by 16.8%. Consequently, our work provides a practical solution for next-generation brain-computer interface applications, capable of delivering highly personalized content recommendations while minimizing user data collection requirements. Our code is available at https://anonymous.4open.science/r/EEG-FSL-code-72F3/.
Improving the Safety of Medication Recommendation via Graph Augmented Patient Similarity Network
Authors: Ming He, Yongjie Zheng, Changle Li, Man Zhou
Venue: CIKM
Year: 2025

Recommending optimal medication combinations for patients is a crucial application of artificial intelligence in healthcare. Recent works typically use patients' electronic health record combined with their current health conditions. However, these efforts have the following issues: 1) they often reference historical visits unrelated to the current situation, and 2) there is a latent risk of side effects from historical prescriptions. Such issues raise concerns about the safety of medication recommendation. To address this, we propose GPSRec, a novel Graph augmented Patient Similarity network for medication Recommendation. By leveraging dual similarity measures to selectively integrate historical visits, GPSRec effectively filters out irrelevant information, improving the accuracy of recommendation. We further present a training strategy, which combines a pre-training method and a dual threshold loss adjustment, reduces the risk of adverse drug-drug interactions, enhancing the safety of recommendation. Extensive experiment results on two real datasets demonstrate that GPSRec significantly outperforms state-of-the-art methods. Notably, it achieves 30.11% and 24.92% improvements in safety, respectively, with higher accuracy.
Assessing Natural Language Explanations of Relational Graph Neural Networks
Authors: Stefan Heindorf, Daniel Neib
Venue: CIKM
Year: 2025

Although relational graph neural networks (RGNNs) excel at learning from graph-structured data as it appears in knowledge graphs, they often lack interpretability. While natural language (NL) explanations offer a promising solution, evaluating these explanations remains largely unaddressed. There is no unified evaluation framework including evaluation metrics, benchmarking datasets, and established evaluation procedures. This paper introduces NLEF, a novel NL Evaluation Framework to assess the quality of NL explanations for RGNNs. It uses the NL explanations to make new predictions and assesses in how far they align with the predictions produced by the RGNN. Towards this end, we propose two methods: (1) we convert NL explanations to description logics (DL) and use a DL reasoner for node classifications, (2) we use a retrieval augmented approach (RAG) for node classifications. Our evaluation results show that our DL method is highly scalable, whereas the RAG approach often yields the highest performance.
FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection
Authors: Minji Heo, Simon S. Woo
Venue: CIKM
Year: 2025

Multi-step or hybrid deepfakes, created by sequentially applying different deepfake creation methods such as Face-Swapping, GAN-based generation, and Diffusion methods, can pose an emerging and unforseen technical challenge for detection models trained on single-step forgeries. While prior studies have mainly focused on detecting isolated single manipulation, little is known about the detection model behavior under such compositional, hybrid, and complex manipulation pipelines. In this work, we introduce FakeChain, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. Using this approach, we analyze detection performance and spectral properties across hybrid manipulation at different step, along with varying generator combinations and quality settings. Surprisingly, our findings reveal that detection performance highly depends on the final manipulation type, with F1-score dropping by up to 58.83% when it differs from training distribution. This clearly demonstrates that detectors rely on last-stage artifacts rather than cumulative manipulation traces, limiting generalization. Such findings highlight the need for detection models to explicitly consider manipulation history and sequences. Our results highlight the importance of benchmarks such as FakeChain, reflecting growing synthesis complexity and diversity in real-world scenarios. Our sample code is available here. https://github.com/minjihh/FakeChain.
Structuring Data Science Automation: A Competency-Aware Taxonomy Approach
Authors: Maike Madeline Holtkemper, Max Pernklau, Christian Beecks
Venue: CIKM
Year: 2025

The growing number of data science (DS) automation frameworks complicates the selection of suitable tools for project-specific tasks. Current overviews emphasize functional capabilities or pipeline coverage, yet often overlook how tools align with DS workflows and user competencies. This paper introduces a first approach toward a competency-focused taxonomy of DS automation tools. Based on a structured literature review, we identify which CRISP-DM tasks are automated, how automation is achieved, and which DS competencies are required for effective use. We classify tools across four dimensions: CRISP-DM stage, task function, degree of automation, and required competencies. This taxonomy enables practitioners to match tools with workflow needs and team skill sets, while also clarifying human-in-the-loop dependencies. We validate the taxonomy through expert interviews and case mappings, demonstrating its practical value in identifying competency gaps and guiding framework adoption.
Quantum Deepflow: A Quantum-Integrated Forecasting Platform for Strategic Decisions in Raw Material Procurement
Authors: Charmgil Hong, Doohee Chung, Jongyeong Kim, Heewon Jung
Venue: CIKM
Year: 2025

We present Quantum Deepflow, a forecasting and decision support platform that integrates classical and quantum sequence modeling to address volatility and data irregularity in raw material procurement. The system combines an LSTM autoencoder with a Quantum Long Short-Term Memory (QLSTM) model, which enables robust and accurate forecasts from noisy time-series inputs. Users can interact with the platform through a visual interface that links forecast outputs to strategic key performance indicators such as purchase timing, cost estimates, and inventory risk. In a real-world deployment at a Korean steel manufacturer, the system achieved a 32.5% reduction in overstocking and saved $1.8 million in inventory costs. This work demonstrates a practical approach to exposing quantum-enhanced forecasting capabilities through an automated, cloud-based interface that bridges the gap between emerging quantum technology and enterprise-scale decision-making.
Adaptive Spike Neural Networks for Natural Language Inference Tasks with Dynamic Spike Predictor
Authors: Seung-Kyu Hong, Hyuk-Yoon Kwon
Venue: CIKM
Year: 2025

Spike Neural Networks offer energy efficiency and are promising candidates for ultra-low-power inference on neuromorphic hardware. While extensively studied in computer vision, their application in Natural Language Processing remains limited and underexplored. Three significant challenges of the existing work are as follows: (1) spike firing functions are sensitive to initial conditions, (2) spike timings are stochastic even for identical token inputs, preventing the stable preservation of contextual information, and (3) the analysis of spike occurrences on learning effectiveness is limited. To improve learning efficiency and stability, we propose Dynamic Spike Predictor (DSP) that adaptively regulates spike generation. DSP predicts a scale-adjusted input current at each time step to regulate spike activity, maintaining stable gradient flow, with only about 0.2% additional parameters to the backbone SNNs. We validate its effectiveness through comprehensive experiments on three NLI benchmarks (CB, RTE, and SICK), addressing research questions on the learning performance, robustness, and extensibility of DSP. The code is available at https://github.com/bigbases/Spike-Predictor.
Context-Aware Fine-Grained Graph RAG for Query-Focused Summarization
Authors: Yubin Hong, Chaofan Li, Jingyi Zhang, Yingxia Shao
Venue: CIKM
Year: 2025

Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge. In the Query-Focused Summarization (QFS) task, GraphRAG-based approaches have notably enhanced the comprehensiveness and diversity of generated responses. However, existing GraphRAG-based approaches lack sufficient fine-grained contextual information during graph retrieval, resulting in LLMs being unable to accurately understand the detailed and specific background knowledge of a query. To address it, we propose Context-Aware Fine-Grained Graph RAG (FG-RAG). On the one hand, FG-RAG employs Context-Aware Entity Expansion in graph retrieval to provide more contextual information for the retrieved content. On the other hand, FG-RAG utilizes Query-Level Fine-Grained Summarization to incorporate fine-grained details during response generation, enhancing query awareness for the generated summarization. Our evaluation demonstrates that FG-RAG outperforms other RAG systems in multiple metrics of comprehensiveness, diversity, and empowerment when handling the QFS task. Our implementation is available at https://github.com/BuptWululu/FG-RAG.
Towards Large Generative Recommendation: A Tokenization Perspective
Authors: Yupeng Hou, An Zhang, Leheng Sheng, Jiancan Wu, Xiang Wang, Tat-Seng Chua, Julian J. McAuley
Venue: CIKM
Year: 2025

The emergence of large generative models is transforming the landscape of recommender systems. One of the most fundamental components in building these models is action tokenization, the process of converting human-readable data (e.g., user-item interactions) into machine-readable formats (e.g., discrete token sequences). In this tutorial, we present a comprehensive overview of existing action tokenization techniques, converting actions to (1) item IDs, (2) textual descriptions, and (3) semantic IDs. We then make an in-depth discussion on the challenges and open questions of building large generative recommendation models from the perspective of action tokenization. Materials of this tutorial are available at: https://large-genrec.github.io/.
LeadFairRec: LLM-enhanced Discriminative Counterfactual Debiasing for Two-sided Fairness in Recommendation
Authors: Yimin Hou, Yue Kou, Derong Shen, Xiangmin Zhou, Dong Li, Tiezheng Nie, Ge Yu
Venue: CIKM
Year: 2025

Fairness-aware recommendation has emerged as a pivotal research area in recent years. Current fairness studies primarily examine two independent dimensions: user-side fairness and item-side fairness. However, most approaches address each side's fairness in isolation while neglecting their complex interdependencies. In this paper, we propose an LLM-Enhanced DiscriminAtive Counterfactual Debiasing Model for Two-sided Fairness in Recommendation (LeadFairRec). Specifically, we first design a two-sided causal graph that jointly models provider-customer fairness interactions through their causal relationships. Then we propose a discriminative counterfactual debiasing method, which effectively removes spurious correlations while maintaining true user-item interactions. Finally, we propose an LLM-enhanced counterfactual inference method to derive noise-resistant user/item representations from interaction data, enhancing the robustness of causal debiasing. The experimental results demonstrate the high effectiveness of our proposed model. We provide our code at https://github.com/houyimin660/LeadFairRec.
Heterogeneous Influence Maximization in User Recommendation
Authors: Hongru Hou, Jiachen Sun, Wenqing Lin, Wendong Bi, Xiangrong Wang, Deqing Yang
Venue: CIKM
Year: 2025

User recommendation systems enhance user engagement by encouraging users to act as inviters to interact with other users (invitees), potentially fostering information propagation. Conventional recommendation methods typically focus on modeling interaction willingness. Influence-Maximization (IM) methods focus on identifying a set of users to maximize the information propagation. However, existing methods face two significant challenges. First, recommendation methods fail to unleash the candidates' spread capability. Second, IM methods fail to account for the willingness to interact. To solve these issues, we propose two models named HeteroIR and HeteroIM. HeteroIR provides an intuitive solution to unleash the dissemination potential of user recommendation systems. HeteroIM fills the gap between the IM method and the recommendation task, improving interaction willingness and maximizing spread coverage. The HeteroIR introduces a two-stage framework to estimate the spread profits. The HeteroIM incrementally selects the most influential invitee to recommend and rerank based on the number of reverse reachable (RR) sets containing inviters and invitees. RR set denotes a set of nodes that can reach a target via propagation. Extensive experiments show that HeteroIR and HeteroIM significantly outperform the state-of-the-art baselines with the p-value<0.05. Furthermore, we have deployed HeteroIR and HeteroIM in Tencent's online gaming platforms and gained an 8.5% and 10% improvement in the online A/B test, respectively. Implementation codes are available at https://github.com/socialalgo/HIM.
Model-Agonistic Iterative Graph Diversification for Improving Learning to Solve Graph Optimization Problems
Authors: Bay-Yuan Hsu, Chia-Hsun Lu, Chih-Ya Shen
Venue: CIKM
Year: 2025

A recent line of research on learning to solve graph optimization problems has attracted much research attention. However, most recent machine learning approaches to graph optimization problems usually employ graph generators to randomly generate training graphs, which may lead to overfitting and deteriorate the model's generalization. To tackle this issue, we observe that enhancing the diversity of training graphs is a crucial factor in improving the model's performance. Therefore, in this paper, we formulate a new research problem, named Graph Augmentation for Diversity Maximization (GRAM), to maximize the training graph diversity by performing graph modifications. We first analyze the NP-hardness of GRAM. We then propose a 2-approximation algorithm and formally analyze its performance guarantee. Experimental results on well-known graph optimization problems show that our proposed approach significantly outperforms the baselines, such as graph augmentation and deep learning-based graph generation approaches.
Distributed Computation of k-Vertex Connected Components in Large Scale Networks
Authors: Xinchao Hu, Yuan Li, Feng Guo, Shan Huang, Guoli Yang, Yuhai Zhao
Venue: CIKM
Year: 2025

Recently, k-vertex connected component (k-VCC) detection has gained significant attention in graph analysis owing to its ability to capture structural cohesion. A k-VCC remains connected even after the removal of any k-1 vertices from itself. The k-VCC has broad applications across multiple domains, such as social network analysis, cybersecurity, and bioinformatics. Yet, the existing exact k-VCC detection algorithms require repeated computation of minimum vertex cuts, imposing a prohibitive computational cost for large scale graphs. In this paper, we present an approximate algorithm for k-VCC detection that leverages Monte Carlo sampling to accelerate minimum vertex cut computation with theoretical guarantee. Further, we design a distributed algorithm for mining all k-VCCs, named DkVCC. DkVCC adopts a divide-and-conquer strategy, decomposing the problem into smaller subgraph mining tasks that can be executed concurrently. Specifically, we generate tasks from individual vertices to construct initial subgraphs, and then iteratively expand and merge the subgraphs to form the final k-VCCs. Extensive experiments on 5 large real datasets demonstrates the efficiency of our proposed algorithms. For example, we achieve 4× runtime speedup on the LiveJournal dataset with 3.99M vertices and 34.7M edges in a 3-node cluster.
Mixture of Semantic and Spatial Experts for Explainable Traffic Prediction
Authors: Yang Hu, Shaobo Li, Dawen Xia, Zhiheng Zhou, Wenyong Zhang, Huaqing Li, Xingxing Zhang, Senzhang Wang
Venue: CIKM
Year: 2025

To satisfy the growing demand for traffic prediction induced by urbanization, the intelligent transportation system integrated various cutting-edge artificial intelligence technologies, with large language models (LLMs) as a representative, has been developed. However, existing methods are mostly confined by shallow LLMs utilization, where the semantic capacity of LLMs is ignored and the traffic data are directly fed in. Furthermore, the modality diversity of different traffic prediction scenarios (e.g., flow, speed, and demanding) remains to be underexplored, which restricts the model flexibility towards downstream applications. To mitigate these limitations, we propose a Mixture of Semantic and Spatial Experts (SS-MoE) for traffic prediction along with the human-intelligible post-hoc result explanation. Specifically, to enlighten the traffic predictor with abundant semantic information, we design hierarchically coarse- and fine-grained prompts including role assignments, dataset descriptions, and background supplements, which serves as the auxiliary knowledge for downstream prediction. Afterwards, considering the diversity of real-world traffic scenarios, we construct the MoE framework consisting of a spatial expert, a semantic expert, and a general expert, which accounts for the node-level features, the semantic representations, and the overall generalization, respectively. At last, we instruct the LLM to explain and analyze the final prediction, which is able to provide insightful conclusions and support intelligent transportation decisions, forming a unified prediction-explanation pipeline. Extensive experiments on five public traffic datasets demonstrate the superiority of SS-MoE across three traffic prediction tasks. Experimental results indicate that the MAE and RMSE values of SS-MoE are reduced by up to 4.04% and 3.20% compared with that of the runner-up, respectively.
GRIT: An Accurate and Efficient Graph Stream Summarization for Temporal Query
Authors: Jingxian Hu, Guozhang Sun, Xin Wang, Yuhai Zhao, Yuan Li, Xingwei Wang
Venue: CIKM
Year: 2025

Graph stream summarization refers to the technique used to process graph streams-unbounded sequences of edges-by constructing compressed representations that support approximate queries on both graph topology and temporal information in computing power networks. However, existing methods struggle to achieve accurate and efficient temporal queries due to two key limitations: (1) inefficient integration of temporal information, leading to high latency in both edge processing and query execution; and (2) redundant multilayer structures that accumulate errors, significantly reducing query accuracy. In this paper, we propose GRIT, an accurate and efficient Graph stReam summarIzation for Temporal query. GRIT introduces a new structure FlatIndex, which organizes temporal information in a flattened form, playing a critical role in minimizing error accumulation and ensuring accurate temporal queries. To further enhance edge processing efficiency, we introduce a lazy update strategy, which updates only a single element in the FlatIndex upon edge insertion, significantly reducing insertion latency. Moreover, our greedy-based decomposition (GBD) algorithm decomposes the target query range into the minimal number of intervals corresponding to the FlatIndex, enabling efficient execution of temporal queries over arbitrary time ranges. Extensive experiments on five real-world datasets demonstrate that GRIT improves query accuracy by 2-3 orders of magnitude, while reducing query latency by 1-2 orders of magnitude and increasing throughput by 7-13 times compared to state-of-the-art methods.