GenAI4ELab/papercli-papers huggingface dataset:
A Label-free Heterophily-guided Approach for Unsupervised Graph Fraud Detection
Authors: Junjun Pan; Yixin Liu; Xin Zheng; Yizhen Zheng; Alan Wee-Chung Liew; Fuyi Li; Shirui Pan
Venue: AAAI 2025 (main)

Abstract: Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudster and user greatly impacts detection performance, where the fraudsters tend to camouflage themselves by building more connections to benign users. Despite their promising performance, their label reliance limits its application in unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. Therefore, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a heterophily estimation module and an alignment-based fraud detection module. In the heterophily estimation module, we design a novel unsupervised heterophily metric called HALO, which captures the critical graph properties for GFD, enabling its outstanding ability to estimate heterophily with attributes. In the alignment-based fraud detection module, we develop a joint MLP-GNN architecture with ranking loss and asymmetric alignment loss. The ranking loss aligns the predicted fraud score with the relative order of HALO, providing an extra robustness guarantee by comparing heterophily between non-adjacent nodes. Moreover, the asymmetric alignment loss effectively utilizes structural information to alleviate the feature-smooth effects. Extensive experiments on six datasets demonstrate that HUGE consistently outperforms competitors, showcasing its effectiveness and robustness.
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
Authors: Mohit Prashant; Arvind Easwaran; Suman Das; Michael Yuhas
Venue: AAAI 2025 (main)

Abstract: An issue concerning the use of deep reinforcement learning (RL) agents is whether they can be trusted to perform reliably when deployed, as training environments may not reflect real-life environments. Anticipating instances outside their training scope, learning-enabled systems are often equipped with out-of-distribution (OOD) detectors that alert when a trained system encounters a state it does not recognize or in which it exhibits uncertainty. There exists limited work conducted on the problem of OOD detection within RL, with prior studies being unable to achieve a consensus on the definition of OOD execution within the context of RL. By framing our problem using a Markov Decision Process, we assume there is a transition distribution mapping each state-action pair to another state with some probability. Based on this, we consider the following definition of OOD execution within RL: A transition is OOD if its probability during real-life deployment differs from the transition distribution encountered during training. As such, we utilize conditional variational autoencoders (CVAE) to approximate the transition dynamics of the training environment and implement a conformity-based detector using reconstruction loss that is able to guarantee OOD detection with a pre-determined confidence level. We evaluate our detector by adapting existing benchmarks and compare it with existing OOD detection models for RL.
Seeing Beyond Noise: Joint Graph Structure Evaluation and Denoising for Multimodal Recommendation
Authors: Yuxin Qi; Quan Zhang; Xi Lin; Xiu Su; Jiani Zhu; Jingyu Wang; Jianhua Li
Venue: AAAI 2025 (main)

Abstract: Multimodal Recommendation Systems (MRSs) boost traditional user-item interaction-based methods by incorporating multimodal information. However, existing methods ignore the inherent noise brought by (1) noisy semantic priors in multimodal content, and (2) noisy user interactions in history records, therefore diminishing model performance. To fill this gap, we propose to denoise MRSs by jointly EValuating structure Effectiveness and mitigating Noisy links (EVEN). Firstly, for semantic prior noise in multimodal content, EVEN builds item homogeneous consistency and denoises it by evaluating behavior-driven confidence. Secondly, for noise in user interactions, EVEN updates user feedback by denoising observed interactions following implicit contribution evaluation of high-order representations. Thirdly, EVEN performs cross-modal alignment through self-guided structure learning, reinforcing task-specific inter-modal dependency modeling and cross-modal fusion. Through extensive experiments on three widely-used datasets, EVEN achieves an average improvement of 8.95% and 5.90% in recommendation accuracy compared with LGMRec and FREEDOM, respectively, without extending the total training time.
Online Fraud Detection via Test-Time Retrieval-Based Representation Enrichment
Authors: Yiran Qiao; Ningtao Wang; Yuncong Gao; Yang Yang; Xing Fu; Weiqiang Wang; Xiang Ao
Venue: AAAI 2025 (main)

Abstract: Anti-fraud machine learning systems are perpetually confronted with the significant challenge of concept drift, driven by the continuous and intense evolution of fraudulent techniques. That is, outdated models trained on historical fraudulent behaviors often fall short in addressing the evolving tactics of malicious users over time. The key issue lies in effectively tackling the rapid and significant evolution of fraudsters' behaviors to detect these emerging and unforeseen anomalies. In this paper, we propose a solution by directly accessing real-time data and introducing a lightweight plug-in approach named TRE (Test-time Retrieval-based Representation Enrichment). Considering the similarity among samples, TRE employs a retriever to efficiently identify the top-K most relevant recent samples and implements an aggregation strategy to provide neighboring embeddings to the predictor. It thus adjusts the trained classifiers during the test time, providing them with the information from the latest unlabeled data. Extensive experiments on three large-scale real-world datasets demonstrate the superiority of TRE. By consistently incorporating information from the nearest neighbors, TRE demonstrates high adaptability and surpasses existing methods in performance.
GeoMamba: Towards Multi-granular POI Recommendation with Geographical State Space Model
Authors: Yifang Qin; Jiaxuan Xie; Zhiping Xiao; Ming Zhang
Venue: AAAI 2025 (main)

Abstract: Point-of-Interest (POI) recommendation plays an important role in a wide range of location-based social network ap- plications, aiming to accurately predicting users’ next visits based on their historical check-in records. Previous efforts have primarily focused on the modifications of existing sequential models, neglecting the fact that POI visiting sequences typically involve continuous state transformation of geographical and intention signals. Additionally, the diverse time span between check-ins require the model to prop- erly recognize user’s multi-granular preference. While recent advances of State Space Model (SSM) have revealed their potential in handling intricate temporal signals, we propose a state-based model that is tailored for spatio-temporal POI sequences. On top of traditional SSMs that are typically limited to linear sequences like Mamba, we propose GeoMamba, which customizes the model states to accommodate the spatio-temporal sequences, especially fitting for POI recommendations. Specifically, while the approximation operator HiPPO sets the foundation of linear SSMs, we introduce a novel GaPPO operator that extends the model’s state space into graph-represented geographical domains. This innovation allows us to construct locational SSM encoders that seamlessly integrate users’ spatio-temporal characteristics. The sequence-aware outputs of GeoMamba are further processed to generate multi-scale behavior representations. Extensive experimental results illustrate the superiority of GeoMamba over several state-of-the-art baselines.
Domain-Level Disentanglement Framework Based on Information Enhancement for Cross-Domain Cold-Start Recommendation
Authors: Nian Rong; Fei Xiong; Shirui Pan; Guixun Luo; Jia Wu; Liang Wang
Venue: AAAI 2025 (main)

Abstract: Recommender systems in various applications often encounter the challenge of cold-start, which refers to how to provide recommendations for completely new users. Cross-domain recommendation offers a solution to address this cold-start issue by leveraging user interaction information from other domains and providing recommendations for users in the target domain. However, applying the classic two-tower model in cross-domain scenarios for pure cold-start users proves challenging, and most existing cross-domain cold-start recommendation models adopt an embedding-mapping framework that lacks end-to-end efficiency. The parallel training recommendation method lacks consideration of the domain-level intrinsic characteristics of cross-domain information. In this paper, we propose a generalized framework that Domain-level Disentanglement framework based on information enhancement for Cross-domain Cold-start Recommendation. On one hand, we achieve deep utilization of domain-level information through independent extraction of domain knowledge and fusion using heuristic strategies. On the other hand, our model is incorporated with an information enhancement network based on user attention and a user personalized adaptor. We introduce measures to assess user variability and immutability in cross-domain recommendation, aiming to eliminate inter-domain bias and highlight individual user preferences. Experimental results on widely used cross-domain recommendation datasets demonstrate that our proposed model outperforms state-of-the-art methods, validating its effectiveness.
Hyperparametric Robust and Dynamic Influence Maximization
Authors: Arkaprava Saha; Bogdan Cautis; Xiaokui Xiao; Laks V. S. Lakshmanan
Venue: AAAI 2025 (main)

Abstract: We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with theoretical quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.
Towards Loss-Resilient Image Coding for Unstable Satellite Networks
Authors: Hongwei Sha; Muchen Dong; Quanyou Luo; Ming Lu; Hao Chen; Zhan Ma
Venue: AAAI 2025 (main)

Abstract: Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. To address it, we propose a loss-resilient image coding approach that leverages end-to-end optimization in learned image compression (LIC). Our method builds on the channel-wise progressive coding framework, incorporating Spatial-Channel Rearrangement (SCR) on the encoder side and Mask Conditional Aggregation (MCA) on the decoder side to improve reconstruction quality with unpredictable errors. By integrating the Gilbert-Elliot model into the training process, we enhance the model's ability to generalize in real-world network conditions. Extensive evaluations show that our approach outperforms traditional and deep learning-based methods in terms of compression performance and stability under diverse packet loss, offering robust and efficient progressive transmission even in challenging environments.
Adversarial Contrastive Graph Masked AutoEncoder Against Graph Structure and Feature Dual Attacks
Authors: Weixuan Shen; Xiaobo Shen; Shirui Pan
Venue: AAAI 2025 (main)

Abstract: Graph Neural Networks (GNNs) have been shown vulnerable to graph adversarial attacks. Current robust graph representation learning methods mainly defend against graph structure attack, and improves performance of GNNs. However node feature in graph can been easily attacked in reality. The joint defense on graph structure and feature dual attacks remains challenging yet less studied. To fulfill this gap, we propose Adversarial Contrastive Graph Masked AutoEncoder (ACGMAE) to defend against graph structure and feature dual attacks. ACGMAE employs adversarial feature masking for reconstructing node feature to mitigate the influence of feature attack. ACGMAE employs contrastive learning on kNN graph and attacked graph, considers neighbor nodes as positive samples, and further calculates their probabilities being true positive to mitigate the effect of adversarial edges. Extensive experiments on node classification and clustering demonstrate the effectiveness of the proposed ACGMAE especially under graph structure and feature dual attacks.
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
Authors: Hao Shi; Weili Song; Xinting Zhang; Jiahe Shi; Cuicui Luo; Xiang Ao; Hamid Arian; Luis Angel Seco
Venue: AAAI 2025 (main)

Abstract: The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights.  However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model  outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.
Ready for You When You Are Back: Content-Driven Session-Based Recommendation for Continuity of Experience
Authors: Brijraj Singh; Sonal; Niranjan Pedanekar
Venue: AAAI 2025 (main)

Abstract: Recommender systems used in online platforms can drive users to consume content continuously in an attempt to maximize satisfaction. Such engagement is invariably broken due to more pressing work, alternate pursuits, distractions or fatigue. Recommender systems need to ensure the continuity of experience when the user joins back. Session-based recommender systems typically create different sessions based on a fixed time interval (θ), often resulting in creation of a separate session when the user gets off the platform temporarily. When the user joins back, session-based recommender systems are likely to recommend content different than what they would have in case the earlier session had continued. This may cause dissatisfaction given that there is a difference in the predicted world model of the user, i.e. the expectation from the last session, and the observed one, i.e. the recommendations. To handle this problem, we propose the creation of content-driven sessions instead of time-driven sessions. In our setting, a session continues while a single item category dominates in the user-item interactions. A new session is created when a different item category begins to dominate. The proposed content-driven method also solves the long-standing problem of deciding the optimal value of time threshold (θ) for defining the time-based session. We report that the proposed method outperforms existing SOTA methodologies set by time-based sessions by a large margin in terms of recommendation performance on multiple datasets.
Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints
Authors: Yash Sinha; Murari Mandal; Mohan Kankanhalli
Venue: AAAI 2025 (main)

Abstract: User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS). They recommend diverse content such as products, social media posts, TikTok reels, etc., based on a user-item interaction graph. With rising data privacy demands, recent methods propose unlearning private user data from uni-modal recommender systems (RS). However, methods for unlearning item data related to outdated user preferences, revoked licenses, and legally requested removals are still largely unexplored.

Previous RS unlearning methods are unsuitable for MMRS due to the incompatibility of their matrix-based representation with the multi-modal user-item interaction graph. Moreover, their data partitioning step degrades performance on each shard due to poor data heterogeneity and requires costly performance aggregation across shards.

This paper introduces MMRecUn, the first approach known to us for unlearning in MMRS and unlearning item data. Given a trained RS model, MMRecUn employs a novel Reverse Bayesian Personalized Ranking (BPR) objective to enable the model to forget marked data. The reverse BPR attenuates the impact of user-item interactions within the forget set, while the forward BPR reinforces the significance of user-item interactions within the retain set.
Our experiments demonstrate that MMRecUn outperforms baseline methods across various unlearning requests when evaluated on benchmark MMRS datasets. MMRecUn achieves recall performance improvements of up to 49.85% compared to baseline methods and is up to 1.3× faster than the Gold model, which is trained on retain set from scratch. MMRecUn offers significant advantages, including superiority in removing target interactions, preserving retained interactions, and zero overhead costs compared to previous methods.
Integrating Personalized Spatio-Temporal Clustering for Next POI Recommendation
Authors: Chao Song; Zheng Ren; Li Lu
Venue: AAAI 2025 (main)

Abstract: Location-Based Social Networks (LBSNs) offer a rich dataset of user activity at Points-of-Interest (POIs), making next POI recommendation a key task. Traditional algorithms face challenges due to broad searching scopes, affecting recommendation accuracy. Users tend to visit nearby POIs and show temporal concentration in their activities, reflecting personalized spatio-temporal clustering. However, individual user data may be insufficient to capture these clustering effects for personalized recommendations. In this paper, we propose an integrated Personalized Spatio-Temporal Clustering Model (iPCM) for next POI recommendation. The model learns this kind of personalized spatio-temporal clustering effect by using global historical trajectory data in conjunction with user feature embeddings. It integrates the features of personalized spatio-temporal clustering with the user's trajectory, and completes the user's POI recommendation through a Transformer encoding and MLP decoding. To enhance the accuracy of predictions, we add a module of probability adjustment. The experimental results on multiple datasets show that with the help of personalized spatio-temporal clustering, the proposed iPCM is superior to existing methods in various evaluation metrics.
UniFORM: Towards Unified Framework for Anomaly Detection on Graphs
Authors: Chuancheng Song; Xixun Lin; Hanyang Shen; Yanmin Shang; Yanan Cao
Venue: AAAI 2025 (main)

Abstract: Graph anomaly detection has attracted significant attention due to its critical applications, such as identifying money laundering in financial systems and detecting fake reviews on social networks. However, two major challenges persist: (1) anomaly detection at the node, edge, and graph levels is often addressed in isolation, hindering the integration of complementary information to identify anomalies arising from collective behaviors; and (2) the inherent label sparsity in graph data, coupled with the difficulty of obtaining high-quality annotations, exacerbates bias in detection. To address these challenges, we propose UniFORM, a unified self-supervised anomaly detection framework comprising two modules: UIO and UMC. UIO unifies node-, edge-, and graph-level tasks from a subgraph perspective, leveraging an energy-based GNN for iterative multi-granular anomaly detection. UMC enhances meta-learning through contrastive learning and employs Langevin dynamics to generate phantom samples as substitutes for anomalous data, reducing reliance on labeled data. Extensive experiments on real-world datasets demonstrate that UniFORM significantly outperforms state-of-the-art methods across multiple granularities.
Enhancing Diffusion Model with Auxiliary Information Mining-Exploration and Efficient Sampling Mechanism for Sequential Recommendation
Authors: Te Song; Lianyong Qi; Weiming Liu; Fan Wang; Xiaolong Xu; Xuyun Zhang; Amin Beheshti; Xiaokang Zhou; Wanchun Dou
Venue: AAAI 2025 (main)

Abstract: Sequential recommendation aims to capture the temporal dependencies of items in a user's historical interactions and make recommendations based on this. Previous generative methods addressed the issue of data not directly reflecting user preference uncertainty by modeling the distribution of latent item representations. Diffusion model (DM)-based methods have achieved significant success due to their high-quality generation and stable training. However, they lack satisfactory user sequence representations to guide the generation process, impacting recommendation performance. Moreover, these methods overlook the drawback of slow inference speed, severely limiting their practical value. To obtain effective generative guidance signals and accelerate the recommendation process, we propose DAE4Rec. In this approach, a Graph Auto-Encoder (GAE) is used to obtain interpretable item node representations, revealing global transitions of items that previous methods struggled to uncover. Then, we use it to construct a generative guidance signal with lower coupling and variance for the diffusion model. Additionally, by employing a non-Markov chain derived from the forward diffusion process, it is the first to implement a 'skip-step' reverse process in diffusion model-based methods. And a creatively designed compensator is used to bridge the performance gap caused by 'skip-step'. Extensive experiments on three real-world datasets demonstrate that DAE4Rec outperforms other state-of-the-art generative sequential recommenders.