GenAI4ELab/papercli-papers huggingface dataset:
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods
Authors: Wenzhen Yue; Xianghua Ying; Ruohao Guo; DongDong Chen; Ji Shi; Bowei Xing; Yuqing Zhu; Taiyan Chen
Venue: IJCAI 2024 (main)

Abstract: In this paper, we present the Sub-Adjacent Transformer with a novel attention mechanism for unsupervised time series anomaly detection. Unlike previous approaches that rely on all the points within some neighborhood for time point reconstruction, our method restricts the attention to regions not immediately adjacent to the target points, termed sub-adjacent neighborhoods. Our key observation is that owing to the rarity of anomalies, they typically exhibit more pronounced differences from their sub-adjacent neighborhoods than from their immediate vicinities. By focusing the attention on the sub-adjacent areas, we make the reconstruction of anomalies more challenging, thereby enhancing their detectability. Technically, our approach concentrates attention on the non-diagonal areas of the attention matrix by enlarging the corresponding elements in the training stage. To facilitate the implementation of the desired attention matrix pattern, we adopt linear attention because of its flexibility and adaptability. Moreover, a learnable mapping function is proposed to improve the performance of linear attention. Empirically, the Sub-Adjacent Transformer achieves state-of-the-art performance across six real-world anomaly detection benchmarks, covering diverse fields such as server monitoring, space exploration, and water treatment.
Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names
Authors: Dabin Zhang; Meng Chen; Weiming Huang; Yongshun Gong; Kai Zhao
Venue: IJCAI 2024 (main)

Abstract: Semantic annotation for points of interest (POIs) is the process of annotating a POI with a category label, which facilitates many services related to POIs, such as POI search and recommendation. Most of the existing solutions extract features related to POIs from abundant user-generated content data (e.g., check-ins and user comments). However, such data are often difficult to obtain, especially for newly created POIs. In this paper, we aim to explore semantic annotation for POIs with limited information such as POI (place) names and geographic locations. Additionally, we have found that the street view images provide extensive visual clues about POI attributes and could be an essential supplement to limited information of POIs that enables semantic annotation. To this end, we propose a novel multimodal model for POI semantic annotation, namely M3PA, which achieves enhanced semantic annotation through fusing a POI’s textual and visual representations. Specifically, M3PA extracts visual features from street view images using a pre-trained image encoder and integrates these features to generate the visual representation of a targeted POI based on a geographic attention mechanism. Furthermore, M3PA utilizes the contextual information of neighboring POIs to extract textual features and captures their spatial relationships through geographical encoding to generate the textual representation of a targeted POI. Finally, the visual and textual representations of a POI are fused for semantic annotation. Extensive experiments with POI data from Amap validate the effectiveness of M3PA for POI semantic annotation, compared with several competitive baselines.
Natural Language-centered Inference Network for Multi-modal Fake News Detection
Authors: Qiang Zhang; Jiawei Liu; Fanrui Zhang; Jingyi Xie; Zheng-Jun Zha
Venue: IJCAI 2024 (main)

Abstract: The proliferation of fake news with image and text in the internet has triggered widespread concern. Existing research has made important contributions in cross-modal information interaction and fusion, but fails to fundamentally address the modality gap among news image, text, and news-related external knowledge representations. In this paper, we propose a novel Natural Language-centered Inference Network (NLIN) for multi-modal fake news detection by aligning multi-modal news content with the natural language space and introducing an encoder-decoder architecture to fully comprehend the news in-context. Specifically, we first unify multi-modal news content into textual modality by converting news images and news-related external knowledge into plain textual content. Then, we design a multi-modal feature reasoning module, which consists of a multi-modal encoder, a unified-modal context encoder and an inference decoder with prompt phrase. This framework not only fully extracts the latent representation of cross-modal news content, but also utilizes the prompt phrase to stimulate the powerful in-context learning ability of the pre-trained large language model to reason about the truthfulness of the news content. In addition, to support the research in the field of multi-modal fake news detection, we produce a challenging large scale, multi-platform, multi-domain multi-modal Chinese Fake News Detection (CFND) dataset. Extensive experiments show that our CFND dataset is challenging and the proposed NLIN outperforms state-of-the-art methods.
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
Authors: Shunyang Zhang; Senzhang Wang; Hao Miao; Hao Chen; Changjun Fan; Jian Zhang
Venue: IJCAI 2024 (main)

Abstract: Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal features learning. However, it is hard to adaptively trade off the diverse effects of local and global temporal features by simply combining CNN and attention. To address this issue, we propose a Score-weighted Convolutional Diffusion Model (Score-CDM for short), whose backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive Reception Module (ARM). SCM adopts a score map to capture the global temporal features in the time domain, while ARM uses a Spectral2Time Window Block (S2TWB) to convolve the local time series data in the spectral domain. Benefiting from the time convolution properties of Fast Fourier Transformation, ARM can adaptively change the receptive field of the score map, and thus effectively balance the local and global temporal features. We conduct extensive evaluations on three real MTS datasets of different domains, and the result verifies the effectiveness of the proposed Score-CDM.
SaSDim:Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation
Authors: Shunyang Zhang; Senzhang Wang; Xianzhen Tan; Renzhi Wang; Ruochen Liu; Jian Zhang; Jianxin Wang
Venue: IJCAI 2024 (main)

Abstract: Spatial time series imputation is of great importance to various real-world applications. As the state-of-the-art generative models, diffusion models (e.g. CSDI) have outperformed statistical and autoregressive based models in time series imputation. However, diffusion models may introduce unstable noise owing to the inherent uncertainty in sampling, leading to the generated noise deviating from the intended Gaussian distribution. Consequently, the imputed data may deviate from the real data. To this end, we propose a Self-adaptive noise Scaling Diffusion Model named SaSDim for spatial time series imputation. Specifically, we introduce a novel Probabilistic High-Order SDE Solver Module to scale the noise following the standard Gaussian distribution. The noise scaling operation helps the noise prediction module of the diffusion model to more accurately estimate the variance of noise. To effectively learn the spatial and temporal features, a Spatial guided Global Convolution Module (SgGConv) for multi-periodic temporal dependencies learning with the Fast Fourier Transformation and dynamic spatial dependencies learning with dynamic graph convolution is also proposed. Extensive experiments conducted on three real-world spatial time series datasets verify the effectiveness of SaSDim.
TFWT: Tabular Feature Weighting with Transformer
Authors: Xinhao Zhang; Zaitian Wang; Lu Jiang; Wanfu Gao; Pengfei Wang; Kunpeng Liu
Venue: IJCAI 2024 (main)

Abstract: In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique contributions of each feature, and thus may miss important feature information. As a result, it leads to suboptimal performance in complex datasets with rich features. To address this problem, we introduce Tabular Feature Weighting with Transformer, a novel feature weighting approach for tabular data. Our method adopts Transformer to capture complex feature dependencies and contextually assign appropriate weights to discrete and continuous features. Besides, we employ a reinforcement learning strategy to further fine-tune the weighting process. Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis.
G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
Authors: Zhanwei Zhang; Zishuo Hua; Minghao Chen; Wei Lu; Binbin Lin; Deng Cai; Wenxiao Wang
Venue: IJCAI 2024 (main)

Abstract: Predicting future trajectories of traffic agents accurately holds substantial importance in various applications such as autonomous driving. Previous methods commonly infer all future steps of an agent either recursively or simultaneously. However, the recursive strategy suffers from the accumulated error, while the simultaneous strategy overlooks the constraints among future steps, resulting in kinematically infeasible predictions. To address these issues, in this paper, we propose G2LTraj, a plug-and-play global-to-local generation approach for trajectory prediction. Specifically, we generate a series of global key steps that uniformly cover the entire future time range. Subsequently, the local intermediate steps between the adjacent key steps are recursively filled in. In this way, we prevent the accumulated error from propagating beyond the adjacent key steps. Moreover, to boost the kinematical feasibility, we not only introduce the spatial constraints among key steps but also strengthen the temporal constraints among the intermediate steps. Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory. Our G2LTraj significantly improves the performance of seven existing trajectory predictors across the ETH, UCY and nuScenes datasets. Experimental results demonstrate its effectiveness. Code will be available at https://github.com/Zhanwei-Z/G2LTraj.
A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq
Authors: Yaya Zhao; Kaiqi Zhao; Zhiqian Chen; Yuanyuan Zhang; Yalei Du; Xiaoling Lu
Venue: IJCAI 2024 (main)

Abstract: The prevalent issue in urban trajectory data usage, notably in low-sample rate datasets, revolves around the accuracy of travel time estimations, traffic flow predictions, and trajectory similarity measurements. Conventional methods, often relying on simplistic mixes of static road networks and raw GPS data, fail to adequately integrate both network and trajectory dimensions. Addressing this, the innovative GRFTrajRec framework offers a graph-based solution for trajectory recovery. Its key feature is a trajectory-aware graph representation, enhancing the understanding of trajectory-road network interactions and facilitating the extraction of detailed embedding features for road segments. Additionally, GRFTrajRec's trajectory representation acutely captures spatiotemporal attributes of trajectory points. Central to this framework is a novel spatiotemporal interval-informed seq2seq model, integrating an attention-enhanced transformer and a feature differences-aware decoder. This model specifically excels in handling spatiotemporal intervals, crucial for restoring missing GPS points in low-sample datasets. Validated through extensive experiments on two large real-life trajectory datasets, GRFTrajRec has proven its efficacy in significantly boosting prediction accuracy and spatial consistency.
Modeling Personalized Retweeting Behaviors for Multi-Stage Cascade Popularity Prediction
Authors: Mingyang Zhou; Yanjie Lin; Gang Liu; Zuwen Li; Hao Liao; Rui Mao
Venue: IJCAI 2024 (main)

Abstract: Predicting the size of message cascades is critical in various applications, such as online advertising and early detection of rumors. However, most existing deep learning approaches rely on cascade observation, which hinders accurate cascade prediction before message posting. Besides, these approaches overlook personalized retweeting behaviors that reflect users' inclination to retweeting specific types of information. In this study, we propose a universal cascade prediction framework, namely Cascade prediction regarding Multiple Stage (CasMS), that effectively predicts cascade popularity across message generation stage as well as short-term and long-term stages. Unlike previous methods, our approach not only captures users' personalized retweeting behaviors but also incorporates temporal cascade features. We perform the experiments in datasets collected ourselves as well as public datasets. The results show that our method significantly surpasses existing approaches in predicting the cascade during the message generation stage and different time periods in the cascade dynamics.
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
Authors: Yicheng Zhou; Pengfei Wang; Hao Dong; Denghui Zhang; Dingqi Yang; Yanjie Fu; Pengyang Wang
Venue: IJCAI 2024 (main)

Abstract: Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology. While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. Specifically, we first develop a Dual Cross-Scale Transformer (DCST) architecture, including a Spatial Transformer and a Temporal Transformer, to preserve the cross-scale topology-free patterns and associated dynamics, respectively. Then, to further integrate both topology-regularized/-free patterns, we propose a distillation-style learning framework, in which the existing GNN-based methods are considered as the teacher model, and the proposed DCST architecture is considered as the student model. The teacher model would inject the learned topology-regularized patterns into the student model for integrating topology-free patterns. The extensive experimental results demonstrated the effectiveness of our methods.
Generalized Taxonomy-Guided Graph Neural Networks
Authors: Yu Zhou; Di Jin; Jianguo Wei; Dongxiao He; Zhizhi Yu; Weixiong Zhang
Venue: IJCAI 2024 (main)

Abstract: Graph neural networks have been demonstrated to be effective analytic apparatus for mining network data. Most real-world networks are inherently hierarchical, offering unique opportunities to acquire latent, intrinsic network organizational properties by utilizing network taxonomies. The existing approaches for learning implicit hierarchical network structures focus on introducing taxonomy to graph neural networks but often run short of exploiting the rich network semantics and structural properties in the taxonomy, resulting in poor generalizability and reusability. To address these issues, we propose generalized Taxonomy-Guided Graph Neural Networks (TG-GNN) to integrate taxonomy into network representation learning. We first construct a taxonomy representation learning module that introduces the concept of ego network to propagate and aggregate rich semantic and structural information in the taxonomy. We then design a taxonomy-guided Markov mechanism, which encapsulates taxonomy knowledge in pairwise potential functions, to refine network embeddings. Extensive experiments on various real-world networks illustrate the effectiveness of TG-GNN over the state-of-the-art methods on scenarios involving incomplete taxonomies and inductive settings.
Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning
Authors: Guogang Zhu; Xuefeng Liu; Xinghao Wu; Shaojie Tang; Chao Tang; Jianwei Niu; Hao Su
Venue: IJCAI 2024 (main)

Abstract: Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model. In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model parameters or outputs. However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. Building upon this insight, we propose a debiasing method for FSSL named FedDB. FedDB utilizes the Average Prediction Probability of Unlabeled Data (APP-U) to approximate the biased prior. During local training, FedDB employs APP-U to refine pseudo-labeling through Bayes' theorem, thereby significantly reducing the label prior bias. Concurrently, during the model aggregation, FedDB uses APP-U from participating clients to formulate unbiased aggregate weights, thereby effectively diminishing bias in the global model. Experimental results show that FedDB can surpass existing FSSL methods. The code is available at https://github.com/GuogangZhu/FedDB.
R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation
Authors: Nengjun Zhu; Jieyun Huang; Jian Cao; Liang Hu; Zixuan Yuan; Huanjing Gao
Venue: IJCAI 2024 (main)

Abstract: Integrating data-driven and rule-based approaches is crucial for therapy recommendations since they can collaborate to achieve better performance. Medical rules, which are chains of reasoning that can infer therapies, widely exist. However, their symbolic and logical forms make integrating them with data-driven modeling technologies hard. Although rare attempts have indirectly modeled rules using data that supports them, the poor generalization of medical rules leads to inadequate supporting data and thus impairs the benefit of medical rules. To this end, we propose R2V-MIF, which fills the gap by rule-to-vector contrastive learning (R2V) and multi-channel information fusion (MIF). R2V is a data-free module and utilizes a hypergraph, including condition and result nodes, to instantiate the logic of medical rules. Each rule is reflected in the relations between nodes, and their representations are determined through contrastive learning. By taking rule representations as a bridge, MIF integrates the knowledge from medical rules, similar neighbors, and patient contents, and then recommends therapies. Extensive experiments show that R2V-MIF outperforms the baselines in several metrics using real-world medical data. Our code is available at https://github.com/vgeek-z/r2vmif.
Optimizing Viscous Democracy
Authors: Ben Armstrong; Shiri Alouf-Heffetz; Nimrod Talmon
Venue: IJCAI 2024 (main)

Abstract: Viscous democracy is a generalization of liquid democracy, a social choice framework in which voters may transitively delegate their votes. In viscous democracy, a "viscosity" factor decreases the weight of a delegation the further it travels, reducing the chance of excessive weight flowing between ideologically misaligned voters. We demonstrate that viscous democracy often significantly improves the quality of group decision-making over liquid democracy. We first show that finding optimal delegations within a viscous setting is NP-hard. However, simulations allow us to explore the practical effects of viscosity. Across social network structures, competence distributions, and delegation mechanisms we find high viscosity reduces the chance of ``super-voters'' attaining large amounts of weight and increases the number of voters that are able to affect the outcome of elections. This, in turn, improves group accuracy as a whole. As a result, we argue that viscosity should be considered a core component of liquid democracy.
Facility Location Problems with Capacity Constraints: Two Facilities and Beyond
Authors: Gennaro Auricchio; Zihe Wang; Jie Zhang
Venue: IJCAI 2024 (main)

Abstract: In this paper, we investigate the Mechanism Design aspects of the m-Capacitated Facility Location Problem (m-CFLP) on a line. We focus on two frameworks. In the first framework, the number of facilities is arbitrary, all facilities have the same capacity, and the number of agents is equal to the total capacity of all facilities. In the second framework, we aim to place two facilities, each with a capacity of at least half of the total agents. For both of these frameworks, we propose truthful mechanisms with bounded approximation ratios with respect to the Social Cost (SC) and the Maximum Cost (MC). When m>2, the result sharply contrasts with the impossibility results known for the classic m-Facility Location Problem, where capacity constraints are not considered. Furthermore, all our mechanisms are optimal with respect to the MC and optimal or nearly optimal with respect to the SC among anonymous mechanisms. For both frameworks, we provide a lower bound on the approximation ratio that any truthful and deterministic mechanism can achieve with respect to the SC and MC.
Welfare Loss in Connected Resource Allocation
Authors: Xiaohui Bei; Alexander Lam; Xinhang Lu; Warut Suksompong
Venue: IJCAI 2024 (main)

Abstract: We study the allocation of indivisible goods that form an undirected graph and investigate the worst-case welfare loss when requiring that each agent must receive a connected subgraph. Our focus is on both egalitarian and utilitarian welfare. Specifically, we introduce the concept of egalitarian (resp., utilitarian) price of connectivity, which captures the worst-case ratio between the optimal egalitarian (resp., utilitarian) welfare among all allocations and that among the connected allocations. We provide tight or asymptotically tight bounds on the price of connectivity for various large classes of graphs when there are two agents, and for paths, stars and cycles in the general case. Many of our results are supplemented with algorithms which find connected allocations with a welfare guarantee corresponding to the price of connectivity.