Most Influential ICDE Papers (2026-03 Version)
IEEE International Conference on Data Engineering is one of the top database conferences in the world. Paper Digest Team analyzes all papers published on ICDE in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2026-03)
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TABLE 1: Most Influential ICDE Papers (2026-03 Version)
| Year | Rank | Paper | Author(s) |
|---|---|---|---|
| 2025 | 1 | EasyTime: Time Series Forecasting Made Easy IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: By demonstrating EasyTime11https://decisionintelligence.github.io/EasyTime, we aim to show how it simplifies the use of time-series forecasting and facilitates the development of new generations of time series forecasting methods. |
XIANGFEI QIU et. al. |
| 2025 | 2 | Efficient Multivariate Time Series Forecasting Via Calibrated Language Models with Privileged Knowledge Distillation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the deployment of LLMs often suffers from low efficiency during the inference phase. To address this problem, we introduce TimeKD, an efficient MTSF framework that leverages the calibrated language models and privileged knowledge distillation. |
CHENXI LIU et. al. |
| 2025 | 3 | BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although recent advances in ST data pre-training and ST foundation models aim to develop universal models for ST data analysis, most existing models are “multi-task, solo-data modality” (MTSM), meaning they can handle multiple tasks within either trajectory data or traffic state data, but not both simultaneously. To address this gap, this paper introduces BIGCity, a pioneer multi-task, multi-data modality (MTMD) model for ST data analysis. |
Xie Yu; Jingyuan Wang; Yifan Yang; Qian Huang; Ke Qu; |
| 2025 | 4 | DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we prove that directly aligning the representations of LLMs and collaborative models is suboptimal for enhancing downstream recommendation tasks performance, based on the information theorem. |
XIHONG YANG et. al. |
| 2025 | 5 | SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose SIGMA, an efficient global heterophilous GNN aggregation integrating the structural similarity measurement SimRank. |
Haoyu Liu; Ningyi Liao; Siqiang Luo; |
| 2025 | 6 | BLEND: A Unified Data Discovery System IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To reduce the execution runtime of discovery pipelines, we propose a unified index structure and a rule- and cost-based optimizer that rewrites SQL statements into low-level operators when possible. |
Mahdi Esmailoghli; Christoph Schnell; Renée J. Miller; Ziawasch Abedjan; |
| 2025 | 7 | Effective and General Distance Computation for Approximate Nearest Neighbor Search IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In particular, compared to ADSampling, our method achieves a speedup of 1.6 to 2.1 times on real-world datasets while providing higher accuracy. |
MINGYU YANG et. al. |
| 2024 | 1 | Adapting Large Language Models By Integrating Collaborative Semantics for Recommendation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. |
BOWEN ZHENG et. al. |
| 2024 | 2 | A Unified Replay-Based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To enable spatio-temporal prediction on streaming data, we propose a unified replay- based continuous learning framework. |
HAO MIAO et. al. |
| 2024 | 3 | Parameterized Decision-Making with Multi-Modality Perception for Autonomous Driving IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods either ignore the complexity of environments only fitting straight roads, or ignore the impact on surrounding vehicles during optimization phases, leading to weak environmental adaptability and incomplete optimization objectives. To address these limitations, we propose a pArameterized decision-making framework with mU lti-modality percepTiOn based on deep reinforcement learning, called AUTO. |
YUYANG XIA et. al. |
| 2024 | 4 | CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The balanced performance and recall of graph-based approaches have more recently garnered significant attention in ANNS algorithms, however, only a few studies have explored harnessing the power of GPUs and multi-core processors despite the widespread use of massively parallel and general-purpose computing. To bridge this gap, we introduce a novel parallel computing hardware-based proximity graph and search algorithm. |
HIROYUKI OOTOMO et. al. |
| 2024 | 5 | PURPLE: Making A Large Language Model A Better SQL Writer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose PURPLE (Pre-trained models Utilized to Retrieve Prompts for Logical Enhancement), which improves accuracy by retrieving demonstrations containing the requisite logical operator composition for the NL2SQL task on hand, thereby guiding LLMs to produce better SQL translation. |
TONGHUI REN et. al. |
| 2024 | 6 | HeteFedRec: Federated Recommender Systems with Model Heterogeneity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For example, clients with limited training data may prefer to train a smaller recommendation model to avoid excessive data consumption, while clients with sufficient data would benefit from a larger model to achieve higher recommendation accuracy. To address the above challenge, this paper introduces HeteFedRec, a novel FedRec framework that enables the assignment of personalized model sizes to partici-pants. |
WEI YUAN et. al. |
| 2024 | 7 | Graph Condensation for Inductive Node Representation Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. |
XINYI GAO et. al. |
| 2024 | 8 | LightTR: A Lightweight Framework for Federated Trajectory Recovery IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To bridge the gap between decentralized training and trajectory recovery, we propose a lightweight framework, LightTR, for federated trajectory recovery based on a client-server architecture, while keeping the data decentralized and private in each client/platform center (e.g., each data center of a company). |
ZIQIAO LIU et. al. |
| 2024 | 9 | FedCross: Towards Accurate Federated Learning Via Multi-Model Cross-Aggregation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. |
MING HU et. al. |
| 2024 | 10 | Multi-Modality Is All You Need for Transferable Recommender Systems IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we unleash the boundaries of the ID- based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. |
YOUHUA LI et. al. |
| 2024 | 11 | MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite resource limitations, SFL also faces two other critical challenges in EC systems, i.e., statistical heterogeneity and system heterogeneity. In order to address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. |
YUNMING LIAO et. al. |
| 2024 | 12 | AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break … |
XUNKAI LI et. al. |
| 2024 | 13 | Temporal-Frequency Masked Autoencoders for Time Series Anomaly Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While existing reconstruction-based methods have demonstrated favorable detection capabilities in the absence of labeled data, they still encounter issues of training bias on abnormal times and distribution shifts within time series. To address these issues, we propose a simple yet effective Temporal-Frequency Masked AutoEncoder (TFMAE) to detect anomalies in time series through a contrastive criterion. |
YUCHEN FANG et. al. |
| 2024 | 14 | CoachLM: Automatic Instruction Revisions Improve The Data Quality in LLM Instruction Tuning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, instead of discarding low-quality samples, we propose CoachLM, a novel approach to enhance the quality of instruction datasets through automatic revisions on samples in the dataset. |
YILUN LIU et. al. |
| 2024 | 15 | Graph Augmentation for Recommendation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Secondly, many existing GCL approaches rely on graph neural network (GNN) architectures, which can suffer from over-smoothing problems due to non-adaptive message passing. To address these challenges, we propose a principled framework called GraphAug. |
QIANRU ZHANG et. al. |
| 2023 | 1 | Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing TRL works usually treat trajectories as ordinary sequence data, while some important spatial-temporal characteristics, such as temporal regularities and travel semantics, are not fully exploited. To fill this gap, we propose a novel Self-supervised trajectory representation learning framework with TemporAl Regularities and Travel semantics, namely START. |
JIAWEI JIANG et. al. |
| 2023 | 2 | When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting Via Efficient Spectral Graph Attention Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Besides, we incorporate a novel query sampling strategy and graph wavelet-based graph positional encoding into the full graph attention network to efficiently and effectively model dynamic spatial correlations. |
YUCHEN FANG et. al. |
| 2023 | 3 | PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the construction and utilization of conditional information are inevitable challenges when applying diffusion models to spatiotemporal imputation. To address above issues, we propose a conditional diffusion framework for spatiotemporal imputation with enhanced prior modeling, named PriSTI. |
MINGZHE LIU et. al. |
| 2023 | 4 | Deep Feature-Based Text Clustering and Its Explanation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. |
RENCHU GUAN et. al. |
| 2023 | 5 | Contrastive Trajectory Similarity Learning with Dual-Feature Attention IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: An ideal measure should have the capability to accurately evaluate the similarity between any two trajectories in a very short amount of time. Towards this aim, we propose a contrastive learning-based trajectory modeling method named TrajCL. |
Yanchuan Chang; Jianzhong Qi; Yuxuan Liang; Egemen Tanin; |
| 2023 | 6 | Layer-refined Graph Convolutional Networks for Recommendation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a layer-refined GCN model, dubbed LayerGCN, that refines layer representations during information propagation and node updating of GCN. |
Xin Zhou; Donghui Lin; Yong Liu; Chunyan Miao; |
| 2023 | 7 | RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. |
Yuqi Chen; Hanyuan Zhang; Weiwei Sun; Baihua Zheng; |
| 2023 | 8 | Relational Message Passing for Fully Inductive Knowledge Graph Completion IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. |
YUXIA GENG et. al. |
| 2023 | 9 | Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Even worse, existing methods follow the paradigm of message passing that aggregates neighborhood information linearly, which fails to capture complicated spatio-temporal high-order interactions. To tackle these issues, in this paper, we propose a novel model named Dynamic Hypergraph Structure Learning (DyHSL) for traffic flow prediction. |
YUSHENG ZHAO et. al. |
| 2023 | 10 | Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, the wide variety of DI&P tasks and pipelines oftentimes requires customizing ML solutions at a significant cost for model engineering and experimentation. These factors inevitably hold back the adoption of ML-based approaches to new domains and tasks.In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based on contrastive representation learning. |
Runhui Wang; Yuliang Li; Jin Wang; |
| 2023 | 11 | HyGNN: Drug-Drug Interaction Prediction Via Hypergraph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the Simplified Molecular Input Line Entry System (SMILES) string of drugs, available for any drug, for the DDI prediction problem. |
Khaled Mohammed Saifuddin; Briana Bumgardner; Farhan Tanvir; Esra Akbas; |
| 2023 | 12 | Self-Supervised Spatial-Temporal Bottleneck Attentive Network for Efficient Long-term Traffic Forecasting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Aiming at solving the long-term traffic forecasting problem and facilitating the deployment of traffic forecasting models in practice, this paper proposes an efficient and effective Self-supervised Spatial-Temporal Bottleneck Attentive Network (SSTBAN). |
SHENGNAN GUO et. al. |
| 2023 | 13 | RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the abovementioned challenges, we propose an advanced method, namely, RETIA. |
Kangzheng Liu; Feng Zhao; Guandong Xu; Xianzhi Wang; Hai Jin; |
| 2023 | 14 | TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In particular, we systematically formulate the transaction allocation problem and convert it to the community detection problem on a graph. |
Yuanzhe Zhang; Shirui Pan; Jiangshan Yu; |
| 2023 | 15 | Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose an efficient Transformer-based model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoder-decoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of sliding-window attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. |
YAN LI et. al. |
| 2022 | 1 | Federated Learning on Non-IID Data Silos: An Experimental Study IF:8 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, to help researchers better understand and study the non-IID data setting in federated learning, we propose comprehensive data partitioning strategies to cover the typical non-IID data cases. |
Qinbin Li; Yiqun Diao; Quan Chen; Bingsheng He; |
| 2022 | 2 | Contrastive Learning for Sequential Recommendation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To tackle that, inspired by recent advances of contrastive learning techniques in the computer vision, we propose a novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec). |
XU XIE et. al. |
| 2022 | 3 | Towards Spatio- Temporal Aware Traffic Time Series Forecasting IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a framework that aims at turning spatio-temporal agnostic models to spatio-temporal aware models. |
Razvan-Gabriel Cirstea; Bin Yang; Chenjuan Guo; Tung Kieu; Shirui Pan; |
| 2022 | 4 | Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Self-Supervised Hypergraph Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. |
Zhonghang Li; Chao Huang; Lianghao Xia; Yong Xu; Jian Pei; |
| 2022 | 5 | Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose attentive Knowledge-aware Graph convolutional networks with Collaborative Guidance for personalized Recommendation (CG-KGR). |
YANKAI CHEN et. al. |
| 2022 | 6 | Cross-Domain Recommendation to Cold-Start Users Via Variational Information Bottleneck IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we consider a key point of the CDR task: what information needs to be shared across domains? |
Jiangxia Cao; Jiawei Sheng; Xin Cong; Tingwen Liu; Bin Wang; |
| 2022 | 7 | FedRecAttack: Model Poisoning Attack to Federated Recommendation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. |
DAZHONG RONG et. al. |
| 2022 | 8 | Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and efficient anomaly detection in time series in unsupervised settings. |
TUNG KIEU et. al. |
| 2022 | 9 | FedMP: Federated Learning Through Adaptive Model Pruning in Heterogeneous Edge Computing IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We theoretically analyze the impact of pruning ratio on model training performance, and propose to employ a Multi-Armed Bandit based online learning algorithm to adaptively determine different pruning ratios for heterogeneous edge nodes, even without any prior knowledge of their computation and communication capabilities. |
ZHIDA JIANG et. al. |
| 2022 | 10 | Improving Fairness for Data Valuation in Horizontal Federated Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. |
ZHENAN FAN et. al. |
| 2022 | 11 | VChain+: Optimizing Verifiable Blockchain Boolean Range Queries IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a new searchable blockchain system, vChain+, that supports efficient verifiable boolean range queries with additional features. |
HAIXIN WANG et. al. |
| 2022 | 12 | FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a new FL protocol termed FedADMM based on primal-dual optimization. |
Yonghai Gong; Yichuan Li; Nikolaos M. Freris; |
| 2022 | 13 | Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised auto encoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. |
TUNG KIEU et. al. |
| 2022 | 14 | Semantics Driven Embedding Learning for Effective Entity Alignment IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on the fundamental problem in knowledge base integration, i.e., entity alignment (EA). |
Ziyue Zhong; Meihui Zhang; Ju Fan; Chenxiao Dou; |
| 2022 | 15 | DualGraph: Improving Semi-supervised Graph Classification Via Dual Contrastive Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we study semi-supervised graph classification, a fundamental problem in data mining and machine learning. |
XIAO LUO et. al. |
| 2021 | 1 | Feature Inference Attack on Model Predictions in Vertical Federated Learning IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. |
X. Luo; Y. Wu; X. Xiao; B. C. Ooi; |
| 2021 | 2 | Optimizing Error-Bounded Lossy Compression for Scientific Data By Dynamic Spline Interpolation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a novel error-bounded lossy compressor based on a state-of-the-art prediction-based compression framework. |
K. ZHAO et. al. |
| 2021 | 3 | CleanML: A Study for Evaluating The Impact of Data Cleaning on ML Classification Tasks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a CleanML study that systematically investigates the impact of data cleaning on ML classification tasks. |
P. LI et. al. |
| 2021 | 4 | Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. |
P. Gao; et al. |
| 2021 | 5 | DAEMON: Unsupervised Anomaly Detection and Interpretation for Multivariate Time Series IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To overcome those limitations, in this paper, we propose an unsupervised anomaly detection framework, called DAEMON (Adversarial Autoencoder Anomaly Detection Interpretation), which performs robustly for various datasets. |
X. Chen; et al. |
| 2021 | 6 | Valentine: Evaluating Matching Techniques for Dataset Discovery IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we aim to rectify the problem of evaluating the effectiveness and efficiency of schema matching methods for the specific needs of dataset discovery. |
C. Koutras; et al. |
| 2021 | 7 | Search to Aggregate Neighborhood for Graph Neural Network IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures. |
H. ZHAO; Q. YAO; W. TU; |
| 2021 | 8 | Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose PEXESO, a framework for joinable table discovery in data lakes. |
Y. Dong; K. Takeoka; C. Xiao; M. Oyamada; |
| 2021 | 9 | RCC: Resilient Concurrent Consensus for High-Throughput Secure Transaction Processing IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To push throughput beyond this single-replica limit, we propose concurrent consensus. |
S. Gupta; J. Hellings; M. Sadoghi; |
| 2021 | 10 | T3S: Effective Representation Learning for Trajectory Similarity Computation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a deep learning based model, namely T3S, which embeds each trajectory (i.e., a sequence of points) into a vector (point) in a d-dimensional space, and hence can significantly accelerate the similarity computation between the trajectories. |
P. YANG et. al. |
| 2021 | 11 | Attacking Black-box Recommendations Via Copying Cross-domain User Profiles IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present a novel framework CopyAttack. It is a reinforcement learning based black-box attacking method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items. |
W. Fan; et al. |
| 2021 | 12 | EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we propose two plugin neural networks that are able to better capture distinct temporal dynamics for different entities and dynamic entity correlations across time, so that forecasting accuracy is improved while model parameters to be learned are reduced. |
R. -G. Cirstea; T. Kieu; C. Guo; B. Yang; S. J. Pan; |
| 2021 | 13 | Meepo: Sharded Consortium Blockchain IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. |
P. ZHENG et. al. |
| 2021 | 14 | Efficient and Effective Community Search on Large-scale Bipartite Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we study the significant (a, ?)-community search problem on weighted bipartite graphs. |
K. WANG et. al. |
| 2021 | 15 | Fairness-aware Task Assignment in Spatial Crowdsourcing: Game-Theoretic Approaches IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In particular, we aim to minimize the payoff difference among workers while maximizing the average worker payoff. |
Y. ZHAO et. al. |
| 2020 | 1 | FakeDetector: Effective Fake News Detection With Deep Diffusive Neural Network IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. |
J. Zhang; B. Dong and P. S. Yu; |
| 2020 | 2 | An Intersectional Definition Of Fairness IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. |
J. R. Foulds; R. Islam; K. N. Keya and S. Pan; |
| 2020 | 3 | Self-paced Ensemble For Highly Imbalanced Massive Data Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Taking those factors into consideration, we propose a novel framework for imbalance classification that aims to generate a strong ensemble by self-paced harmonizing data hardness via under-sampling. |
Z. Liu et al.; |
| 2020 | 4 | Dataset Discovery In Data Lakes IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We refer to this as the problem of dataset discovery in data lakes and this paper contributes an effective and efficient solution to it. |
A. Bogatu; A. A. A. Fernandes; N. W. Paton and N. Konstantinou; |
| 2020 | 5 | Reinforcement Learning With Tree-LSTM For Join Order Selection IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present RTOS, a novel learned optimizer that uses Reinforcement learning with Tree-structured long short-term memory (LSTM) for join Order Selection. |
X. Yu; G. Li; C. Chai and N. Tang; |
| 2020 | 6 | Online Anomalous Trajectory Detection With Deep Generative Sequence Modeling IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel model, namely Gaussian Mixture Variational Sequence AutoEncoder (GM-VSAE), to tackle these challenges. |
Y. Liu; K. Zhao; G. Cong and Z. Bao; |
| 2020 | 7 | SONG: Approximate Nearest Neighbor Search On GPU IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a novel framework that decouples the searching on graph algorithm into 3 stages, in order to parallel the performance-crucial distance computation. |
W. Zhao; S. Tan and P. Li; |
| 2020 | 8 | Collective Entity Alignment Via Adaptive Features IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To fill this gap, we propose a collective EA framework. |
W. Zeng; X. Zhao; J. Tang and X. Lin; |
| 2020 | 9 | Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these problems, we propose a novel method with Hierarchical bipartite Graph Neural Network (HiGNN) to handle large-scale e-commerce tasks. |
Z. Li et al.; |
| 2020 | 10 | AutoSF: Searching Scoring Functions For Knowledge Graph Embedding IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, inspired by the recent success of automated machine learning (AutoML), we propose to automatically design SFs (AutoSF) for distinct KGs by the AutoML techniques. |
Y. Zhang; Q. Yao; W. Dai and L. Chen; |
| 2020 | 11 | Efficient Bitruss Decomposition For Large-scale Bipartite Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k = 0. |
K. Wang; X. Lin; L. Qin; W. Zhang and Y. Zhang; |
| 2020 | 12 | Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To solve this problem, we propose a generic learning framework that (i) employs matrix factorization and graph convolutional neural networks to contend with the data sparseness while capturing spatial correlations and that (ii) captures spatio-temporal dynamics via recurrent neural networks extended with graph convolutions. |
J. Hu; B. Yang; C. Guo; C. S. Jensen and H. Xiong; |
| 2020 | 13 | Predicting Origin-Destination Flow Via Multi-Perspective Graph Convolutional Network IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Multi-Perspective Graph Convolutional Networks (MPGCN) to capture the complex dependencies. |
H. Shi et al.; |
| 2020 | 14 | PoisonRec: An Adaptive Data Poisoning Framework For Attacking Black-box Recommender Systems IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose an adaptive data poisoning framework, PoisonRec, which can automatically learn effective attack strategies on various recommender systems with very limited knowledge. |
J. Song et al.; |
| 2020 | 15 | Sequence-Aware Factorization Machines For Temporal Predictive Analytics IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Hence, in this paper, we propose a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. |
T. Chen; H. Yin; Q. V. Hung Nguyen; W. Peng; X. Li and X. Zhou; |
| 2019 | 1 | Collecting And Analyzing Multidimensional Data With Local Differential Privacy IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. |
N. Wang et al.; |
| 2019 | 2 | Neural Multi-task Recommendation From Multi-behavior Data IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. |
C. Gao et al.; |
| 2019 | 3 | Social Influence-Based Group Representation Learning For Group Recommendation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel group recommender system, namely SIGR (short for Social Influence-based Group Recommender), which takes an attention mechanism and a bipartite graph embedding model BGEM as building blocks. We create two large-scale benchmark datasets and conduct extensive experiments on them. |
H. Yin; Q. Wang; K. Zheng; Z. Li; J. Yang and X. Zhou; |
| 2019 | 4 | IFair: Learning Individually Fair Data Representations For Algorithmic Decision Making IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The paper introduces a method for probabilistically mapping user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications. |
P. Lahoti; K. P. Gummadi and G. Weikum; |
| 2019 | 5 | Presto: SQL On Everything IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we outline a selection of use cases that Presto supports at Facebook. |
R. Sethi et al.; |
| 2019 | 6 | Information Diffusion Prediction Via Recurrent Cascades Convolution IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To capture both the underlying structures governing the spread of information and inherent dependencies between re-tweeting behaviors of users, we propose a semi-supervised method, called Recurrent Cascades Convolutional Networks (CasCN), which explicitly models and predicts cascades through learning the latent representation of both structural and temporal information, without involving any other features. |
X. Chen; F. Zhou; K. Zhang; G. Trajcevski; T. Zhong and F. Zhang; |
| 2019 | 7 | Slice Finder: Automated Data Slicing For Model Validation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are large and problematic. |
Y. Chung; T. Kraska; N. Polyzotis; K. H. Tae and S. E. Whang; |
| 2019 | 8 | Computing Trajectory Similarity In Linear Time: A Generic Seed-Guided Neural Metric Learning Approach IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose NeuTraj to accelerate trajectory similarity computation. |
D. Yao; G. Cong; C. Zhang and J. Bi; |
| 2019 | 9 | NSCaching: Simple And Efficient Negative Sampling For Knowledge Graph Embedding IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, motivated by the observation that negative triplets with large scores are important but rare, we propose to directly keep track of them with cache. |
Y. Zhang; Q. Yao; Y. Shao and L. Chen; |
| 2019 | 10 | Towards Explaining The Effects Of Data Preprocessing On Machine Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this initial work we define a simple metric, which we call volatility, to measure the effect of including/excluding a specific step on predictions made by the resulting model. |
C. V. Gonzalez Zelaya; |
| 2019 | 11 | Assessing And Remedying Coverage For A Given Dataset IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we assess the coverage of a given dataset over multiple categorical attributes. |
A. Asudeh; Z. Jin and H. V. Jagadish; |
| 2019 | 12 | GEM^2-Tree: A Gas-Efficient Structure For Authenticated Range Queries In Blockchain IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. |
C. Zhang; C. Xu; J. Xu; Y. Tang and B. Choi; |
| 2019 | 13 | Adaptive Dynamic Bipartite Graph Matching: A Reinforcement Learning Approach IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose the dynamic bipartite graph matching (DBGM) problem to be better aligned with real-world applications and devise a novel adaptive batch-based solution framework with a constant competitive ratio. |
Y. Wang; Y. Tong; C. Long; P. Xu; K. Xu and W. Lv; |
| 2019 | 14 | AIR: Attentional Intention-Aware Recommender Systems IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Hence, in this paper, we propose AIR, namely attentional intention-aware recommender systems to predict category-wise future user intention and collectively exploit the rich heterogeneous user interaction behaviors (i.e., multiple types of user behaviors). |
T. Chen; H. Yin; H. Chen; R. Yan; Q. V. H. Nguyen and X. Li; |
| 2019 | 15 | Distributed In-memory Trajectory Similarity Search And Join On Road Network IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To support trajectory similarity search and join, we propose a filtering-refine framework. |
H. Yuan and G. Li; |