2021 IEEE International Conference on Data Mining (ICDM)最新文献_第10页

Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling 流动态图神经网络用于连续时间图建模

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00171

Sheng Tian, T. Xiong, Leilei Shi

{"title":"Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling","authors":"Sheng Tian, T. Xiong, Leilei Shi","doi":"10.1109/ICDM51629.2021.00171","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00171","url":null,"abstract":"Dynamic graphs are suitable for modeling structured data that evolve over time and have been widely used in many application scenarios such as social networks, financial transaction networks, and recommendation systems. Recently, many dynamic graph methods are proposed to deal with temporal networks. However, due to the limitations of storage space and computational efficiency, most approaches evolve node representations by aggregating the latest state information of neighbor nodes, thus losing a lot of information about neighbor nodes’ state changes. Besides, high computational complexity makes it challenging to deploy dynamic graph algorithms in real-time. To tackle these challenges, we propose a novel streaming dynamic graph neural network (SDGNN) for modeling continuous-time temporal graphs, which can fully capture the state changes of neighbors and reduce the computational complexity of inference. Under SDGNN, an incremental update component is designed to incrementally update node representation based on the interaction sequence, an inference component is utilized for specific downstream tasks, and a message propagation component is employed to propagate interactive information to the influenced nodes by considering the update time interval, position distance, and influence strengths. Extensive experiments demonstrated that the proposed approach significantly outperforms state-of-the-art methods by capturing more state change information and efficient parallelization.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116875272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation 基于可靠性传播的灵活、鲁棒、可扩展半监督学习

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00030

Chen Huang, Liangxu Pan, Qinli Yang, Honglian Wang, Junming Shao

{"title":"Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation","authors":"Chen Huang, Liangxu Pan, Qinli Yang, Honglian Wang, Junming Shao","doi":"10.1109/ICDM51629.2021.00030","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00030","url":null,"abstract":"Semi-supervised learning aims to generate a model with a better performance using plenty of unlabeled data. However, most existing methods treat unlabeled data equally without considering whether it is safe or not, which may lead to the degradation of prediction performance. In this paper, towards reliable semi-supervised learning, we propose a data-driven algorithm, called Reliability Propagation (RP), to learn the reliability of each unlabeled instance. The basic idea is to take local label regularity as a prior, and then perform reliability propagation on an adaptive graph. As a result, the most reliable unlabeled instances could be selected to construct a safer classifier. Beyond, the distributed RP algorithm is introduced to scale up to large volumes of data. In contrast to existing approaches, RP exploits the structural information and shed light on the soft instance selection for unlabeled data in a classifier-independent way. Experiments on both synthetic and real-world data have demonstrated that RP allows extracting most reliable unlabeled instances and supports a gained prediction performance compared to other algorithms.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130692450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series 优点:不规则时间序列慢性病的药物推荐

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00192

Shuai Zhang, Jianxin Li, Haoyi Zhou, Qishan Zhu, Shanghang Zhang, Danding Wang

{"title":"MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series","authors":"Shuai Zhang, Jianxin Li, Haoyi Zhou, Qishan Zhu, Shanghang Zhang, Danding Wang","doi":"10.1109/ICDM51629.2021.00192","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00192","url":null,"abstract":"Medication recommendation for chronic diseases based on the complex historical electronic medical records (EMR) is an important and challenging research problem in medical informatics because the medical records are often irregularly sampled and contain many missing data. However, most existing approaches fail to explore the irregular time-series dependencies and ignore the consecutive correlation in dynamic prescription history. To fill this gap, we propose the MEdication Recommendation network on Irregular Time-Series (MERITS), which captures the irregular time-series dependencies with the neural ordinary differential equations (Neural ODE). Meanwhile, it leverages a drug-drug interaction knowledge graph and two learned medication relation graphs to explore the co-occurrence and sequential correlations of the medications. We further propose an attention-based encoder-decoder framework to combine the historical information of patients and medications from EMR. Besides, we collect and annotate a diabetes inpatient medication dataset and demonstrate the effectiveness of MERITS by comparing it with several state-of-the-art methods of medication recommendations.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130734951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Generating Explanations for Recommendation Systems via Injective VAE 通过注入式VAE生成推荐系统的解释

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00115

Zerui Cai

{"title":"Generating Explanations for Recommendation Systems via Injective VAE","authors":"Zerui Cai","doi":"10.1109/ICDM51629.2021.00115","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00115","url":null,"abstract":"Generating explanations for recommendation systems is essential for improving its transparency since informative explanations such as generated reviews can help users comprehend the reason for receiving a specified recommendation. The generated reviews should be specific for the given user, item, and rating, however, recent works only focus on designing more and more powerful decoder, merely treating this task as a plain natural language generation process. We argue that there may exist the risk that the powerful decoder neglects the input embeddings and suffers from the biases that exist in data. In this paper, we propose a novel Injective Variational Autoencoders (InVAE) for generating high-quality reviews. Specifically, we employ a Collaborative Kullback-Leibler divergences (CKL) mechanism to building a better latent space that captures meaningful information. Base on this, the Spectral Regularization on Flow-based transformation (SRF) method is designed to backward transfer the priorities of generated latent variables to the input embeddings. Therefore, our method can construct more informative input embeddings and provides more specific explanations for different inputs. Extensive empirical experiments demonstrate that our model can construct much more meaningful feature embeddings and generate personalized reviews in high quality.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129085873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GNES: Learning to Explain Graph Neural Networks GNES:学习解释图神经网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00023

Yuyang Gao, Tong Sun, R. Bhatt, Dazhou Yu, S. Hong, Liang Zhao

{"title":"GNES: Learning to Explain Graph Neural Networks","authors":"Yuyang Gao, Tong Sun, R. Bhatt, Dazhou Yu, S. Hong, Liang Zhao","doi":"10.1109/ICDM51629.2021.00023","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00023","url":null,"abstract":"In recent years, graph neural networks (GNNs) and the research on their explainability are experiencing rapid developments and achieving significant progress. Many methods are proposed to explain the predictions of GNNs, focusing on “how to generate explanations” However, research questions like “whether the GNN explanations are inaccurate”, “what if the explanations are inaccurate”, and “how to adjust the model to generate more accurate explanations” have not been well explored. To address the above questions, this paper proposes a GNN Explanation Supervision (GNES) 1 framework to adaptively learn how to explain GNNs more correctly. Specifically, our framework jointly optimizes both model prediction and model explanation by enforcing both whole graph regularization and weak supervision on model explanations. For the graph regularization, we propose a unified explanation formulation for both node-level and edge-level explanations by enforcing the consistency between them. The node- and edge-level explanation techniques we propose are also generic and rigorously demonstrated to cover several existing major explainers as special cases. Extensive experiments on five real-world datasets across two application domains demonstrate the effectiveness of the proposed model on improving the reasonability of the explanation while still keep or even improve the backbone GNNs model performance.1Code available at: https://github.com/YuyangGao/GNES.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129238774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone 递归神经网络遇到上下文无关语法:一石二鸟

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00125

Hui Guan, Umana Chaudhary, Yuanchao Xu, Lin Ning, Lijun Zhang, Xipeng Shen

{"title":"Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone","authors":"Hui Guan, Umana Chaudhary, Yuanchao Xu, Lin Ning, Lijun Zhang, Xipeng Shen","doi":"10.1109/ICDM51629.2021.00125","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00125","url":null,"abstract":"Recurrent Neural Networks (RNN) are widely used for various prediction tasks on sequences such as text, speed signals, program traces, and system logs. Due to RNNs’ inherently sequential behavior, one key challenge for the effective adoption of RNNs is to reduce the time spent on RNN inference and to increase the scope of a prediction. This work introduces CFG-guided compressed learning, an approach that creatively integrates Context-Free Grammar (CFG) and online tokenization into RNN learning and inference for streaming inputs. Through a hierarchical compression algorithm, it compresses an input sequence to a CFG and makes predictions based on the compressed sequence. Its algorithm design employs a set of techniques to overcome the issues from the myopic nature of online tokenization, the tension between inference accuracy and compression rate, and other complexities. Experiments on 16 real-world sequences of various types validate that the proposed compressed learning can successfully recognize and leverage repetitive patterns in input sequences, and effectively translate them into dramatic (1-1762×) inference speedups as well as much (1-7830×) expanded prediction scope, while keeping the inference accuracy satisfactory.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127866079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Impression Allocation and Policy Search in Display Advertising 展示广告中的印象分配与策略搜索

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00086

Di Wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-chih Lee

{"title":"Impression Allocation and Policy Search in Display Advertising","authors":"Di Wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-chih Lee","doi":"10.1109/ICDM51629.2021.00086","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00086","url":null,"abstract":"In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

CASPITA: Mining Statistically Significant Paths in Time Series Data from an Unknown Network 从未知网络中挖掘时间序列数据的统计显著路径

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1007/s10115-022-01800-7

Andrea Tonon, Fabio Vandin

引用次数: 1

Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction 自适应时空卷积网络交通预测

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00191

Mingyang Zhang, Yong Li, Funing Sun, Diansheng Guo, Pan Hui

引用次数: 3

STING: Self-attention based Time-series Imputation Networks using GAN STING:使用GAN的基于自注意的时间序列Imputation网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00155

Eunkyu Oh, Taehun Kim, Yunhu Ji, Sushil Khyalia

{"title":"STING: Self-attention based Time-series Imputation Networks using GAN","authors":"Eunkyu Oh, Taehun Kim, Yunhu Ji, Sushil Khyalia","doi":"10.1109/ICDM51629.2021.00155","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00155","url":null,"abstract":"Time series data are ubiquitous in real-world applications. However, one of the most common problems is that the time series could have missing values by the inherent nature of the data collection process. So imputing missing values from multivariate (correlated) time series is imperative to improve a prediction performance while making an accurate data-driven decision. Conventional works for imputation simply delete missing values or fill them based on mean/zero. Although recent works based on deep neural networks have shown remarkable results, they still have a limitation to capture the complex generation process of multivariate time series. In this paper, we propose a novel imputation method for multivariate time series, called STING (Self-attention based Time-series Imputation Networks using GAN). We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn the latent representations of time series. In addition, we introduce a novel attention mechanism to capture the weighted correlations of a whole sequence and avoid the potential bias brought by unrelated ones. The experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy as well as downstream tasks with the imputed values therein.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130312076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10