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  • Towards an Optimal Outdoor Advertising Placement: When a Budget Constraint Meets Moving Trajectories
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-07-06
    Ping Zhang; Zhifeng Bao; Yuchen Li; Guoliang Li; Yipeng Zhang; Zhiyong Peng

    In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T, and a budget L, we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards

    更新日期:2020-07-06
  • Multi-User Mobile Sequential Recommendation for Route Optimization
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-07-06
    Keli Xiao; Zeyang Ye; Lihao Zhang; Wenjun Zhou; Yong Ge; Yuefan Deng

    We enhance the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective. The multi-user MSR (MMSR) model searches optimal routes for multiple drivers at different locations while disallowing overlapping routes to be recommended. To enrich the properties of pick-up points in the problem

    更新日期:2020-07-06
  • Learning Distance Metrics from Probabilistic Information
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-07-06
    Mengdi Huai; Chenglin Miao; Yaliang Li; Qiuling Suo; Lu Su; Aidong Zhang

    The goal of metric learning is to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. An implicit assumption in the traditional settings of metric learning is that the associated labels of the instances are deterministic. However, in many real-world applications, the associated labels come naturally with probabilities

    更新日期:2020-07-06
  • Pop Music Generation: From Melody to Multi-style Arrangement
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-07-06
    Hongyuan Zhu; Qi Liu; Nicholas Jing Yuan; Kun Zhang; Guang Zhou; Enhong Chen

    Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music

    更新日期:2020-07-06
  • Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-19
    Dominik Mautz; Wei Ye; Claudia Plant; Christian Böhm

    A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the dataset. The new research field of non-redundant clustering addresses this class of problems. In this article, we follow the approach that different, non-redundant k-means-like

    更新日期:2020-07-06
  • MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-19
    Matteo Riondato; Fabio Vandin

    We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new formulation of these measures as functions of averages, that makes it possible to approximate them using sampling. We then discuss how pseudodimension, a key

    更新日期:2020-07-06
  • Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-19
    Daniel Zügner; Oliver Borchert; Amir Akbarnejad; Stephan Günnemann

    Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we present a study of adversarial attacks on attributed

    更新日期:2020-07-06
  • Efficient Approaches to k Representative G-Skyline Queries
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-07-06
    Xu Zhou; Kenli Li; Zhibang Yang; Yunjun Gao; Keqin Li

    The G-Skyline (GSky) query is a powerful tool to analyze optimal groups in decision support. Compared with other group skyline queries, it releases users from providing an aggregate function. Besides, it can get much comprehensive results without overlooking some important results containing non-skylines. However, it is hard for the users to make sensible choices when facing so many results the GSky

    更新日期:2020-07-06
  • Learning Bayesian Networks with the Saiyan Algorithm
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-22
    Anthony C. Constantinou

    Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesian Network graphs from synthetic data. However, in their mission to maximise a scoring function, many become conservative and minimise edges discovered. While simplicity is desired, the output is often a graph that consists of multiple independent subgraphs that do not enable full propagation of evidence

    更新日期:2020-06-30
  • Sparse Graph Connectivity for Image Segmentation
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-15
    Xiaofeng Zhu; Shichao Zhang; Jilian Zhang; Yonggang Li; Guangquan Lu; Yang Yang

    It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point (e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of

    更新日期:2020-06-30
  • Internal Evaluation of Unsupervised Outlier Detection
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-26
    Henrique O. Marques; Ricardo J. G. B. Campello; Jürg Sander; Arthur Zimek

    Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, based solely on the data and the assessed solutions themselves, is required if one wants to statistically validate (in absolute terms) or just compare (in

    更新日期:2020-06-30
  • Self-weighted Multi-view Fuzzy Clustering
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-22
    Xiaofeng Zhu; Shichao Zhang; Yonghua Zhu; Wei Zheng; Yang Yang

    Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering

    更新日期:2020-06-30
  • Discovering Anomalies by Incorporating Feedback from an Expert
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-22
    Shubhomoy Das; Weng-Keen Wong; Thomas Dietterich; Alan Fern; Andrew Emmott

    Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false-positive and high false-negative rates. One main cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this article, we describe the Active Anomaly Discovery

    更新日期:2020-06-30
  • Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-06-15
    Yuanbo Xu; Yongjian Yang; En Wang; Jiayu Han; Fuzhen Zhuang; Zhiwen Yu; Hui Xiong

    Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation

    更新日期:2020-06-30
  • Incomplete Network Alignment
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Si Zhang; Hanghang Tong; Jie Tang; Jiejun Xu; Wei Fan

    Networks are prevalent in many areas and are often collected from multiple sources. However, due to the veracity characteristics, more often than not, networks are incomplete. Network alignment and network completion have become two fundamental cornerstones behind a wealth of high-impact graph mining applications. The state-of-the-art have been addressing these two tasks in parallel. That is, most

    更新日期:2020-05-30
  • Fully Dynamic Approximate k-Core Decomposition in Hypergraphs
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Bintao Sun; T.-H. Hubert Chan; Mauro Sozio

    In this article, we design algorithms to maintain approximate core values in dynamic hypergraphs. This notion has been well studied for normal graphs in both static and dynamic setting. We generalize the problem to hypergraphs when edges can be inserted or deleted by an adversary.

    更新日期:2020-05-30
  • Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Thirunavukarasu Balasubramaniam; Richi Nayak; Chau Yuen

    With the advancements in computing technology and web-based applications, data are increasingly generated in multi-dimensional form. These data are usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process

    更新日期:2020-05-30
  • The Gene of Scientific Success
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Xiangjie Kong; Jun Zhang; Da Zhang; Yi Bu; Ying Ding; Feng Xia

    This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement

    更新日期:2020-05-30
  • Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Cen Chen; Kenli Li; Sin G. Teo; Xiaofeng Zou; Keqin Li; Zeng Zeng

    Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the

    更新日期:2020-05-30
  • Social Collaborative Mutual Learning for Item Recommendation
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Tianyu Zhu; Guannan Liu; Guoqing Chen

    Recommender Systems (RSs) provide users with item choices based on their preferences reflected in past interactions and become important tools to alleviate the information overload problem for users. However, in real-world scenarios, the user–item interaction matrix is generally sparse, leading to the poor performance of recommendation methods. To cope with this problem, social information is introduced

    更新日期:2020-05-30
  • Edge2vec
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-05-30
    Changping Wang; Chaokun Wang; Zheng Wang; Xiaojun Ye; Philip S. Yu

    Graph embedding, also known as network embedding and network representation learning, is a useful technique which helps researchers analyze information networks through embedding a network into a low-dimensional space. However, existing graph embedding methods are all node-based, which means they can just directly map the nodes of a network to low-dimensional vectors while the edges could only be mapped

    更新日期:2020-05-30
  • From Community to Role-based Graph Embeddings
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-04-25
    Ryan A. Rossi; Di Jin; Sungchul Kim; Nesreen Ahmed; Danai Koutra; John Boaz Lee

    Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities are sets of nodes with more connections inside the set than outside (based on proximity/closeness, density). Roles and communities are fundamentally different but important complementary notions. Recently, the notion of structural roles has become increasingly important

    更新日期:2020-04-25
  • Scalable Spatial Scan Statistics for Trajectories
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2020-04-08
    Michael Matheny; Dong Xie; Jeff M Phillips

    We define several new models for how to define anomalous regions among enormous sets of trajectories. These are based on spatial scan statistics, and identify a geometric region which captures a subset of trajectories which are significantly different in a measured characteristic from the background population. The model definition depends on how much a geometric region is contributed to by some overlapping

    更新日期:2020-04-08
  • Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2017-12-06
    Chen Chen,Hanghang Tong,Lei Xie,Lei Ying,Qing He

    The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered

    更新日期:2019-11-01
  • CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2016-07-01
    Wei Cheng,Zhishan Guo,Xiang Zhang,Wei Wang

    Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume

    更新日期:2019-11-01
  • Scalable and Axiomatic Ranking of Network Role Similarity.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2014-11-11
    Ruoming Jin,Victor E Lee,Longjie Li

    A key task in analyzing social networks and other complex networks is role analysis: describing and categorizing nodes according to how they interact with other nodes. Two nodes have the same role if they interact with equivalent sets of neighbors. The most fundamental role equivalence is automorphic equivalence. Unfortunately, the fastest algorithms known for graph automorphism are nonpolynomial.

    更新日期:2019-11-01
  • Social Trust Prediction Using Heterogeneous Networks.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2014-04-15
    Jin Huang,Feiping Nie,Heng Huang,Yi-Cheng Tu,Yu Lei

    Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic

    更新日期:2019-11-01
  • Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2013-09-01
    Thanawin Rakthanmanon,Bilson Campana,Abdullah Mueen,Gustavo Batista,Brandon Westover,Qiang Zhu,Jesin Zakaria,Eamonn Keogh

    Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms, including classification, clustering, motif discovery, anomaly detection, and so on. The difficulty of scaling a search to large datasets explains to a great extent why most academic work on time series

    更新日期:2019-11-01
  • Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2012-02-01
    Jianhui Chen,Ji Liu,Jieping Ye

    We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via

    更新日期:2019-11-01
  • Motif Discovery in Physiological Datasets: A Methodology for Inferring Predictive Elements.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2010-08-24
    Zeeshan Syed,Collin Stultz,Manolis Kellis,Piotr Indyk,John Guttag

    In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to efficiently search for such patterns in large datasets. This involves first transforming continuous physiological

    更新日期:2019-11-01
  • Author Name Disambiguation in MEDLINE.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2010-01-15
    Vetle I Torvik,Neil R Smalheiser

    BACKGROUND: We recently described "Author-ity," a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis

    更新日期:2019-11-01
  • Developmental Stage Annotation of Drosophila Gene Expression Pattern Images via an Entire Solution Path for LDA.
    ACM Trans. Knowl. Discov. Data (IF 2.01) Pub Date : 2008-09-05
    Jieping Ye,Jianhui Chen,Ravi Janardan,Sudhir Kumar

    Gene expression in a developing embryo occurs in particular cells (spatial patterns) in a time-specific manner (temporal patterns), which leads to the differentiation of cell fates. Images of a Drosophila melanogaster embryo at a given developmental stage, showing a particular gene expression pattern revealed by a gene-specific probe, can be compared for spatial overlaps. The comparison is fundamentally

    更新日期:2019-11-01
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