Modeling and exploring high-order connectivity patterns, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of many complex systems. For example, in social networks, triangles have been proven to play the fundamental role in understanding social network communities; in online transaction networks, detecting directed looped transactions

We provide a detailed analysis and a first complete description of 8M—an old but virtually unknown algorithm for Boolean matrix factorization. Even though the algorithm uses a rather limited insight into the factorization problem from today’s perspective, we demonstrate that its performance is reasonably good compared to the currently available algorithms. Our analysis reveals that this is due to certain

Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among

One shirt size cannot fit everybody, while we cannot make a unique shirt that fits perfectly for everyone because of resource limitations. This analogy is true for policy making as well. Policy makers cannot make a single policy to solve all problems for all regions because each region has its own unique issue. At the other extreme, policy makers also cannot make a policy for each small village due

Legal judgment prediction, which aims at predicting judgment results such as penalty, charges, and statutes for cases, has attracted much attention recently. In this article, we focus on building a recommender system to predict the associated statutes for a case given the facts of the case as input. For this purpose, we propose a two-step neural network-based machine learning framework to assist judges

Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis

This article proposes a new approach to sales forecasting for new products (stock-keeping units [SKUs]) with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only takes into account SKU attributes, but also pairwise interactions

In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus

Dimensionality reduction is a commonly used technique in data analytics. Reducing the dimensionality of datasets helps not only with managing their analytical complexity but also with removing redundancy. Over the years, several such algorithms have been proposed with their aims ranging from generating simple linear projections to complex non-linear transformations of the input data. Subsequently,

We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods. These methods have been shown to be biased towards increasing the importance of features with more potential splits. We show that by appropriately incorporating split-improvement as measured on out of sample data, this bias can be corrected yielding better summaries

Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose

Recent studies have shown that there exhibits significantly imbalanced medical resource allocation across public hospitals. Patients, regardless of their diseases, tend to choose hospitals and physicians with a better reputation, which often overloads major hospitals while leaving others underutilized. Guiding patients to hospitals that can serve their treatment needs both timely and with good quality

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this article, we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its

Route planning satisfied multiple requests is an emerging branch in the route planning field and has attracted significant attention from the research community in recent years. The prevailing studies focus only on seeking a route by minimizing a single kind of Travel Cost, such as trip time or distance, among others. In reality, most users would like to choose an appropriate route, neither fastest

Class imbalance treatment methods and cost-sensitive classification algorithms are typically treated as two independent research areas. However, many of these techniques have properties in common. After providing a background to the two fields of research, this article identifies the fundamental mechanism which is common to both. Using this mechanism, a taxonomy is created which encompasses approaches

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this article, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization

With a large number of distance measures, the appropriate choice for clustering a given data set with a specified clustering algorithm becomes an important problem. In this article, an automatic distance measure recommendation method for clustering algorithms is proposed. The recommendation method consists of the following steps: (1) metadata extraction, including meta-feature collection and meta-target

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s location history). In this article, we consider the problem of sanitizing a string by concealing the occurrences

In this article, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed graphlets generalize graphlets to rich heterogeneous networks as they explicitly capture the higher-order typed connectivity patterns in such networks. To address this problem, we describe a general framework for counting

In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types

Indexing biomedical research articles with Medical Subject Headings (MeSH) can greatly facilitate biomedical research and information retrieval. Currently MeSH indexing is performed by human experts. To alleviate the time consumption and monetary cost caused by manual indexing, many automatic MeSH indexing models have been developed, such as MeSHProbeNet, DeepMeSH, and NLM’s official model Medical

Crowdsourcing is a relatively inexpensive and efficient mechanism to collect annotations of data from the open Internet. Crowdsourcing workers are paid for the provided annotations, but the task requester usually has a limited budget. It is desirable to wisely assign the appropriate task to the right workers, so the overall annotation quality is maximized while the cost is reduced. In this article

Internet of Things (IoT) systems usually involve interconnected, low processing capacity, and low memory sensor nodes (devices) that collect data in several sorts of applications that interconnect people and things. In this scenario, mining tasks, such as clustering, have been commonly deployed to detect behavioral patterns from the collected data. The centralized clustering of IoT data demands high

Outlier detection in text data collections has become significant due to the need of finding anomalies in the myriad of text data sources. High feature dimensionality, together with the larger size of these document collections, presents a need for developing accurate outlier detection methods with high efficiency. Traditional outlier detection methods face several challenges including data sparseness

Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within these models. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation

Game bots are automated programs that assist cheating users, leading to an imbalance in the game ecosystem and the collapse of user interest. Online games provide immersive gaming experience and attract many loyal fans. However, game bots have proliferated in volume and method, evolving with the real-world detection methods and showing strong diversity, leaving game bot detection efforts extremely

Influence maximization problem attempts to find a small subset of nodes in a social network that makes the expected influence maximized, which has been researched intensively before. Most of the existing literature focus only on maximizing total influence, but it ignores whether the influential distribution is balanced through the network. Even though the total influence is maximized, but gathered

Human mobility annotation aims to assign mobility records the corresponding visiting Point-of-Interests (POIs). It is one of the most fundamental problems for understanding human mobile behaviors. In literature, many efforts have been devoted to annotating mobility records in a pointwise or trajectory-wise manner. However, the user preference factor is not fully explored and, worse still, the mobile

In outlier detection, recent major research has shifted from developing univariate methods to multivariate methods due to the rapid growth of multidimensional data. However, one typical issue of this paradigm shift is that many multidimensional data often mainly contains univariate outliers, in which many features are actually irrelevant. In such cases, multivariate methods are ineffective in identifying

Burstiness and overdispersion phenomena of count vectors pose significant challenges in modeling such data accurately. While the dependency assumption of the multinomial distribution causes its failure to model frequency vectors in several machine learning and data mining applications, researchers found that by extending the multinomial distribution to the Dirichlet Compound multinomial (DCM), both

The emergence of mining complex networks like social media, sensor networks, and the world-wide-web has attracted considerable research interest. In a streaming scenario, the concept to be learned can change over time. However, while there has been some research done for detecting concept drift in traditional data streams, little work has been done on addressing concept drift in data represented as

This article proposes a novel time-warped sparse non-negative factorization method for functional data analysis. The proposed method on the one hand guarantees the extracted basis functions and their coefficients to be positive and interpretable, and on the other hand is able to handle weakly correlated functions with different features. Furthermore, the method incorporates time warping into factorization

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

In a document, the topic distribution of a sentence depends on both the topics of its neighbored sentences and its own content, and it is usually affected by the topics of the neighbored sentences with different weights. The neighbored sentences of a sentence include the preceding sentences and the subsequent sentences. Meanwhile, it is natural that a document can be treated as a sequence of sentences

Linear discriminant analysis (LDA) is a well-known supervised method for dimensionality reduction in which the global structure of data can be preserved. The classical LDA is sensitive to the noises, and the projection direction of LDA cannot preserve the main energy. This article proposes a novel feature extraction model with l2,1 norm constraint based on LDA, termed as RALDA. This model preserves

Analysts wishing to explore multivariate data spaces, typically issue queries involving selection operators, i.e., range or equality predicates, which define data subspaces of potential interest. Then, they use aggregation functions, the results of which determine a subspace’s interestingness for further exploration and deeper analysis. However, Aggregate Query (AQ) results are scalars and convey limited

Missing value (MV) imputation is a critical preprocessing means for data mining. Nevertheless, existing MV imputation methods are mostly designed for batch processing, and thus are not applicable to streaming data, especially those with poor quality. In this article, we propose a framework, called Real-time and Error-tolerant Missing vAlue ImputatioN (REMAIN), to impute MVs in poor-quality streaming

No abstract available.

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

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

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

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

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

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

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

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

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream

Diversity maximization is a fundamental problem in web search and data mining. For a given dataset S of n elements, the problem requires to determine a subset of S containing k≪n “representatives” which maximize some diversity function expressed in terms of pairwise distances, where distance models dissimilarity. An important variant of the problem prescribes that the solution satisfy an additional

Given a labeled dataset that contains a rare (or minority) class containing of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? The setting is different from traditional classification in that instances from novel minority subclasses might continually emerge over time—and hence is

Recently, a lot of research work has been proposed in different domains to detect outliers and analyze the outlierness of outliers for relational data. However, while sequence data is ubiquitous in real life, analyzing the outlierness for sequence data has not received enough attention. In this article, we study the problem of mining outlying sequence patterns in sequence data addressing the question:

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

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

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

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

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

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

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

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

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.

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