Latest Articles

## Mining User Development Signals for Online Community Churner Detection

Churners are users who stop using a given service after previously signing up. In the domain of telecommunications and video games, churners represent... (more)

## Put Three and Three Together

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its applications in many fields such as biology, social networks, or network traffic analysis. Although the existing metrics used to quantify the quality of a community work well in general, under some circumstances, they fail at correctly... (more)

## Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

Mining knowledge from a multimedia database has received increasing attentions recently since huge repositories are made available by the development... (more)

## Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes

Anesthesiologists are taught to carefully manage patient vital signs during surgery. Unfortunately,... (more)

## Collective Graph Identification

Data describing networks—such as communication networks, transaction networks, disease transmission networks, collaboration networks, etc.—are becoming increasingly available. While observational data can be useful, it often only hints at the actual underlying process that governs interactions and attributes. For example, an email... (more)

## Mining Influencers Using Information Flows in Social Streams

The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in... (more)

## Toward Generalizing the Unification with Statistical Outliers

In this work, we introduce a novel definition of outlier, namely the Gradient Outlier Factor (or GOF), with the aim to provide a definition that... (more)

## DeltaCon

How much has a network changed since yesterday? How different is the wiring of Bob’s brain (a left-handed male) and Alice’s brain (a right-handed female), and how is it different? Graph similarity with given node correspondence, i.e., the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we... (more)

## Mining Product Adopter Information from Online Reviews for Improving Product Recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted... (more)

## Adaptive Model Rules From High-Speed Data Streams

Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules... (more)

## Synchronization-Core-Based Discovery of Processes with Decomposable Cyclic Dependencies

Traditional process discovery techniques mine process models based upon event traces giving little consideration to workflow relevant data recorded in... (more)

## An Efficient Algorithm For Weak Hierarchical Lasso

Linear regression is a widely used tool in data mining and machine learning. In many applications, fitting a regression model with only linear effects... (more)

### New options for ACM authors to manage rights and permissions for their work

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new author-pays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

The ACM Transactions on Knowledge Discovery from Data (TKDD) publishes original archival papers in the area of knowledge discovery from data and closely related disciplines.  The majority of the papers that appear in TKDD is expected to address the logical and technical foundation of knowledge discovery and data mining.

##### Forthcoming Articles
Co-clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. The large amount of data associated with process variables monitored over time form a rich reservoir of information, which can be used for a variety of purposes, such as anomaly detection, quality control and fault diagnostics. In particular, following the same recipe for a certain IC device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this paper, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. Furthermore, we establish the equivalence between C-Struts and a generic optimization problem, which is able to accommodate various distance functions. Extensive experiments on synthetic, benchmark, as well as manufacturing data sets demonstrate the effectiveness of the proposed method.

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-blog

Micro-blog has been increasingly used for the public to express their opinions, and for organisations to detect public sentiment about social events or public policies. In this paper we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, multi-media elements and hierarchical structure of Chinese Weibo. Based on the analysis we propose a novel approach and develop associated theoretical and technological methods to address these problems. These include a new sentiment word mining method based on three wording metrics and point-wise information, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements as auxiliary affective factors. We evaluate our new word discovery and sentiment detection methods on a real-life Chinese microblog dataset. Initial results show that our new diction can improve sentiment detection, and demonstrate that our multilevel rule set method is more effective with average accuracy being 10.2% and 1.5% higher than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualisation techniques to study the relationships between online sentiment and real life. The visualisation of detected sentiment can help depict temporal patterns and spatial discrepancy.

Shop Type Recommendation Leveraging the Data from Social Media and Location-based Services

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion, etc.) for a newly opened store. Traditional ways are predominantly field surveys and empirical estimation, which are not effective as they lack shop-related data. As social media and location-based services (LBS) are becoming more and more pervasive, user-generated data from these platforms is providing rich information not only about individual consumption experiences but also about shop attributes. In this paper, we investigate the recommendation of shop types for a given location, by leveraging heterogeneous data that are mainly historical user preferences and location context from social media and LBS. Our goal is to select the most suitable shop type, seeking to maximize the number of customers served from a candidate set of types. We propose a novel bias learning matrix factorization method with feature fusion for shop popularity prediction. Features are defined and extracted from two perspectives: location, where features are closely related to location characteristics, and commercial, where features are about the relationships between shops in the neighborhood. Experimental results show that the proposed method outperforms state-of-the-art solutions.

Jointly Modeling Label and Feature Heterogeneity in Medical Informatics

Multiple types of heterogeneity, such as label heterogeneity and feature heterogeneity, often co-exist in many real-world data mining applications, such as news article categorization, gene functionality prediction. To effectively leverage such heterogeneity, in this paper, we propose a novel graph-based model for Learning with both Label and Feature heterogeneities, namely $L^2F$. It models the label correlation by requiring that any two label-specific classifiers behave similarly on the same views if the associated labels are similar, and imposes the view consistency by requiring that view-based classifiers generate similar predictions on the same examples. To solve the resulting optimization problem, we propose an iterative algorithm, which is guaranteed to converge to the global optimum. One appealing feature of $L^2F$ is that it is capable of handling data with missing views and labels. Furthermore, we analyze its generalization performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling the label and feature heterogeneity. Experimental results on various biomedical data sets show the effectiveness of the proposed approach.

Mining Dual Networks: Models, Algorithms and Applications

Finding the densest subgraph in a single graph is a fundamental problem that has been extensively studied. In many emerging applications, there exist dual networks. For example, in genetics, it is important to use protein interactions to interpret genetic interactions. In this application, one network represents physical interactions among nodes, e.g., protein-protein interactions, and another network represents conceptual interactions, e.g., genetic interactions. Edges in the conceptual network are usually derived based on certain correlation measure or statistical test measuring the strength of the interaction. Two nodes with strong conceptual interaction may not have direct physical interaction. In this paper, we propose the novel dual network model and investigate the problem of finding the densest connected subgraph (DCS) which has the largest density in the conceptual network and is also connected in the physical network. Density in the conceptual network represents the average strength of the measured interacting signals among the set of nodes. Connectivity in the physical network shows how they interact physically. Such pattern cannot be identified using the existing algorithms for a single network. We show that even though finding the densest subgraph in a single network is polynomial time solvable, the DCS problem is NP-hard. We develop a two-step approach to solve the DCS problem. In the first step, we effectively prune the dual networks while guarantee that the optimal solution is contained in the remaining networks. For the second step, we develop two efficient greedy methods based on different search strategies to find the DCS. Different variations of the DCS problem are also studied. We perform extensive experiments on a variety of real and synthetic dual networks to evaluate the effectiveness and efficiency of the developed methods.

Listwise Learning to Rank from Crowds

Learning to rank has received great attention in recent years as it plays a crucial role in many applications such as information retrieval, data mining. The existing concept of learning to rank assumes that each training instance is associated with a reliable label. However, in practice, this assumption does not necessarily hold true as it may be infeasible or remarkably expensive to obtain reliable labels for many learning to rank applications. Therefore, a feasible approach is to collect labels from crowds and then learn a ranking function from crowdsourcing labels. This study explores the listwise learning to rank with crowdsourcing labels obtained from multiple annotators, who may be unreliable. A new probabilistic ranking model is first proposed by combining two existing models. Subsequently, a ranking function is trained by proposing a maximum likelihood learning approach, which estimates ground-truth labels and annotator expertise, and learns the ranking function iteratively. In practical crowdsourcing machine learning, valuable prior information (e.g., professional grades) about the annotators is normaly attainable. Therefore, this study also investigates learning to rank from crowd labels when prior information on the exptertise of involved annotators is avaliable. In particular, three basic types of prior information are investigated, and corresponding learning algorithms are consequently introduced. The proposed algorithms are tested on both synthetic and real-world data. Results reveal that the maximum likelihood approach significantly outperforms the average approach, and its results are comparable to those of the learning model in consideration reliable labels. The results of the investigation further indicate that prior information is helpful in inferring both ranking functions and expertise degrees of annotators.

Heterogeneous Translated Hashing: A Scalable Solution towards Multi-modal Similarity Search

Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions embedding heterogeneous media into different Hamming spaces, and a translator aligning different Hamming spaces. The extensive experiments on two real-world datasets, one publicly available dataset of Flickr and the other MIRFLICKR-Yahoo Answers dataset, highlight the effectiveness and efficiency of our algorithm.

#### Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees.

Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis using High-Dimensional Molecular Profiling Data

With the advancement of genome-wide monitoring technologies, molecular expression data have become widely used for diagnosing cancer through tumor or blood samples. When mining molecular signature data, the process of comparing samples through an adaptive distance function is fundamental but difficult, as such data sets are normally heterogeneous and high dimensional. In this paper, we present kernelized information-theoretic metric learning (KITML) algorithms that optimize a distance function to tackle the cancer diagnosis problem and scale to high dimensionality. By learning a nonlinear transformation in the input space implicitly through kernelization, KITML permits efficient optimization, low storage, and improved learning of distance metric. We propose two novel applications of KITML for diagnosing cancer using high-dimensional molecular profiling data. (1) For sample-level cancer diagnosis, the learned metric is used to improve the performance of $k$-nearest neighbor classification. (2) For estimating the severity level or stage of a group of samples, we propose a novel set-based ranking approach to extend KITML. For the sample-level cancer classification task, we have evaluated on fourteen cancer gene microarray data sets and compared with six other state-of-the-art approaches. The results show that our approach achieves the best overall performance for the task of molecular expression driven cancer sample diagnosis. For the group-level cancer stage estimation, we test the proposed set-KITML approach using three multi-stage cancer microarray data sets, and correctly estimated the stages of sample groups for all three studies.

Leveraging Neighbor Attributes for Classification in Sparsely-Labeled Networks

Many analysis tasks involve linked nodes, such as people connected by friendship links. Research on "link-based classification" (LBC) has studied how to leverage these connections to improve classification accuracy. Most such prior research has assumed the provision of a densely-labeled training network. Instead, this article studies the common and challenging case when LBC must use a single sparsely-labeled network for both learning and inference, a case where existing methods often yield poor accuracy. To address this challenge, we introduce a novel method that enables prediction via "neighbor attributes," which were briefly considered by early LBC work but then abandoned due to perceived problems. We then explain, using both extensive experiments and loss decomposition analysis, how using neighbor attributes often significantly improves accuracy. We further show that using appropriate semi-supervised learning (SSL) is essential to obtaining the best accuracy in this domain, and that the gains of neighbor attributes remain across a range of SSL choices and data conditions. Finally, given the challenges of label sparsity for LBC and the impact of neighbor attributes, we show that multiple previous studies must be re-considered, including studies regarding the best model features, the impact of noisy attributes, and strategies for active learning.

Product Selection Problem: Improve Market Share by Learning Consumer Behavior

It is often crucial for manufacturers to decide what products to produce so that they can increase their market share in an increasingly fierce market. To decide which products to produce, manufacturers need to analyze the consumers' requirements and how consumers make their purchase decisions so that the new products will be competitive in the market. In this paper, we first present a general distance-based product adoption model to capture consumers' purchase behavior. Using this model, various distance metrics can be used to describe different real life purchase behavior. We then provide a learning algorithm to decide which set of distance metrics one should use when we are given some accessible historical purchase data. Based on the product adoption model, we formalize the {\em \mbox{$k$ most} marketable products (or $k$-$\MMP$)} selection problem and formally prove that the problem is {\em NP-hard}. To tackle this problem, we propose an efficient greedy-based approximation algorithm with a provable solution guarantee. Using submodularity analysis, we prove that our approximation algorithm can achieve at least 63\% of the optimal solution. We apply our algorithm on both synthetic datasets and real-world datasets (TripAdvisor.com), and show that our algorithm can easily achieve five or more orders of speedup over the exhaustive search and achieve about 96\% of the optimal solution on average. Our experiments also demonstrate the robustness of our distance metric learning method, and illustrate how one can adopt it to improve the accuracy of product selection.

Unsupervised Rare Pattern Mining: A Survey

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare pattern in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art method for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution we give a general framework for algorithms categorized under-various categories: Apriori and Tree based. We highlight the differences between these methods. Finally we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.

Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach

Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns show up in many applications, from Twitter users who buy fake followers, manipulating the social network, to botnet members performing distributed denial of service attacks, disturbing the network traffic graph. We propose a fast and effective method, CATCHSYNC, which exploits two of the tell-tale signs left in graphs by fraudsters: (a) synchronized behavior: suspicious nodes have extremely similar behavior pattern, because they are often required to perform some task together (such as follow the same user); and (b) rare behavior: their connectivity patterns are very different from the majority. We introduce novel measures to quantify both concepts (synchronicity and normality) and we propose a parameter-free algorithm that works on the resulting synchronicity-normality plots. Thanks to careful design, CATCHSYNC has the following desirable properties: (a) it is scalable to large datasets, being linear on the graph size; (b) it is parameter free; and (c) it is side-information-oblivious: it can operate using only the topology, without needing labeled data, nor timing information, etc., while still capable of using side information, if available. We applied CATCHSYNC on three large, real datasets 1-billion-edge Twitter social graph, 3-billion-edge and 12-billion-edge Tencent Weibo social graphs, and several synthetic ones; CATCHSYNC consistently outperforms existing competitors, both in detection accuracy by 36% on Twitter and 20% on Tencent Weibo, as well as in speed.

#### Parallel Field Ranking

Inferring Dynamic Diffusion Networks in Online Media

Online media play an important role in information societies by providing a convenient infrastructure for different processes. Information diffusion that is a fundamental process taking place on social and information networks has been investigated in many studies. Research on information diffusion in these networks faces two main challenges: 1) In most cases diffusion takes place on an underlying network which is latent and its structure is unknown. 2) This latent network is not fixed and changes over time. In this paper, we investigate the diffusion network extraction problem when the underlying network is dynamic and latent. We model the diffusion behavior (existence probability) of each edge as a stochastic process and utilize the Hidden Markov Model to discover the most probable diffusion links according to the current observation of the diffusion process, which is the infection time of nodes and the past diffusion behavior of links. We evaluate the performance of our Dynamic Diffusion Network Extraction (DDNE) method, on both synthetic and real datasets. Experimental results show that the performance of the proposed method is independent of the cascade transmission model and outperforms the state of art method in terms of F-measure.

#### Guest Editorial: Special Issue on Connected Health at Big Data Era (BigChat)

Biomedical Ontology Quality Assurance Using a Big Data Approach

This paper presents recent progress made in using scalable cloud computing environment, Hadoop and MapReduce, to perform ontology quality assurance (OQA), and points to areas of future opportunity. The standard sequential approach used for implementing OQA methods can take weeks if not months for exhaustive analyses for large biomedical ontological systems. With OQA methods newly implemented using massively parallel algorithms in the MapReduce framework, several orders of magnitude in speed-up can be achieved (e.g. from three months to three hours). Such dramatically reduced time makes it feasible not only to perform exhaustive structural analysis of large ontological hierarchies, but also to systematically track structural changes between versions for evolutional analysis. As an exemplar, progress is reported in using MapReduce to perform evolutional analysis and visualization on the Systemized Nomenclature of Medicine - Clinical Terms (SNOMED CT), a prominent clinical terminology system. Future opportunities in three areas are described: one is to extend the scope of MapReduce-based approach to existing OQA methods, especially for automated exhaustive structural analysis. The second is to apply our proposed MapReduce Pipeline for Lattice-based Evaluation (MaPLE) pipeline, demonstrated as an exemplar method for SNOMED CT, to other biomedical ontologies. The third area is to develop interfaces for reviewing results obtained by OQA methods and for visualizing ontological alignment and evolution, which can also take advantage of cloud computing technology to systematically pre-compute computationally intensive jobs in order to increase performance during user interaction with the visualization interface. Progress in these directions are expected to better support the ontological engineering lifecycle.

CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering

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 that different views are available for the same set of instances. Thus instances in different domains can be treated as having strict one-to-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge. In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relationship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. We develop an efficient optimization method that guarantees to find the global optimal solution with a given confidence requirement. The proposed method can automatically identify noisy domains and assign smaller weights to them. This helps to obtain optimal graph partition for the focused domain. Extensive experimental results on UCI benchmark data sets, newsgroup data sets and biological interaction networks demonstrate the effectiveness of our approach.

Spatial-Proximity Optimization for Rapid Task Group Deployment

Spatial proximity is one of the most important factors for the quick deployment of the task groups in various time-sensitive missions. This paper proposes a new spatial query, Spatio-Social Team Query (SSTQ), that forms a strong task group by considering 1) the groups spatial distance (i.e., transportation time), 2) skills of the candidate group members, and 3) social rapport among the candidates. Efficient processing of SSTQ is very challenging, because the aforementioned spatial, skill, and social factors need to be carefully examined. In this paper, therefore, we first formulate two subproblems of SSTQ, namely Hop-Constrained Team Problem (HCTP) and Connection-Oriented Team Query (COTQ). HCTP is a decision problem that considers only social and skill dimensions. We prove that HCTP is NP-Complete. Moreover, based on the hardness of HCTP, we prove that SSTQ is NP-Hard and inapproximable within any factor. On the other hand, COTQ is a special case of SSTQ that relaxes the social constraint. We prove that COTQ is NP-Hard and propose an approximation algorithm for COTQ, COTprox, that achieves the best approximation ratio. Furthermore, based on the observations on COTprox, we devise an approximation algorithm, SSTprox, with a guaranteed error bound for SSTQ. Finally, to efficiently obtain the optimal solution to SSTQ for small instances, we design two efficient algorithms, SpatialFirst and SkillFirst, with different scenarios in mind. These two algorithms incorporate various effective ordering and pruning techniques to reduce the search space for answering SSTQ. Experimental results on real datasets indicate that the proposed algorithms can efficiently answer SSTQ under various parameter settings.

#### Batch Mode Active Sampling based on Marginal Probability Distribution Matching

Convex Sparse PCA for Unsupervised Feature Analysis

Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology and social science. Classical PCA and its variants seek for linear projections of the original variables to obtain a low dimensional feature representation with maximal variance. One limitation is that it is very difficult to interpret the results of PCA. In addition, the classical PCA is vulnerable to certain noisy data. In this paper, we propose a convex sparse principal component analysis (CSPCA) algorithm and apply it to feature analysis. First we show that PCA can be formulated as a low-rank regression optimization problem. Based on the discussion, the $l_{2,1}$-norm minimization is incorporated into the objective function to make the regression coefficients sparse, thereby robust to the outliers. In addition, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability. With the output of our CSPCA, we can effectively analyze the importance of each feature under the PCA criteria. The objective function is convex, and we propose an iterative algorithm to optimize it. We apply the CSPCA algorithm to feature selection and conduct extensive experiments on six different benchmark datasets. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art unsupervised feature selection algorithms.

#### Differentially-Private Multidimensional Data Publishing

Less is More: Building Selective Anomaly Ensembles

Ensemble learning for anomaly detection have been barely studied due to lack of ground truth and inherent objective functions, although ensemble approaches for classification and clustering have been effectively used for long. In this work, we propose a new selective ensemble approach for anomaly detection, and demonstrate its performance on event detection in temporal graphs. Our method combines the results from multiple heterogeneous detectors to yield better performance. Importantly, trusting results from all the constituent detectors may deteriorate the overall performance of the ensemble, as some detectors may provide inaccurate results depending on the type of data in hand. This suggests that selectively combining results is key to building effective anomaly ensemblesas such, we find that less is more. In this paper we propose a novel ensemble approach SELECT for anomaly detection, which automatically and systematically selects the results from constituent detectors to combine in a fully unsupervised fashion. We apply our method to event detection in temporal graphs, where SELECT successfully utilizes five base detectors and seven consensus methods under a unified ensemble framework. We provide extensive quantitative evaluation of our approach on five real-world datasets (four with ground truth events), including Enron email communications, RealityMining SMS and phone call records, New York Times news corpus, and World Cup 2014 Twitter news feed. Thanks to its selection mechanism, SELECT yields superior performance compared to the individual detectors alone, the full ensemble (naively combining all results), an existing diversity-based ensemble, and an existing weighted ensemble approach.

### Bibliometrics

First Name Last Name Award
John Canny ACM Doctoral Dissertation Award (1987)
Carlos A. Castillo ACM Senior Member (2014)
Chris Clifton ACM Senior Member (2006)
Graham R. Cormode ACM Distinguished Member (2013)
Benjamin Fung ACM Senior Member (2013)
John E Hopcroft ACM Karl V. Karlstrom Outstanding Educator Award (2008)
A. M. Turing Award (1986)
Piotr Indyk ACM Paris Kanellakis Theory and Practice Award (2012)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM-Infosys Foundation Award in the Computing Sciences (2008)
Chih-Jen Lin ACM Distinguished Member (2011)
ACM Senior Member (2010)
Sethuraman Panchanathan ACM Senior Member (2009)
Jian Pei ACM Senior Member (2007)
Domenico Sacca ACM Senior Member (2007)
Qiang Yang ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Christos Faloutsos 10
Jieping Ye 7
Tao Li 5
Philip Yu 4
Jian Pei 4
Shenghuo Zhu 4
Aristides Gionis 4
Huan Liu 4
John Hopcroft 3
Jure Leskovec 3
Lise Getoor 3
Hui Xiong 3
Malik Magdon-Ismail 3
Zhihua Zhou 3
Jilles Vreeken 3
John Lui 3
Lei Tang 3
Heng Huang 3
Yun Chi 3
Yasushi Sakurai 3
Evimaria Terzi 3
Yihong Gong 3
Hong Cheng 3
Feiping Nie 3
Dingding Wang 3
Fabio Fassetti 3
Christopher Jermaine 3
Fabrizio Angiulli 3
Antonella Guzzo 2
Jilei Tian 2
Shinjae Yoo 2
Andrea Esuli 2
Ping Luo 2
Jiawei Han 2
Guofei Jiang 2
Ian Davidson 2
Yuru Lin 2
Daniel Kifer 2
Antônio Loureiro 2
Sucheta Soundarajan 2
Don Towsley 2
Laks Lakshmanan 2
Jimeng Sun 2
Enhong CHEN 2
Qi Liu 2
Jie Tang 2
Belle Tseng 2
Mingsyan Chen 2
Xiaoli Fern 2
Vivekanand Gopalkrishnan 2
Charalampos Tsourakakis 2
Dantong Yu 2
Carlotta Domeniconi 2
Sanjay Ranka 2
Eugene Agichtein 2
Jiliang Tang 2
Srinivasan Parthasarathy 2
Hari Sundaram 2
Joydeep Ghosh 2
Dino Pedreschi 2
Hao Huang 2
Hong Qin 2
Pinghui Wang 2
Panayiotis Tsaparas 2
Yehuda Koren 2
Heikki Mannila 2
Fabrizio Sebastiani 2
Xiao Yu 2
Junzhou Zhao 2
Xiaohong Guan 2
Yu Zhang 2
Arthur Zimek 2
Indrajit Bhattacharya 2
Michalis Vazirgiannis 2
Jin Huang 2
Geoffrey Webb 2
Jianhui Chen 2
Panagis Magdalinos 2
Charles Ling 1
Mengling Feng 1
Lei Zou 1
Luming Zhang 1
Jian Wang 1
Manos Papagelis 1
Ruud Van De Bovenkamp 1
Clyde Giles 1
Wei Peng 1
B Prakash 1
Quanquan Gu 1
Luan Tang 1
Xintao Wu 1
Xiaowen Ding 1
John Hutchins 1
Taneli Mielikäinen 1
Ruoming Jin 1
Victor Lee 1
Robert Kleinberg 1
Zhi Yang 1
Yafei Dai 1
Jörg Sander 1
Siyuan Liu 1
Yizhou Sun 1
Xiaofei He 1
Sethuraman Panchanathan 1
Abdullah Mueen 1
Muthuramakrishnan Venkitasubramaniam 1
Maria Halkidi 1
David Gleich 1
Steven Hoi 1
David Jensen 1
Glenn Fung 1
Manuel Gomez-Rodriguez 1
Ji Liu 1
Zeeshan Syed 1
Kamalakar Karlapalem 1
Dale Schuurmans 1
Jean Boulicaut 1
Dimitrios Mavroeidis 1
Peer Kröger 1
Céline Robardet 1
Zengjian Hu 1
Boaz Ben-Moshe 1
Sanjay Chawla 1
Saurav Sahay 1
Neil Smalheiser 1
James Cheng 1
Shachar Kaufman 1
Ori Stitelman 1
Nikolaj Tatti 1
Leland Wilkinson 1
Hockhee Ang 1
Steven Hoi 1
Weekeong Ng 1
Xiao Jiang 1
Lyle Ungar 1
Franco Turini 1
José Balcázar 1
Nick Duffield 1
Chun Li 1
Jianyong Wang 1
Feitony Liu 1
Petros Drineas 1
Tengfei Bao 1
Brook Wu 1
Jinpeng Wang 1
Arnau Prat-Pérez 1
Josep Larriba-Pey 1
Risa Myers 1
Qingtian Zeng 1
Brian Gallagher 1
Dan Simovici 1
Hao Wang 1
Siddharth Gopal 1
Alice Leung 1
Renato Assunção 1
Stephen North 1
Zhiwen Yu 1
Sitaram Asur 1
Jerry Kiernan 1
Kevin Yip 1
Wei Zheng 1
Brandon Norick 1
Jiawei Han 1
Ming Ji 1
Zhenxing Wang 1
Zheng Wang 1
Thanawin Rakthanmanon 1
Jesin Zakaria 1
Kedar Bellare 1
Yuval Elovici 1
Ming Lin 1
Changshui Zhang 1
Ravi Konuru 1
Fan Guo 1
Edward Wild 1
Murat Kantarcıoğlu 1
Chihjen Lin 1
Seungil Huh 1
Chojui Hsieh 1
John Guttag 1
Marc Plantevit 1
Jinlin Chen 1
Alin Dobra 1
Shantanu Godbole 1
Binay Bhattacharya 1
Ana Appel 1
Jeffreyxu Yu 1
Ying Jin 1
Hiroshi Mamitsuka 1
Andrew Mehler 1
Bin Zhou 1
Anushka Anand 1
Yicheng Tu 1
Subhabrata Sen 1
Dino Ienco 1
Rosa Meo 1
Eduardo Hruschka 1
Hongliang Fei 1
Jun Huan 1
Pauli Miettinen 1
Baoxing Huai 1
Hengshu Zhu 1
Pritam Gundecha 1
Zhen Guo 1
Yashu Liu 1
Lei Chen 1
Waynexin Zhao 1
Faming Lu 1
Haojun Zhang 1
Limsoon Wong 1
Liang Hong 1
Hunghsuan Chen 1
Venu Satuluri 1
Yan Liu 1
Rose Yu 1
Yao Zhang 1
Zhiting Hu 1
Pedro Melo 1
Yuan Jiang 1
Zhanpeng Fang 1
Jing Peng 1
Julian McAuley 1
Yang Zhou 1
Xinjiang Lu 1
Dengyong Zhou 1
Ming Zhang 1
Biru Dai 1
Xifeng Yan 1
Qi Lou 1
Wei Fan 1
Divesh Srivastava 1
Hungleng Chen 1
Zhenjie Zhang 1
Aisling Kelliher 1
Paul Castro 1
Anon Plangprasopchok 1
Shengrui Wang 1
Patrick Hung 1
A Patterson 1
Carlos Guestrin 1
Tomoharu Iwata 1
Naonori Ueda 1
Ganesh Ramesh 1
Manolis Kellis 1
Carlos Castillo 1
Tianbing Xu 1
Sanmay Das 1
Amit Dhurandhar 1
Beechung Chen 1
Elizabeth Chang 1
Aminul Islam 1
Charles Elkan 1
Li Wan 1
Weekeong Ng 1
Sougata Mukherjea 1
Ashwin Ram 1
Sethuraman Panchanathan 1
Michael Mampaey 1
Yu Lei 1
Shipeng Yu 1
Maria Sapino 1
Matteo Riondato 1
Qinbao Song 1
Michele Coscia 1
Yi Wang 1
Lian Duan 1
Bruno Ribeiro 1
Siyuan Liu 1
Jaideep Srivastava 1
João Gama 1
Luigi Pontieri 1
Bingrong Lin 1
Francesco Bonchi 1
Wei Ding 1
Lei Zhang 1
Thomas Porta 1
Hongzhi Yin 1
Jie Tang 1
Haiqin Yang 1
Aparna Varde 1
Christo Wilson 1
Ben Zhao 1
Chris Volinsky 1
Ricardo Campello 1
Xianchao Zhang 1
Shuhui Wang 1
Pedro Vaz De Melo 1
Jeffrey Chan 1
Michael Houle 1
Binbin Lin 1
Johannes Gehrke 1
Dimitrios Gunopulos 1
Daxin Jiang 1
Muna Al-Razgan 1
Mohsen Bayati 1
Peilin Zhao 1
Raymond Wong 1
Noman Mohammed 1
Chao Liu 1
Jaideep Vaidya 1
Andreas Krause 1
Dacheng Tao 1
Hsiangfu Yu 1
Collin Stultz 1
Boleslaw Szymanski 1
Maguelonne Teisseire 1
Paolo Boldi 1
Lini Thomas 1
Sachindra Joshi 1
Tharam Dillon 1
U Kang 1
Peter Christen 1
Daniel Dunlavy 1
Christos Doulkeridis 1
Steven Skiena 1
Yixin Chen 1
Xuanhong Dang 1
Kosuke Hashimoto 1
Nobuhisa Ueda 1
Hiroshi Motoda 1
Shumo Chu 1
Yong Ge 1
S Upham 1
Kasim Candan 1
Jeffrey Erman 1
Ming Li 1
Dora Erdős 1
Joydeep Ghosh 1
Kaiyuan Zhang 1
Carlos Ordonez 1
Fosca Giannotti 1
James Cheng 1
Li Zheng 1
Joao Duarte 1
David Dominguez-Sal 1
Danai Koutra 1
Kui Yu 1
Cheng Zeng 1
Atreya Srivathsan 1
Seekiong Ng 1
Hong Xie 1
Tong Sun 1
Jing Zhang 1
Rodrigo Alves 1
Juhua Hu 1
Wei Fan 1
Claudia Plant 1
Jiayu Pan 1
Rezwan Ahmed 1
Wei Wei 1
Duygu Ucar 1
Mustafa Bilgic 1
Ben Kao 1
David Cheung 1
Christopher Leckie 1
Brandon Westover 1
Eamonn Keogh 1
Ron Eyal 1
Avi Rosenfeld 1
Asaf Shabtai 1
Shifeng Weng 1
Kun Liu 1
Duo Zhang 1
Dmitry Pavlov 1
Raymond Ng 1
Piotr Indyk 1
Christopher Carothers 1
Anne Laurent 1
Satyanarayana Valluri 1
Ashish Verma 1
Jérémy Besson 1
Raghu Ramakrishnan 1
Rong Ge 1
Byronju Gao 1
Timothy De Vries 1
Jiang Bian 1
Li Tu 1
Yijuan Lu 1
Feng Liu 1
Yufeng Wang 1
Ernest Garcia 1
Shamkant Navathe 1
Saharon Rosset 1
Claudia Perlich 1
Ramana Kompella 1
Chengkai Li 1
Vasileios Kandylas 1
Salvatore Ruggieri 1
Tuannhon Dang 1
Yu Jin 1
Giulio Rossetti 1
Yanchi Liu 1
Songhua Xu 1
Eric Xing 1
Albert Bifet 1
Xiaoming Li 1
Josep Brunat 1
Fernando Kuipers 1
Dick Epema 1
Linpeng Tang 1
Min Wang 1
Ali Pınar 1
Ling Chen 1
Michail Vlachos 1
Yang Liu 1
Chunxiao Xing 1
Michael Lyu 1
Alexander Ihler 1
Dechuan Zhan 1
Dityan Yeung 1
Evangelos Papalexakis 1
Nicholas Sidiropoulos 1
George Karypis 1
Jilei Tian 1
Bin Guo 1
Davoud Moulavi 1
Qiang Qu 1
Koji Hino 1
Masaru Kitsuregawa 1
Xiang Zhang 1
Jenwei Huang 1
James Bailey 1
Philip Yu 1
Forrest Briggs 1
Gustavo Batista 1
Qiang Zhu 1
Jure Leskovec 1
Jon Kleinberg 1
Jianping Zhang 1
Manas Somaiya 1
Graham Cormode 1
Maya Bercovitch 1
Bin Li 1
Marc Maier 1
Mohamed Bouguessa 1
Mingxi Wu 1
Benjamin Fung 1
Ye Chen 1
John Canny 1
Kaiwei Chang 1
Dominique Laurent 1
Yeowwei Choong 1
Meghana Deodhar 1
Luca Becchetti 1
Ying Cui 1
Keli Xiao 1
Bo Long 1
Hans Kriegel 1
Martin Ester 1
Gunjan Gupta 1
Ling Feng 1
Diana Inkpen 1
Evrim Acar 1
Yang Zhou 1
Kuan Zhang 1
Vetle Torvik 1
Wei Fan 1
Masahiro Kimura 1
Nesreen Ahmed 1
Min Wang 1
Luigi Moccia 1
Edoardo Serra 1
Claudio Schifanella 1
Shuiwang Ji 1
Ruggero Pensa 1
Saurabh Paul 1
Jose Hern´ndez-Orallo 1
Rainer Gemulla 1
Eli Upfal 1
Guangtao Wang 1
Xueying Zhang 1
Yiping Ke 1
William Street 1
Ben London 1
Lionel Ni 1
Charu Aggarwal 1
Jirong Wen 1
Joseph Ruiz Md 1
Neil Shah 1
Gianluigi Greco 1
Francesco Gullo 1
Guimei Liu 1
Alexandru Iosup 1
Saurabh Kataria 1
Aniket Chakrabarti 1
Reza Zafarani 1
Yiming Yang 1
Irwin King 1
ChengXiang Zhai 1
Dong Xin 1
Christian Böhm 1
Longjie Li 1
Xiaolin Wang 1
Tingting Gao 1
Bruno Abrahão 1
Ling Liu 1
Huilei He 1
Hua Wang 1
Fei Zou 1
Virgílio Almeida 1
Christos Faloutsos 1
Deng Cai 1
Laiwan Chan 1
Raviv Raich 1
Bilson Campana 1
Nitin Agarwal 1
S Muthukrishnan 1
Kunta Chuang 1
Anthony Tung 1
Vibhor Rastogi 1
Sigal Sina 1
Sarit Kraus 1
Chris Ding 1
Lior Rokach 1
Dityan Yeung 1
Amin Saberi 1
Matthew Rattigan 1
Limin Yao 1
Kristina Lerman 1
Cheukkwong Lee 1
Olvi Mangasarian 1
Chris Clifton 1
Dafna Shahaf 1
Stephen Fienberg 1
Mohammed Zaki 1
Jennifer Dy 1
Shaojun Wang 1
Loïc Cerf 1
Henry Tan 1
Naren Ramakrishnan 1
Qi Tian 1
Jennifer Neary 1
Minoru Kanehisa 1
Kazumi Saito 1
Min Ding 1
Jennifer Neville 1
Gensheng Zhang 1
Christophe Giraud-Carrier 1
Kaiming Ting 1
Ayan Acharya 1
Sreangsu Acharyya 1
Arnold Boedihardjo 1
Changtien Lu 1
Zhiqiang Xu 1
Geoffrey Barbier 1
Zhongfei Zhang 1
Matthew Rowe 1
Edward Chang 1
Francesco Lupia 1
Nima Mirbakhsh 1
Antti Ukkonen 1
Xindong Wu 1
Siqi Shen 1
Lei Li 1
Xinran He 1
Zheng Wang 1
Bin Cui 1
Chengqi Zhang 1
Juanzi Li 1
Johannes Schneider 1
Tiancheng Lou 1
Guna Seetharaman 1
Qiaozhu Mei 1
Erheng Zhong 1
Wei Fan 1
Qiang Yang 1
Giacomo Berardi 1
Zhu Wang 1
Xiaotong Zhang 1
Han Liu 1
Kathleen Carley 1
Xiaodan Song 1
Yasuhiro Fujiwara 1
Wei Wang 1
ChienWei Chen 1
Weiyin Loh 1
Jiawei Han 1
Ashwin Machanavajjhala 1
John Salerno 1
Nitin Kumar 1
Flip Korn 1
Suresh Iyengar 1
Ying Wang 1
Ke Wang 1
Jing Zhang 1
Xiuyao Song 1
Benoît Dumoulin 1
John Gums 1
Yin Zhang 1
Zhongfei Zhang 1
Yunxin Zhao 1
Jude Shavlik 1
Hui Ke 1
Tamara Kolda 1
Yandong Liu 1
T Murali 1
Kiyoko Aoki-Kinoshita 1
Ravi Janardan 1
Sudhir Kumar 1
Qian Sun 1
Xiaohui Lu 1
Domenico Saccà 1
Patrick Haffner 1
Zhili Zhang 1
Qingyan Yang 1
Scott Burton 1
Christos Boutsidis 1
Bingsheng Wang 1
Chris Ding 1
Jie Wang 1
Karthik Subbian 1
Galileo Namata 1
Yulan He 1
John Frenzel MD 1
Hua Duan 1
Joshua Vogelstein 1

Affiliation Paper Counts
Syracuse University 1
University of Queensland 1
Curtin University of Technology, Perth 1
University of Roma La Sapienza 1
University of the Saarland 1
Institute of Mathematics and Informatics Lithuanian 1
Amazon.com, Inc. 1
Harvard School of Engineering and Applied Sciences 1
Ariel University Center of Samaria 1
Siemens USA 1
Microsoft Research Asia 1
Innopolis University 1
Ryukoku University 1
Cemagref 1
University of Michigan 1
Anhui University 1
University of Ontario Institute of Technology 1
Universite de Cergy-Pontoise 1
Princeton University 1
Queens College, City University of New York 1
University of Arkansas - Fayetteville 1
Yale University 1
University of Missouri-Columbia 1
John F. Kennedy School of Government 1
The University of North Carolina at Charlotte 1
University of South Florida Tampa 1
Valley Laboratory 1
University of Salford 1
Australian National University 1
University of Texas at Dallas 1
University of Vermont 1
Nanjing University of Science and Technology 1
Washington University in St. Louis 1
HP Labs 1
BBN Technologies 1
Air Force Research Laboratory Information Directorate 1
University of Shizuoka 1
MITRE Corporation 1
Norwegian University of Science and Technology 1
Indian Institute of Science 1
Zhejiang Wanli University 1
Aston University 1
University of Southern California, Information Sciences Institute 1
John Carroll University 1
Brigham and Women's Hospital 1
University of Toronto 1
Wright State University 1
Singapore Management University 1
Air Force Research Laboratory 1
IBM 1
Universite Montpellier 2 Sciences et Techniques 1
Nanjing University of Aeronautics and Astronautics 1
Industrial Technology Research Institute of Taiwan 1
Hong Kong Red Cross Blood Transfusion Service 1
Nokia USA 1
Universite Claude Bernard Lyon 1 1
Lancaster University 1
Osaka University 1
University of Iowa 1
University of California, Berkeley 1
Shanghai Jiaotong University 1
Wright-Patterson AFB 1
University Michigan Ann Arbor 1
Swiss Federal Institute of Technology, Zurich 1
Lawrence Livermore National Laboratory 1
Jerusalem College of Technology 1
National Taiwan University of Science and Technology 1
Oracle Corporation 1
Lanzhou University 1
Northeastern University 1
Research Organization of Information and Systems National Institute of Informatics 1
Kent State University 1
University of Milan 1
Max Planck Institute for Informatics 2
Hefei University of Technology 2
Zhejiang University 2
Institute of High Performance Computing, Singapore 2
Johns Hopkins University 2
Tel Aviv University 2
University of Minnesota System 2
University of Houston 2
The University of Hong Kong 2
Brigham Young University 2
The University of Western Ontario 2
Brown University 2
Montclair State University 2
Hong Kong Baptist University 2
Renmin University of China 2
University of California, Davis 2
University of Texas M. D. Anderson Cancer Center 2
University of Kansas Lawrence 2
University of Quebec in Outaouais 2
Institute for Systems and Computer Engineering of Porto 2
University of Massachusetts Boston 2
University of Tokyo 2
Nokia 2
University of Athens 2
IBM Zurich Research Laboratory 2
University of California, San Diego 2
Rutgers University 2
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 2
Qatar Computing Research institute 2
International Institute of Information Technology Hyderabad 3
Shandong University of Science and Technology 3
Bar-Ilan University 3
Dalian University of Technology 3
Rice University 3
University of Pennsylvania 3
University of California, Irvine 3
University of Sao Paulo 3
The University of British Columbia 3
IBM Research 3
INSA Lyon 3
George Mason University 3
Xerox Corporation 3
Binghamton University State University of New York 3
Italian National Research Council 3
The University of North Carolina at Chapel Hill 3
University of Sydney 3
Microsoft 3
University of Melbourne 3
University of California, Santa Barbara 3
Wuhan University 3
University of Southern California 3
University of Alberta 3
Rutgers University-Newark Campus 4
Emory University 4
Institute for Infocomm Research, A-Star, Singapore 4
Brookhaven National Laboratory 4
Universitat Politecnica de Catalunya 4
University of Antwerp 4
National University of Singapore 4
Athens University of Economics and Business 4
Monash University 4
Boston University 4
Massachusetts Institute of Technology 4
New Jersey Institute of Technology 4
Ben-Gurion University of the Negev 4
University of Pisa 4
Yahoo Research Barcelona 4
Aalto University 4
Pennsylvania State University 5
University of Texas at San Antonio 5
Ohio State University 5
Northwestern Polytechnical University China 5
Purdue University 5
Kyoto University 5
University of Turin 5
Oregon State University 5
Sandia National Laboratories 5
Microsoft Research 5
University of Technology Sydney 5
Yahoo Inc. 5
Delft University of Technology 6
AT&T Laboratories Florham Park 6
University of Massachusetts Amherst 6
Georgia Institute of Technology 6
Nippon Telegraph & Telephone 6
Stony Brook University 6
Ludwig Maximilian University of Munich 6
University of Minnesota Twin Cities 6
Hong Kong University of Science and Technology 7
University of Florida 7
Peking University 7
University of Science and Technology of China 7
University of Maryland 7
Virginia Tech 7
University of California, Riverside 7
Federal University of Minas Gerais 7
Nanjing University 7
Nanyang Technological University 8
Stanford University 8
University of Texas at Austin 8
IBM Thomas J. Watson Research Center 8
Xi'an Jiaotong University 8
Yahoo Research Labs 8
National Taiwan University 9
Florida International University 9
University of Illinois at Chicago 10
Rensselaer Polytechnic Institute 11
Cornell University 12
Simon Fraser University 12
University of Calabria 12
University of Texas at Arlington 13
University of Illinois at Urbana-Champaign 15
NEC Laboratories America, Inc. 15
Chinese University of Hong Kong 17
Tsinghua University 18
Carnegie Mellon University 29
Arizona State University 38

### ACM Transactions on Knowledge Discovery from Data (TKDD) Archive

#### 2016

Volume 10 Issue 3, February 2016

#### 2015

Volume 10 Issue 2, October 2015
Volume 10 Issue 1, July 2015
Volume 9 Issue 4, June 2015
Volume 9 Issue 3, April 2015 TKDD Special Issue (SIGKDD'13)

#### 2014

Volume 9 Issue 2, November 2014
Volume 9 Issue 1, October 2014
Volume 8 Issue 4, October 2014
Volume 8 Issue 3, June 2014
Volume 8 Issue 2, June 2014
Volume 8 Issue 1, February 2014 Casin special issue

#### 2013

Volume 7 Issue 4, November 2013
Volume 7 Issue 3, September 2013 Special Issue on ACM SIGKDD 2012
Volume 7 Issue 2, July 2013
Volume 7 Issue 1, March 2013

#### 2012

Volume 6 Issue 4, December 2012 Special Issue on the Best of SIGKDD 2011
Volume 6 Issue 3, October 2012
Volume 6 Issue 2, July 2012
Volume 6 Issue 1, March 2012
Volume 5 Issue 4, February 2012

#### 2011

Volume 5 Issue 3, August 2011
Volume 5 Issue 2, February 2011

#### 2010

Volume 5 Issue 1, December 2010
Volume 4 Issue 4, October 2010
Volume 4 Issue 3, October 2010
Volume 4 Issue 2, May 2010
Volume 4 Issue 1, January 2010

#### 2009

Volume 3 Issue 4, November 2009
Volume 3 Issue 3, July 2009
Volume 3 Issue 2, April 2009
Volume 3 Issue 1, March 2009
Volume 2 Issue 4, January 2009

#### 2008

Volume 2 Issue 3, October 2008
Volume 2 Issue 2, July 2008
Volume 2 Issue 1, March 2008
Volume 1 Issue 4, January 2008

#### 2007

Volume 1 Issue 3, December 2007
Volume 1 Issue 2, August 2007
Volume 1 Issue 1, March 2007