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Articles 5761 - 5790 of 8479
Full-Text Articles in Computer Sciences
Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama
Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama
Research Collection School Of Computing and Information Systems
In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible …
Board Interlock Networks And The Use Of Relative Performance Evaluation, Qian Hao, Nan Hu, Ling Liu, Lee J. Yao
Board Interlock Networks And The Use Of Relative Performance Evaluation, Qian Hao, Nan Hu, Ling Liu, Lee J. Yao
Research Collection School Of Computing and Information Systems
Purpose - The purpose of this paper is to explore how networks of boards of directors affect relative performance evaluation (RPE) in chief executive officer (CEO) compensation. Design/methodology/approach - In this study, the authors propose that an interlocking network is an important inter-corporate setting, which has a bearing on whether boards decide to use RPE in CEO compensation. They adopt four typical graph measures to depict the centrality/position of each board in the interlock network: degree, betweenness, eigenvector and closeness, and study their impacts on RPE use. Findings - The authors find that firms that have more connected board members …
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Research Collection School Of Computing and Information Systems
The open source community, as well as numerous technical blogs and community web sites, put online vast quantities of free source code, ranging from snippets to full-blown products. This code embodies the software development community’s domain knowledge, and mirrors the structure of the Internet: it is distributed rather than hierarchical; it is chaotic, incomplete, and inconsistent. StackOverflow.com is a Question and Answer (Q&A) website which uses social media to facilitate knowledge exchange between programmers by mitigating the pitfalls involved in using code from the Internet. Its design nurtures a community of developers, and enables crowd sourced software engineering activities ranging …
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to …
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Scalable Detection Of Missed Cross-Function Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Refactoring is an important way to improve the design of existing code. Identifying refactoring opportunities (i.e., code fragments that can be refactored) in large code bases is a challenging task. In this paper, we propose a novel, automated and scalable technique for identifying cross-function refactoring opportunities that span more than one function (e.g., Extract Method and Inline Method). The key of our technique is the design of efficient vector inlining operations that emulate the effect of method inlining among code fragments, so that the problem of identifying cross-function refactoring can be reduced to the problem of finding similar vectors before …
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Sougata Sen, Lipyeow Lim, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Sougata Sen, Lipyeow Lim, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this paper, we reduce the energy overheads of continuous mobile sensing for context-aware applications that are interested in collective context or events. We propose a cloud-based query management and optimization framework, called CloQue, which can support concurrent queries, executing over thousands of individual smartphones. CloQue exploits correlation across context of different users to reduce energy overheads via two key innovations: i) Dynamically reordering the order of predicate processing to preferentially select predicates with not just lower sensing cost and higher selectivity, but that maximally reduce the uncertainty about other context predicates, and ii) intelligently propagating the query evaluation results …
Building Algorithm Portfolios For Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau
Building Algorithm Portfolios For Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performance. The past performance is used to cluster algorithm combinations. Top performing combinations are then considered as the members of the set. The set is expected to have algorithm combinations complementing each other with respect to their strengths in a portfolio setting. In other words, each algorithm combination should be good at solving a certain type of problem instances such that this set can be …
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang
Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang
Research Collection School Of Computing and Information Systems
Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments …
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Research Collection School Of Computing and Information Systems
Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and enough hardware infrastructure. Synchronicity is a location system that aims to push the envelope of highly-accurate localization systems further in both dimensions, requiring less line-of-sight and less infrastructure. With Distributed MIMO network of wireless LAN access points (APs) as a starting point, we leverage the time synchronization that such a network affords to localize with time-difference-of-arrival information at the APs. We contribute novel super-resolution signal processing algorithms and reflection path elimination schemes, yielding superior results even in …
Understanding The Paradigm Shift To Computational Social Science In The Presence Of Big Data, Ray M. Chang, Robert J. Kauffman, Young Ok Kwon
Understanding The Paradigm Shift To Computational Social Science In The Presence Of Big Data, Ray M. Chang, Robert J. Kauffman, Young Ok Kwon
Research Collection School Of Computing and Information Systems
The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data collection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse, corporate announcements, digital journalism, mobile telephony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and …
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Research Collection School Of Computing and Information Systems
We address the problem of minimizing staffing cost in a contact center subject to service level requirements over multiple weeks. We handle both the capacity planning and agent schedule generation aspect of this problem. Our work incorporates two unique business requirements. First, we develop techniques that can provide near-optimal staffing for 247 contact centers over long term, upto eight weeks, rather than planning myopically on a week-on-week basis. Second, our approach is usable in an online interactive setting in which staffing managers using our system expect high quality plans within a short time period. Results on large real world and …
Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only …
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …
Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw
Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to …
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters. Theoretically, we prove the optimal linear convergence rate for the proposed SDCA algorithm, beating the well-known sublinear convergence rate by the previous best metric learning algorithms. Empirically, we conduct extensive …
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li
Research Collection School Of Computing and Information Systems
Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in high-dimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better …
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …
Cenknn: A Scalable And Effective Text Classifier, Guansong Pang, Huidong Jin, Shengyi Jiang
Cenknn: A Scalable And Effective Text Classifier, Guansong Pang, Huidong Jin, Shengyi Jiang
Research Collection School Of Computing and Information Systems
A big challenge in text classification is to perform classification on a large-scale and high-dimensional text corpus in the presence of imbalanced class distributions and a large number of irrelevant or noisy term features. A number of techniques have been proposed to handle this challenge with varying degrees of success. In this paper, by combining the strengths of two widely used text classification techniques, K-Nearest-Neighbor (KNN) and centroid based (Centroid) classifiers, we propose a scalable and effective flat classifier, called CenKNN, to cope with this challenge. CenKNN projects high-dimensional (often hundreds of thousands) documents into a low-dimensional (normally a few …
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
A Novel Algorithm Based On Visual Saliency Attention For Localization And Segmentation In Rapidly-Stained Leukocyte Images, Xin Zheng, Yong Wang, Guoyou Wang, Zhong Chen
Research Collection School Of Computing and Information Systems
In this paper, we propose a fast hierarchical framework of leukocyte localization and segmentation in rapidly-stained leukocyte images (RSLI) with complex backgrounds and varying illumination. The proposed framework contains two main steps. First, a nucleus saliency model based on average absolute difference is built, which locates each leukocyte precisely while effectively removes dyeing impurities and erythrocyte fragments. Secondly, two different schemes are presented for segmenting the nuclei and cytoplasm respectively. As for nuclei segmentation, to solve the overlap problem between leukocytes, we extract the nucleus lobes first and further group them. The lobes extraction is realized by the histogram-based contrast …
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
Research Collection School Of Computing and Information Systems
This real-life case study, which was undertaken by a leading financial services group in the Asia-Pacific region, is used to demonstrate the innovative use of BPM (Business Process Management) technology in a competitive business area. It describes how a BPM project, within the Application Verification and Capture (AVC), was conceived, designed and implemented in order to deliver strategic value to the organization. Hereafter, the financial services group will be referred to as “the bank”. The AVC project was targeted at one of the bank's processes called the Application Verification and Capture (AVC) process for unit trust products. This process involved …
Does Latitude Hurt While Longitude Kills? Geographical And Temporal Separation In A Large Scale Software Development Project, Patrick Wagstrom, Subhajit Datta
Does Latitude Hurt While Longitude Kills? Geographical And Temporal Separation In A Large Scale Software Development Project, Patrick Wagstrom, Subhajit Datta
Research Collection School Of Computing and Information Systems
Distributed software development allows firms to leverage cost advantages and place work near centers of competency. This distribution comes at a cost -- distributed teams face challenges from differing cultures, skill levels, and a lack of shared working hours. In this paper we examine whether and how geographic and temporal separation in a large scale distributed software development influences developer interactions. We mine the work item trackers for a large commercial software project with a globally distributed development team. We examine both the time to respond and the propensity of individuals to respond and find that when taken together, geographic …
Interactive Two-Sided Transparent Displays: Designing For Collaboration, Jiannan Li, Saul Greenberg, Ehud Sharlin, Joaquim Jorge
Interactive Two-Sided Transparent Displays: Designing For Collaboration, Jiannan Li, Saul Greenberg, Ehud Sharlin, Joaquim Jorge
Research Collection School Of Computing and Information Systems
Transparent displays can serve as an important collaborative medium supporting face-to-face interactions over a shared visual work surface. Such displays enhance workspace awareness: when a person is working on one side of a transparent display, the person on the other side can see the other's body, hand gestures, gaze and what he or she is actually manipulating on the shared screen. Even so, we argue that designing such transparent displays must go beyond current offerings if it is to support collaboration. First, both sides of the display must accept interactive input, preferably by at least touch and / or pen, …
Constructive Visualization, Samuel Huron, Sheelagh Carpendale, Alice Thudt, Anthony Tang, Michael Mauerer
Constructive Visualization, Samuel Huron, Sheelagh Carpendale, Alice Thudt, Anthony Tang, Michael Mauerer
Research Collection School Of Computing and Information Systems
If visualization is to be democratized, we need to provide means for non-experts to create visualizations that allow them to engage directly with datasets. We present constructive visualization a new paradigm for the simple creation of flexible, dynamic visualizations. Constructive visualization is simple—in that the skills required to build and manipulate the visualizations are akin to kindergarten play; it is expressive— in that one can build within the constraints of the chosen environment, and it also supports dynamics — in that these constructed visualizations can be rebuilt and adjusted. We describe the conceptual components and processes underlying constructive visualization, and …
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results in a list view is widely adopted by current commercial search engines, which cannot support efficient browsing for complex search topics that have multiple semantic facets. In this article, we propose to organize video search results in a highly structured way. Specifically, videos are placed on a semantic hierarchy that accurately organizes various facets of a given search topic. To pick the most suitable videos for each node of the hierarchy, we define and utilize three important criteria: relevance, uniqueness, and diversity. Extensive evaluations on a large YouTube video dataset demonstrate the effectiveness of our approach.
Learning Euclidean-To-Riemannian Metric For Point-To-Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Learning Euclidean-To-Riemannian Metric For Point-To-Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Research Collection School Of Computing and Information Systems
In this paper, we focus on the problem of point-to-set classification, where single points are matched against sets of correlated points. Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively. To learn a metric between the heterogeneous points, we propose a novel Euclidean-to-Riemannian metric learning framework. Specifically, by exploiting typical Riemannian metrics, the Riemannian manifold is first embedded into a high dimensional Hilbert space to reduce the gaps between the heterogeneous spaces and meanwhile respect the Riemannian geometry of …
Daisy Filter Flow: A Generalized Discrete Approach To Dense Correspondences, Hongsheng Yang, Wen-Yan Lin, Jiangbo Lu
Daisy Filter Flow: A Generalized Discrete Approach To Dense Correspondences, Hongsheng Yang, Wen-Yan Lin, Jiangbo Lu
Research Collection School Of Computing and Information Systems
No abstract provided.
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In wireless sensor networks powered by ambient energy harvesting (WSNs-HEAP), sensor nodes' energy harvesting rates are spatially heterogeneous and temporally variant, which impose difficulties for medium access control (MAC). In this paper, we first derive the necessary conditions under which channel utilization and fairness are optimal in a WSN-HEAP, respectively. Based on the analysis, we propose an earliest deadline first (EDF) polling MAC protocol, which regulates transmission sequence of the sensor nodes based on the spatially heterogeneous energy harvesting rates. It also mitigates temporal variations in energy harvesting rates by a prediction and update mechanism. Simulation results verify the performance …
Bootstrapping Simulation-Based Algorithms With A Suboptimal Policy, Nguyen T., Silander T., Lee W., Tze-Yun Leong
Bootstrapping Simulation-Based Algorithms With A Suboptimal Policy, Nguyen T., Silander T., Lee W., Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Finding optimal policies for Markov Decision Processes with large state spaces is in general intractable. Nonetheless, simulation-based algorithms inspired by Sparse Sampling (SS) such as Upper Confidence Bound applied in Trees (UCT) and Forward Search Sparse Sampling (FSSS) have been shown to perform reasonably well in both theory and practice, despite the high computational demand. To improve the efficiency of these algorithms, we adopt a simple enhancement technique with a heuristic policy to speed up the selection of optimal actions. The general method, called Aux, augments the look-ahead tree with auxiliary arms that are evaluated by the heuristic policy. In …
Online Community Transition Detection, Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu
Online Community Transition Detection, Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu
Research Collection School Of Computing and Information Systems
Mining user behavior patterns in social networks is of great importance in user behavior analysis, targeted marketing, churn prediction and other applications. However, less effort has been made to study the evolution of user behavior in social communities. In particular, users join and leave communities over time. How to automatically detect the online community transitions of individual users is a research problem of immense practical value yet with great technical challenges. In this paper, we propose an algorithm based on the Minimum Description Length (MDL) principle to trace the evolution of community transition of individual users, adaptive to the noisy …