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Articles 391 - 420 of 1024
Full-Text Articles in Numerical Analysis and Scientific Computing
The Role Of Urban Mobility In Retail Business Survival, Krittika D'Silva, Kasthuri Jayarajah, Anastasios Noulas, Cecilia Mascolo, Archan Misra
The Role Of Urban Mobility In Retail Business Survival, Krittika D'Silva, Kasthuri Jayarajah, Anastasios Noulas, Cecilia Mascolo, Archan Misra
Research Collection School Of Computing and Information Systems
Economic and urban planning agencies have strong interest in tackling the hard problem of predicting the odds of survival of individual retail businesses. In this work, we tap urban mobility data available both from a location-based intelligence platform, Foursquare, and from public transportation agencies, and investigate whether mobility-derived features can help foretell the failure of such retail businesses, over a 6 month horizon, across 10 distinct cities spanning the globe. We hypothesise that the survival of such a retail outlet is correlated with not only venue-specific characteristics but also broader neighbourhood-level effects. Through careful statistical analysis of Foursquare and taxi …
Predicting Episodes Of Non-Conformant Mobility In Indoor Environments, Kasthuri Jayarajah, Archan Misra
Predicting Episodes Of Non-Conformant Mobility In Indoor Environments, Kasthuri Jayarajah, Archan Misra
Research Collection School Of Computing and Information Systems
Traditional mobility prediction literature focuses primarily on improved methods to extract latent patterns from individual-specific movement data. When such predictions are incorrect, we ascribe it to 'random' or 'unpredictable' changes in a user's movement behavior. Our hypothesis, however, is that such apparently-random deviations from daily movement patterns can, in fact, of ten be anticipated. In particular, we develop a methodology for predicting Likelihood of Future Non-Conformance (LFNC), based on two central hypotheses: (a) the likelihood of future deviations in movement behavior is positively correlated to the intensity of such trajectory deviations observed in the user's recent past, and (b) the …
Feature Engineering For Machine Learning And Data Analytics, Xin Xia, David Lo
Feature Engineering For Machine Learning And Data Analytics, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
This chapter provides an introduction on feature generation and engineering for software analytics. Specifically, we show how domain-specifc features can be designed and used to automate three software engineering tasks: (1) detecting defective software modules (defect prediction), (2) identifying crashing mobile app release (crash release prediction), and (3) predicting who will leave a software team (developer turnover prediction). For each of the three tasks, different sets of features are extracted from a diverse set of software artifacts, and used to build predictive models.
What Is Gab: A Bastion Of Free Speech Or An Alt-Right Echo Chamber, Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn
What Is Gab: A Bastion Of Free Speech Or An Alt-Right Echo Chamber, Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, Michael Sirivianos, Gianluca Stringhini, Jeremy Blackburn
Research Collection School Of Computing and Information Systems
Over the past few years, a number of new "fringe" communities, like 4chan or certain subreddits, have gained traction on the Web at a rapid pace. However, more often than not, little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities. This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem. In August 2016, a new social network called Gab was created as an alternative to Twitter. It positions itself as putting "people and free …
A Sliding-Window Framework For Representative Subset Selection, Yanhao Wang, Yuchen Li, Kian-Lee Tan
A Sliding-Window Framework For Representative Subset Selection, Yanhao Wang, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. A common approach is to model RSS as the submodular maximization problem because the utility of extracted representatives often satisfies the "diminishing returns" property. To capture the data recency issue and support different types of constraints in real-world problems, we formulate RSS as maximizing a submodular function subject to a d-knapsack constraint (SMDK) over sliding windows. Then, we propose a novel KnapWindow framework for SMDK. Theoretically, KnapWindow is 1-ε/1+d - approximate for SMDK and achieves sublinear complexity. Finally, we evaluate the efficiency and effectiveness …
Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors Of Food Journalers, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek
Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors Of Food Journalers, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek
Research Collection School Of Computing and Information Systems
Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and …
Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Chinese developers often cannot effectively search questions in English, because they may have difficulties in translating technical words from Chinese to English and formulating proper English queries. For the purpose of helping Chinese developers take advantage of the rich knowledge base of Stack Overflow and simplify the question retrieval process, we propose an automated cross-language relevant question retrieval (CLRQR) system to retrieve relevant English questions for a given Chinese question. CLRQR first extracts essential information (both Chinese and English) from the title and description of the input Chinese question, then performs domain-specific translation of the essential Chinese information into English, …
Octopus: An Online Topic-Aware Influence Analysis System For Social Networks, Ju Fan, Jiarong Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, Kian-Lee Tan, Xiaoyong Du
Octopus: An Online Topic-Aware Influence Analysis System For Social Networks, Ju Fan, Jiarong Qiu, Yuchen Li, Qingfei Meng, Dongxiang Zhang, Guoliang Li, Kian-Lee Tan, Xiaoyong Du
Research Collection School Of Computing and Information Systems
The wide adoption of social networks has brought a new demand on influence analysis. This paper presents OCTOPUS that offers social network users and analysts valuable insights through topic-aware social influence analysis services. OCTOPUS has the following novel features. First, OCTOPUS provides a user-friendly interface that allows users to employ simple and easy-to-use keywords to perform influence analysis. Second, OCTOPUS provides three powerful keyword-based topic-aware influence analysis tools: keyword-based influential user discovery, personalized influential keywords suggestion, and interactive influential paths exploration. These tools can not only discover influential users, but also provide insights on how the users influence the network. …
Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Exploiting User And Venue Characteristics For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Which venue is a tweet posted from? We call this a fine-grained geolocation problem. Given an observed tweet, the task is to infer its discrete posting venue, e.g., a specific restaurant. This recovers the venue context and differs from prior work, which geolocats tweets to location coordinates or cities/neighborhoods. First, we conduct empirical analysis to uncover venue and user characteristics for improving geolocation. For venues, we observe spatial homophily, in which venues near each other have more similar tweet content (i.e., text representations) compared to venues further apart. For users, we observe that they are spatially focused and more likely …
A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham
A Data-Driven Analysis Of Workers' Earnings On Amazon Mechanical Turk, Kotaro Hara, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
A growing number of people are working as part of on-line crowd work. Crowd work is often thought to be low wage work. However, we know little about the wage distribution in practice and what causes low/high earnings in this setting. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~$2/h, and only 4% earned more than $7.25/h. While the average requester pays more than $11/h, lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is accounted for, …
A Novel Representation And Compression For Queries On Trajectories In Road Networks, Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, Baihua Zheng
A Novel Representation And Compression For Queries On Trajectories In Road Networks, Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, Baihua Zheng
Research Collection School Of Computing and Information Systems
Recording and querying time-stamped trajectories incurs high cost of data storage and computing. In this paper, we explore several characteristics of the trajectories in road mbox{networks}, which have motivated the idea of coding trajectories by associating timestamps with relative spatial path and locations. Such a representation contains large number of duplicate information to achieve a lower entropy compared with the existing representations, thereby drastically cutting the storage cost. We propose several techniques to compress spatial path and locations separately, which can support fast positioning and achieve better compression ratio. For locations, we propose two novel encoding schemes such that the …
Mining Sandboxes: Are We There Yet?, Lingfeng Bao, Tien Duy B. Le, David Lo
Mining Sandboxes: Are We There Yet?, Lingfeng Bao, Tien Duy B. Le, David Lo
Research Collection School Of Computing and Information Systems
The popularity of Android platform on mobile devices has attracted much attention from many developers and researchers, as well as malware writers. Recently, Jamrozik et al. proposed a technique to secure Android applications referred to as mining sandboxes. They used an automated test case generation technique to explore the behavior of the app under test and then extracted a set of sensitive APIs that were called. Based on the extracted sensitive APIs, they built a sandbox that can block access to APIs not used during testing. However, they only evaluated the proposed technique with benign apps but not investigated whether …
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Research Collection School Of Computing and Information Systems
To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed …
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the …
Unified Locally Linear Classifiers With Diversity-Promoting Anchor Points, Chenghao Liu, Teng Zhang, Peilin Zhao, Jianling Sun, Steven C. H. Hoi
Unified Locally Linear Classifiers With Diversity-Promoting Anchor Points, Chenghao Liu, Teng Zhang, Peilin Zhao, Jianling Sun, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Locally Linear Support Vector Machine (LLSVM) has been actively used in classification tasks due to its capability of classifying nonlinear patterns. However, existing LLSVM suffers from two drawbacks: (1) a particular and appropriate regularization for LLSVM has not yet been addressed; (2) it usually adopts a three-stage learning scheme composed of learning anchor points by clustering, learning local coding coordinates by a predefined coding scheme, and finally learning for training classifiers. We argue that this decoupled approaches oversimplifies the original optimization problem, resulting in a large deviation due to the disparate purpose of each step. To address the first issue, …
Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan
Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan
Research Collection School Of Computing and Information Systems
Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system …
Identifying And Computing The Exact Core-Determining Class, Ye Luo, Hai Wang
Identifying And Computing The Exact Core-Determining Class, Ye Luo, Hai Wang
Research Collection School Of Computing and Information Systems
The indeterministic relations between unobservable events andobserved outcomes in partially identified models can be characterized bya bipartite graph. Given a probability measure on observed outcomes, theset of feasible probability measures on unobservable events can be definedby a set of linear inequality constraints, according to Artstein’s Theorem.This set of inequalities is called the “core-determining class”. However, thenumber of inequalities defined by Artstein’s Theorem is exponentially increasing with the number of unobservable events, and many inequalitiesmay in fact be redundant. In this paper, we show that the “exact coredetermining class”, i.e., the smallest possible core-determining class, canbe characterized by a set of …
Competency Analytics Tool: Analyzing Curriculum Using Course Competencies, Swapna Gottipati, Venky Shankararaman
Competency Analytics Tool: Analyzing Curriculum Using Course Competencies, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
The applications of learning outcomes and competency frameworks have brought better clarity to engineering programs in many universities. Several frameworks have been proposed to integrate outcomes and competencies into course design, delivery and assessment. However, in many cases, competencies are course-specific and their overall impact on the curriculum design is unknown. Such impact analysis is important for analyzing, discovering gaps and improving the curriculum design. Unfortunately, manual analysis is a painstaking process due to large amounts of competencies across the curriculum. In this paper, we propose an automated method to analyze the competencies and discover their impact on the overall …
Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo
Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo
Research Collection School Of Computing and Information Systems
In this entry, we mine collaboration patterns from a large software developer network (Surian et al. 2010). We consider high- and low-level patterns. High-level patterns correspond to various network-level statistics that we observe to hold in this network. Low-level patterns are topological subgraph patterns that are frequently observed among developers collaborating in the network. Mining topological subgraph patterns are difficult as it is an NP-hard problem. To address this issue, we use a combination of frequent subgraph mining and graph matching by leveraging the power law property exhibited by a large collaboration graph. The technique is applicable to any software …
Pose Guided Person Image Generation, Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool
Pose Guided Person Image Generation, Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool
Research Collection School Of Computing and Information Systems
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG^2 utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial …
Multilingual Sentiment Analysis : From Formal To Informal And Scarce Resource Languages, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth
Multilingual Sentiment Analysis : From Formal To Informal And Scarce Resource Languages, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts …
Leveraging Auxiliary Tasks For Document-Level Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang
Leveraging Auxiliary Tasks For Document-Level Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we study domain adaptationwith a state-of-the-art hierarchicalneural network for document-level sentimentclassification. We first design a newauxiliary task based on sentiment scoresof domain-independent words. We thenpropose two neural network architecturesto respectively induce document embeddingsand sentence embeddings that workwell for different domains. When thesedocument and sentence embeddings areused for sentiment classification, we findthat with both pseudo and external sentimentlexicons, our proposed methods canperform similarly to or better than severalhighly competitive domain adaptationmethods on a benchmark dataset of productreviews.
Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ …
A Compact Representation Of Human Actions By Sliding Coordinate Coding, Runwei Ding, Qianru Sun, Mengyuan Liu, Hong Liu
A Compact Representation Of Human Actions By Sliding Coordinate Coding, Runwei Ding, Qianru Sun, Mengyuan Liu, Hong Liu
Research Collection School Of Computing and Information Systems
Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of …
Extracting Implicit Suggestions From Students’ Comments: A Text Analytics Approach, Venky Shankararaman, Swapna Gottipati, Jeff Rongsheng Lin, Sandy Gan
Extracting Implicit Suggestions From Students’ Comments: A Text Analytics Approach, Venky Shankararaman, Swapna Gottipati, Jeff Rongsheng Lin, Sandy Gan
Research Collection School Of Computing and Information Systems
At the end of each course, students are required to give feedback on the course and instructor. This feedback includes quantitative rating using Likert scale and qualitative feedback as comments. Such qualitative feedback can provide valuable insights in helping the instructor enhance the course content and teaching delivery. However, the main challenge in analysing the qualitative feedback is the perceived increase in time and effort needed to manually process the textual comments. In this paper, we provide an automated solution for analysing comments, specifically extracting implicit suggestions from the students’ qualitative feedback comments. The implemented solution leverages existing text mining …
Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw
Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via …
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto
Predicting Indoor Crowd Density Using Column-Structured Deep Neural Network, Akihito Sudo, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Yoshihide Sekimoto
Research Collection School Of Computing and Information Systems
This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure …
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance givenlimited query budget. Secondly, it is also challenging to decide how to update the predictive models effectively whenever the true labelof an instance is queried. Most existing approaches for online active learning are often based on a family of first-order online …
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim
Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness …
On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Analyzing job hopping behavior is important for theunderstanding of job preference and career progression of working individuals.When analyzed at the workforce population level, job hop analysis helps to gaininsights of talent flow and organization competition. Traditionally, surveysare conducted on job seekers and employers to study job behavior. While surveysare good at getting direct user input to specially designed questions, they areoften not scalable and timely enough to cope with fast-changing job landscape.In this paper, we present a data science approach to analyze job hops performedby about 490,000 working professionals located in a city using their publiclyshared profiles. We develop several …