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Articles 4591 - 4620 of 8495
Full-Text Articles in Computer Sciences
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Pivot-Based Metric Indexing, Lu Chen, Yunjun Gao, Baihua Zheng, Christian S. Jensen, Hanyu Yang, Keyu Yang
Research Collection School Of Computing and Information Systems
The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies …
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Research Collection School Of Computing and Information Systems
Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, …
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Sparse Online Learning Of Image Similarity, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Jianshe Zhou, Ji Wan, Zhenyu Chen, Jintao Li, Jianke Zhu
Research Collection School Of Computing and Information Systems
Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations …
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …
Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham
Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Research Collection School Of Computing and Information Systems
We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative …
Basket-Sensitive Personalized Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Basket-Sensitive Personalized Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the …
Fast Adaptation Of Activity Sensing Policies In Mobile Devices, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Dong In Kim
Fast Adaptation Of Activity Sensing Policies In Mobile Devices, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Dong In Kim
Research Collection School Of Computing and Information Systems
With the proliferation of sensors, such as accelerometers,in mobile devices, activity and motion tracking has become a viable technologyto understand and create an engaging user experience. This paper proposes afast adaptation and learning scheme of activity tracking policies when userstatistics are unknown a priori, varying with time, and inconsistent for differentusers. In our stochastic optimization, user activities are required to besynchronized with a backend under a cellular data limit to avoid overchargesfrom cellular operators. The mobile device is charged intermittently usingwireless or wired charging for receiving the required energy for transmission andsensing operations. Firstly, we propose an activity tracking policy …
The Role Of Different Tie Strength In Disseminating Different Topics On A Microblog, Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang
The Role Of Different Tie Strength In Disseminating Different Topics On A Microblog, Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang
Research Collection School Of Computing and Information Systems
The study of information flow typically does not distinguish the choices of tie strength on which the information flows. All receivers of the information are assumed to have the same potential to pass on the information. Modifying the SEIZ (susceptible, exposed, infected, skeptic) model, we discover that people choose to retweet strong or weak ties based on the topic. We made two modifications in the model. In the first modification (Model I), we assume that the contact rates of agents in different compartment and the probability of an agent transitioning from one compartment to another are different for strong ties …
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism, Qi Fan, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan Tan
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism, Qi Fan, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan Tan
Research Collection School Of Computing and Information Systems
Automatic discovery of newsworthy themes from sequenced data can relieve journalists from manually poring over a large amount of data in order to find interesting news. In this paper, we propose a novel k -Sketch query that aims to find k striking streaks to best summarize a subject. Our scoring function takes into account streak strikingness and streak coverage at the same time. We study the k -Sketch query processing in both offline and online scenarios, and propose various streak-level pruning techniques to find striking candidates. Among those candidates, we then develop approximate methods to discover the k most representative …
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer
Research Collection School Of Computing and Information Systems
In this survey, we study the impact of Artificial Intelligence (AI) on manufacturing sector. AI methods can be utilized to make new thoughts several ways: by delivering novel mixes of wellknown thoughts; by investigating the capability of theoretical spaces; and by making changes that empower the era of unexplored thoughts. AI will have less trouble in displaying the era of new thoughts than in automating their assessment. We describe the advances that have been made on AI in manufacturing industry. We close with how to overcome the issues in this area.
Ehealthportal: A Social Support Hub For The Active Living Of The Elderly, Di Wang, Ah-Hwee Tan
Ehealthportal: A Social Support Hub For The Active Living Of The Elderly, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
The absolute and relative increases in the number of elderly are evident worldwide, from the most developed countries to the lowest-income regions. The fast demographic transition poses great challenges to the healthcare system and introduces a significant burden to the elderly and their family. To meet the unprecedented challenges of global aging, various aging-in-place (AIP) solutions have been proposed to enable the elderly to live in their own home and community safely, independently and comfortably. Elderly need support in various aspects, such as physical, cognitive, emotional, and social, in their daily life. However, most existing AIP solutions provide support in …
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Elderly Friendliness Evaluation Of Mobile Assistants, Di Wang, Xinjia Yu, Simon Fauvel, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
The rapidly increasing elderly population in many developed and developing countries poses great challenges to elderly care systems. To alleviate the problem of a shrinking workforce to deliver elderly care, using mobile intelligent assistants to lessen the caregivers' workload becomes a promising solution. However, the friendliness of such mobile assistants, which is seldom measured in a quantitative manner, may hinder their acceptance by the elderly users. In this paper, we propose a formalized systematic approach named Elderly Friendliness Evaluation Methodology (EFEM) to measure the elderly friendliness of any product, service or system. Furthermore, we apply EFEM to evaluate the elderly …
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Research Collection School Of Computing and Information Systems
The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to …
Deep Learning On Lie Groups For Skeleton-Based Action Recognition, Zhiwu Huang, C. Wan, T. Probst, Gool L. Van
Deep Learning On Lie Groups For Skeleton-Based Action Recognition, Zhiwu Huang, C. Wan, T. Probst, Gool L. Van
Research Collection School Of Computing and Information Systems
In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature …
Auditing Anti-Malware Tools By Evolving Android Malware And Dynamic Loading Technique, Yinxing Xue, Guozhu Meng, Yang Liu, Tian Huat Tan, Hongxu Chen, Jun Sun, Jie Zhang
Auditing Anti-Malware Tools By Evolving Android Malware And Dynamic Loading Technique, Yinxing Xue, Guozhu Meng, Yang Liu, Tian Huat Tan, Hongxu Chen, Jun Sun, Jie Zhang
Research Collection School Of Computing and Information Systems
Although a previous paper shows that existing antimalware tools (AMTs) may have high detection rate, the report is based on existing malware and thus it does not imply that AMTs can effectively deal with future malware. It is desirable to have an alternative way of auditing AMTs. In our previous paper, we use malware samples from android malware collection GENOME to summarize a malware meta-model for modularizing the common attack behaviors and evasion techniques in reusable features. We then combine different features with an evolutionary algorithm, in which way we evolve malware for variants. Previous results have shown that the …
Mergeable And Revocable Identity-Based Encryption, Shengmin Xu, Guomin Yang, Yi Mu, Willy Susilo
Mergeable And Revocable Identity-Based Encryption, Shengmin Xu, Guomin Yang, Yi Mu, Willy Susilo
Research Collection School Of Computing and Information Systems
Identity-based encryption (IBE) has been extensively studied and widely used in various applications since Boneh and Franklin proposed the first practical scheme based on pairing. In that seminal work, it has also been pointed out that providing an efficient revocation mechanism for IBE is essential. Hence, revocable identity-based encryption (RIBE) has been proposed in the literature to offer an efficient revocation mechanism. In contrast to revocation, another issue that will also occur in practice is to combine two or multiple IBE systems into one system, e.g., due to the merge of the departments or companies. However, this issue has not …
A Secure, Usable, And Transparent Middleware For Permission Managers On Android, Daibin Wang, Haixia Yao, Yingjiu Li, Hai Jin, Deqing Zou, Robert H. Deng
A Secure, Usable, And Transparent Middleware For Permission Managers On Android, Daibin Wang, Haixia Yao, Yingjiu Li, Hai Jin, Deqing Zou, Robert H. Deng
Research Collection School Of Computing and Information Systems
Android’s permission system offers an all-or-nothing choice when installing an app. To make it more flexible and fine-grained, users may choose a popular app tool, called permission manager, to selectively grant or revoke an app’s permissions at runtime. A fundamental requirement for such permission manager is that the granted or revoked permissions should be enforced faithfully. However, we discover that none of existing permission managers meet this requirement due to permission leaks, in which an unprivileged app can exercise certain permissions which are revoked or not-granted through communicating with a privileged app. To address this problem, we propose a secure, …
Hierarchical Functional Encryption For Linear Transformations, Shiwei Zhang, Yi Mu, Guomin Yang, Xiaofen Wang
Hierarchical Functional Encryption For Linear Transformations, Shiwei Zhang, Yi Mu, Guomin Yang, Xiaofen Wang
Research Collection School Of Computing and Information Systems
In contrast to the conventional all-or-nothing encryption, functional encryption (FE) allows partial revelation of encrypted information based on the keys associated with different functionalities. Extending FE with key delegation ability, hierarchical functional encryption (HFE) enables a secret key holder to delegate a portion of its decryption ability to others and the delegation can be done hierarchically. All HFE schemes in the literature are for general functionalities and not very practical. In this paper, we focus on the functionality of linear transformations (i.e. matrix product evaluation). We refine the definition of HFE and further extend the delegation to accept multiple keys. …
Privacy-Preserving K-Time Authenticated Secret Handshakes, Yangguang Tian, Shiwei Zhang, Guomin Yang, Yi Mu, Yong Yu
Privacy-Preserving K-Time Authenticated Secret Handshakes, Yangguang Tian, Shiwei Zhang, Guomin Yang, Yi Mu, Yong Yu
Research Collection School Of Computing and Information Systems
Secret handshake allows a group of authorized users to establish a shared secret key and at the same time authenticate each other anonymously. A straightforward approach to design an unlinkable secret handshake protocol is to use either long-term certificate or one-time certificate provided by a trusted authority. However, how to detect the misusing of certificates by an insider adversary is a challenging security issue when using those approaches for unlinkable secret handshake. In this paper, we propose a novel k-time authenticated secret handshake (k-ASH) protocol where each authorized user is only allowed to use the credential for k times. We …
Automatically Locating Malicious Packages In Piggybacked Android Apps, Li Li, Daoyuan Li, Tegawende Bissyande, Jacques Klein, Haipeng Cai, David Lo, Yves Le Traon
Automatically Locating Malicious Packages In Piggybacked Android Apps, Li Li, Daoyuan Li, Tegawende Bissyande, Jacques Klein, Haipeng Cai, David Lo, Yves Le Traon
Research Collection School Of Computing and Information Systems
To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to …
Fast Adaptation Of Activity Sensing Policies In Mobile Devices, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Dong In Kim
Fast Adaptation Of Activity Sensing Policies In Mobile Devices, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Dong In Kim
Research Collection School Of Computing and Information Systems
With the proliferation of sensors, such as accelerometers,in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user experience. This paper proposes a fast adaptation and learning scheme of activity tracking policies when user statistics are unknown a priori, varying with time, and inconsistent for different users. In our stochastic optimization, user activities are required to be synchronized with a backend under a cellular data limit to avoid overcharges from cellular operators. The mobile device is charged intermittently using wireless or wired charging for receiving the required energy for transmission and sensing …
How To Enable Future Faster Payments? An Evaluation Of A Hybrid Payments Settlement Mechanism, Zhiling Guo, Yuanzhi Huang
How To Enable Future Faster Payments? An Evaluation Of A Hybrid Payments Settlement Mechanism, Zhiling Guo, Yuanzhi Huang
Research Collection School Of Computing and Information Systems
In the era of Fintech innovation and e-commerce, faster settlement of massive retail transactions is crucial for business growth and financial system stability. However, speeding up payments settlement can create periodic liquidity shortfalls to banks which would incur high cost of funds in the settlement process. We propose a new hybrid settlement mechanism design that integrates features of real-time gross settlement, deferred net settlement, and central queue management structure. The hybrid mechanism is managed by an intermediary and is particularly suitable to settle large volume of small-value retail payments. We evaluate the mechanism using computer experiments and simulation. We find …
Cyber Foraging: Fifteen Years Later, Rajesh Krishna Balan, Jason Flinn
Cyber Foraging: Fifteen Years Later, Rajesh Krishna Balan, Jason Flinn
Research Collection School Of Computing and Information Systems
Revisiting Mahadev Satyanarayanan's original vision of cyber foraging and reflecting on the last 15 years of related research, the authors discuss the major accomplishments achieved as well as remaining challenges. They also look to current and future applications that could provide compelling application scenarios for making cyber foraging a widely deployed technology. This article is part of a special issue on pervasive computing revisited.
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Lipyeow Lim, Sougata Sen, Archan Misra, Rajesh Krishna Balan, Youngki Lee
Cloud-Based Query Evaluation For Energy-Efficient Mobile Sensing, Tianli Mo, Lipyeow Lim, Sougata Sen, 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, specifically for the case of context-aware applications that are interested in collective context or events, i.e., events expressed as a set of complex predicates over sensor data from multiple smartphones. We propose a cloud-based query management and optimization framework, called CloQue, that can support thousands of such concurrent queries, executing over a large number of individual smartphones. Our central insight is that the context of different individuals & groups often have significant correlation, and that this correlation can be learned through standard association rule mining on historical data. …
Truly Multi-Modal Youtube-8m Video Classification With Video, Audio, And Text, Zhe Wang, Kingsley Kuan, Mathieu Ravant, Gaurav Manek, Sibo Song, Yuan Fang, Et Al
Truly Multi-Modal Youtube-8m Video Classification With Video, Audio, And Text, Zhe Wang, Kingsley Kuan, Mathieu Ravant, Gaurav Manek, Sibo Song, Yuan Fang, Et Al
Research Collection School Of Computing and Information Systems
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text. The newly introduced text data is termed as YouTube-8M-Text. We present a classification framework for the joint use of text, visual and audio features, and conduct an extensive set of experiments to quantify the benefit that …
Does Director Interlock Impact The Diffusion Of Accounting Method Choice?, Jie Han, Nan Hu, Ling Liu, Gaoliang Tian
Does Director Interlock Impact The Diffusion Of Accounting Method Choice?, Jie Han, Nan Hu, Ling Liu, Gaoliang Tian
Research Collection School Of Computing and Information Systems
This paper examines the influence of director interlock on firms' discrete accounting method choices from the perspective of behavior diffusion. We argue that firm managers will imitate their interlocked-partner firm's accounting method choices when choosing their own accounting methods. We find that when there is an interlock relationship between two firms, their accounting method choices, including inventory and depreciation methods, are similar to each other, indicating that accounting method choices can diffuse across firms through director interlock. In addition, such similarity is greater the longer the interlock relationship between the two firms is and as uncertainty increases. Further, the interlock …
Sparsity Based Reflection Removal Using External Patch Search, Renjie Wan, Boxin Shi, Ah-Hwee Tan, Alex C. Kot
Sparsity Based Reflection Removal Using External Patch Search, Renjie Wan, Boxin Shi, Ah-Hwee Tan, Alex C. Kot
Research Collection School Of Computing and Information Systems
Reflection removal aims at separating the mixture of the desired background scenes and the undesired reflections, when the photos are taken through the glass. It has both aesthetic and practical applications which can largely improve the performance of many multimedia tasks. Existing reflection removal approaches heavily rely on scene priors such as separable sparse gradients brought by different levels of blur, and they easily fail when such priors are not observed in many real scenes. Sparse representation models and nonlocal image priors have shown their effectiveness in image restoration with self similarity. In this work, we propose a reflection removal …
Effect Of Timing And Source Of Online Product Recommendations: An Eye-Tracking Study, Yan Shi, Qing Zeng, Fiona Fui-Hoon Nah, Chuan-Hoo Tan, Choon Ling Sia, Keng Siau, Jiaqi Yan
Effect Of Timing And Source Of Online Product Recommendations: An Eye-Tracking Study, Yan Shi, Qing Zeng, Fiona Fui-Hoon Nah, Chuan-Hoo Tan, Choon Ling Sia, Keng Siau, Jiaqi Yan
Research Collection School Of Computing and Information Systems
Online retail business has become an emerging market for almost all business owners. Online recommender systems provide better service to consumers during their decision making processes. In this study, a controlled lab experiment was conducted to assess the effect of recommendation timing (early, mid, and late) and recommendation source (expert reviews vs. consumer reviews) on online consumers’ interest and attention. Eye-tracking data was extracted from the experiment and analyzed. The results suggest that consumers show more interest in recommendation based on consumer reviews than expert reviews. Earlier recommendations do not receive greater attention than later recommendations.
A Weighted Maximum Matching Algorithm For Influence Maximization And Structural Controllability, Giorgio Sartor, Yeow Khiang Chia, Laura Wynter, Justin Ruths
A Weighted Maximum Matching Algorithm For Influence Maximization And Structural Controllability, Giorgio Sartor, Yeow Khiang Chia, Laura Wynter, Justin Ruths
Research Collection School Of Computing and Information Systems
Structural control and influence maximization on networks both admit the problem of selecting a particular subset of nodes. In structural control, the subset of nodes should guarantee the controllability of the network (in the usual sense) for almost any combination of weights. In influence maximization, given a diffusion process over the network, the chosen subset of nodes (of a given cardinality) should produce the greatest diffusive influence over the rest of the network. While structural control exploits only the structure of the network, influence maximization depends both on the structure and the weights of the edges. We modify an algorithm …