Optimization Problems For Maximizing Influence In Social Networks

Download Optimization Problems For Maximizing Influence In Social Networks full books in PDF, epub, and Kindle. Read online Optimization Problems For Maximizing Influence In Social Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Optimization Problems for Maximizing Influence in Social Networks

Author : Smita Ghosh
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

DOWNLOAD NOW!

Book Synopsis Optimization Problems for Maximizing Influence in Social Networks by : Smita Ghosh

Download or read book Optimization Problems for Maximizing Influence in Social Networks written by Smita Ghosh and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Networks have become very popular in the past decade. They started as platforms to stay connected with friends and family living in different parts of the world, but have evolved into so much more, resulting in Social Network Analysis (SNA) becoming a very popular area of research. One popular problem under the umbrella of SNA is Influence Maximization (IM), which aims at selecting k initially influenced nodes (users) in a social network that will maximize the expected number of eventually-influenced nodes (users) in the network. Influence maximization finds its application in many domains, such as viral marketing, content maximization, epidemic control, virus eradication, rumor control and misinformation blocking. In this dissertation, we study various variations of the IM problem such as Composed Influence Maximization, Group Influence Maximization, Profit Maximization in Groups and Rumor Blocking Problem in Social Networks. We formulate objective functions for these problems and as most of them are NP-hard, we focus on finding methods that ensure efficient estimation of these functions. The two main challenges we face are submodularity and scalibility. To design efficient algorithms, we perform simulations with sampling techniques to improve the effectiveness of our solution approach.

Optimal Social Influence

Author : Wen Xu
Publisher : Springer Nature
ISBN 13 : 303037775X
Total Pages : 129 pages
Book Rating : 4.0/5 (33 download)

DOWNLOAD NOW!

Book Synopsis Optimal Social Influence by : Wen Xu

Download or read book Optimal Social Influence written by Wen Xu and published by Springer Nature. This book was released on 2020-01-29 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained book describes social influence from a computational point of view, with a focus on recent and practical applications, models, algorithms and open topics for future research. Researchers, scholars, postgraduates and developers interested in research on social networking and the social influence related issues will find this book useful and motivating. The latest research on social computing is presented along with and illustrations on how to understand and manipulate social influence for knowledge discovery by applying various data mining techniques in real world scenarios. Experimental reports, survey papers, models and algorithms with specific optimization problems are depicted. The main topics covered in this book are: chrematistics of social networks, modeling of social influence propagation, popular research problems in social influence analysis such as influence maximization, rumor blocking, rumor source detection, and multiple social influence competing.

Influence Optimization Problems in Social Networks

Author : Shuyang Gu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Influence Optimization Problems in Social Networks by : Shuyang Gu

Download or read book Influence Optimization Problems in Social Networks written by Shuyang Gu and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Online social networks have been developing and prosperous during the last two decades, my dissertation focus on the study of social influence. Several practical problems about social influence are formulated as optimization problems. First, users of online social networks such as Twitter, Instagram have a nature of expanding social relationships. Thus, one important social network service is to provide potential friends to a user that he or she might be interested in, which is called friend recommendation. Different from friend recommendation, which is a passive way for an user to connect with a potential friend, in my work, I tackle a different problem named active friending as an optimization problem about how to friend a person in social networks taking advantage of social influence to increase the acceptance probability by maximizing mutual friends influence. Second, the influence maximization problem has been studied extensively with the development of online social networks. Most of the existing works focus on the maximization of influence spread under the assumption that the number of influenced users determines the success of product promotion. However, the profit of some products such as online game depends on the interactions among users besides the number of users. We take both the number of active users and the user-to-user interactions into account and propose the interaction-aware influence maximization problem. Furthermore, due to the uncertainty in edge probability estimates in social networks, we propose the robust profit maximization problem to have the best solution in the worst case of probability settings.

Optimization in Social Networks

Author : Yuqing Zhu
Publisher :
ISBN 13 :
Total Pages : 204 pages
Book Rating : 4.:/5 (913 download)

DOWNLOAD NOW!

Book Synopsis Optimization in Social Networks by : Yuqing Zhu

Download or read book Optimization in Social Networks written by Yuqing Zhu and published by . This book was released on 2014 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks have shown increasing popularity in real-world applications. In this dissertation, I study several optimization problems in social networks. In Chapter 1, I propose an approximation algorithm for influence maximization problem in social networks which works better than the start-of-arts under certain circumstances. In Chapter 2, noticing that for a company, the profit and influence are often different, I propose the balanced influence and profit (BIP) problem and design effective algorithms. In Chapter 3, I propose a new influence diffusion model - Timeliness Independent Cascade (TIC) for the case where multiple companies spread their influence and compete each other in a social network. I present the FairInf problem aiming at giving different companies fair influence spreads under TIC model. Several algorithms are designed for FairInf problem. In Chapter 4, a new partitioning method for social networks has been devised. This method is based on the mutual relationship between each pair of individuals in the social network, and works better than existing partitioning strategy on real world datasets.

Knowledge-Based Systems

Author : Rajendra Akerkar
Publisher : Jones & Bartlett Publishers
ISBN 13 : 1449662706
Total Pages : 375 pages
Book Rating : 4.4/5 (496 download)

DOWNLOAD NOW!

Book Synopsis Knowledge-Based Systems by : Rajendra Akerkar

Download or read book Knowledge-Based Systems written by Rajendra Akerkar and published by Jones & Bartlett Publishers. This book was released on 2009-08-25 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.

Information and Influence Propagation in Social Networks

Author : Wei Chen
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627051163
Total Pages : 179 pages
Book Rating : 4.6/5 (27 download)

DOWNLOAD NOW!

Book Synopsis Information and Influence Propagation in Social Networks by : Wei Chen

Download or read book Information and Influence Propagation in Social Networks written by Wei Chen and published by Morgan & Claypool Publishers. This book was released on 2013-10-01 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.

Proceedings of the 20th International Conference Companion on World Wide Web

Author : S. Sadagopan
Publisher :
ISBN 13 :
Total Pages : 524 pages
Book Rating : 4.:/5 (112 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of the 20th International Conference Companion on World Wide Web by : S. Sadagopan

Download or read book Proceedings of the 20th International Conference Companion on World Wide Web written by S. Sadagopan and published by . This book was released on 2011 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Evolutionary Multi-Criterion Optimization

Author : Hisao Ishibuchi
Publisher : Springer Nature
ISBN 13 : 3030720624
Total Pages : 781 pages
Book Rating : 4.0/5 (37 download)

DOWNLOAD NOW!

Book Synopsis Evolutionary Multi-Criterion Optimization by : Hisao Ishibuchi

Download or read book Evolutionary Multi-Criterion Optimization written by Hisao Ishibuchi and published by Springer Nature. This book was released on 2021-03-24 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021. The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.

Critical Cliques and Their Application to Influence Maximization in Online Social Networks

Author : Nikhil Pandey
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (812 download)

DOWNLOAD NOW!

Book Synopsis Critical Cliques and Their Application to Influence Maximization in Online Social Networks by : Nikhil Pandey

Download or read book Critical Cliques and Their Application to Influence Maximization in Online Social Networks written by Nikhil Pandey and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph decompositions have useful applications in optimization problems that are categorized as NP-Hard. Modular Decomposition of a graph is a technique to decompose the graph into non-overlapping modules. A module M of an undirected graph G = (V, E) is commonly defined as a set of vertices such that any vertex outside of M is either adjacent or non-adjacent to all vertices in M . By the theory of modular decomposition, the modules can be categorized as parallel, series or prime modules. Series modules which are maximal and are also cliques are termed as simple series modules or critical cliques. There are modular decomposition algorithms that can be used to decompose the graph into modules and obtain critical cliques. In this current research, we present a new algorithm to decompose the graph into critical cliques without applying the process of modular decomposition. Given a simple, undirected graph G = (V, E), the runtime complexity of our proposed algorithm is O(V + E) under certain input constraints. Thus, one of our main contributions is to propose a novel algorithm for decomposing a simple, undirected graph directly into critical cliques. We apply the idea of critical cliques to propose a new way for solving the influence maximization problem in online social networks. Influence maximization in online social networks is the problem of identifying a small, initial set of influential individuals which can influence the maximum number of individuals in the network. In this research, we propose a new model of online social networks based on the notion of critical cliques. We utilize the properties of critical cliques to assign parameters for our proposed model and select an initial set of activation nodes. We then simulate the influence propagation process in the online social network using our proposed model and experimentally compare our approach to the greedy algorithm proposed by Kempe, Kleinberg and Tardos. Our main contribution in the influence maximization research is to propose a new model of online social network taking into account the structural properties of the social network graph and a new, faster algorithm for determining the initial set of influential individuals in the online social network.

Study in Big Data Harnessing and Related Problems

Author : Rong Jin
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

DOWNLOAD NOW!

Book Synopsis Study in Big Data Harnessing and Related Problems by : Rong Jin

Download or read book Study in Big Data Harnessing and Related Problems written by Rong Jin and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks, such as Facebook and Twitter, have provided incredible opportunities for social communication between web users around the world. Social network analysis is an important problem in data harnessing. The analysis of social networks helps summarizing the interests and opinions of users (nodes), discovering patterns from the interactions (edges) between users, and mining the events that take place in online platforms. The information obtained by analyzing social networks could be especially valuable for many applications. Some typical examples include online advertisement targeting, viral marketing, personalized recommendation, health social media, social influence analysis, and citation network analysis. In this dissertation, we study two types of applications emerging from modern online social platforms in the view of social influence. One is influence maximization(IM) problem from a discount-based online viral marketing scenario, which aims at maximizing influence in the adoption of target products, and the other is online rumor source detection problem, in which the spread of misinformation is supposed to be minimized and the source is expected to be detected. We formulate them as set function optimization problems and design solutions with performance guarantees. In study of set function optimization, there is a challenge coming from the submodularity of objective function. That is, some of the practical problems are not submodular or supermodular, the existing greedy strategy cannot be directly applied to problems to get a guaranteed approximate solution. To solve those non-submodular and non-supermodular problems, one method called DS decomposition has been considered, in which given a set function, we decompose it to be representable as a difference between submodular functions. Based on this method, we further study a problem about how to find a DS decomposition efficiently and effectively. Then we propose a generalized framework that is made up of our novel algorithms under deterministic version and random version respectively to solve maximization of DS decomposition and show their performances under various combinatorial settings. In addition, we discuss our findings on the role of black-box, that has been an important component in study of computational complexity theory as well as has been used for establishing the hardness of problems, about its implied power and limitations in study of data-driven computation for proving solutions to some computational problems.

Optimization Problems in Social Networks

Author : Guangmo Tong
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (15 download)

DOWNLOAD NOW!

Book Synopsis Optimization Problems in Social Networks by : Guangmo Tong

Download or read book Optimization Problems in Social Networks written by Guangmo Tong and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks have become the dominant platform for daily communication, social activities and viral marketing. The past years have witnessed a drastic increase in the population of social network users. On one hand, we aim at fully taking the advantage of social networks such that, for example, the effect of the online advertising can be maximized or the expectation of the users can be satisfied. On the other hand, negative impact resulted by social networks should be constrained. For example, to limit the spread of misinformation or to protect the privacy of online users. In this dissertation, we study the problems emerging from modern online social systems, from the view of information diffusion. Based on different information diffusion models, we study several problems regarding viral marketing, online friending, rumor blocking, etc. We formulate the considered problems as optimization problems and design solutions with performance guarantees. As the considered problems are all NP-hard, we focus on the analysis of approximation result. Another challenge comes from the high scale of real social network and the #P-hard nature of computing information influence. In order to provide efficient algorithms with respect to running time, we adopt effective sampling techniques to improve the efficiency of the solutions.

Information and Influence Propagation in Social Networks

Author : Wei Chen
Publisher : Springer Nature
ISBN 13 : 3031018508
Total Pages : 161 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Information and Influence Propagation in Social Networks by : Wei Chen

Download or read book Information and Influence Propagation in Social Networks written by Wei Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.

Open Problems in Optimization and Data Analysis

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 3319991426
Total Pages : 330 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Open Problems in Optimization and Data Analysis by : Panos M. Pardalos

Download or read book Open Problems in Optimization and Data Analysis written by Panos M. Pardalos and published by Springer. This book was released on 2018-12-04 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.

Big Data Optimization: Recent Developments and Challenges

Author : Ali Emrouznejad
Publisher : Springer
ISBN 13 : 3319302655
Total Pages : 492 pages
Book Rating : 4.3/5 (193 download)

DOWNLOAD NOW!

Book Synopsis Big Data Optimization: Recent Developments and Challenges by : Ali Emrouznejad

Download or read book Big Data Optimization: Recent Developments and Challenges written by Ali Emrouznejad and published by Springer. This book was released on 2016-05-26 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.

Several Practical Models and Their Approximate Solutions in Social Networks

Author : Jianxiong Guo
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (128 download)

DOWNLOAD NOW!

Book Synopsis Several Practical Models and Their Approximate Solutions in Social Networks by : Jianxiong Guo

Download or read book Several Practical Models and Their Approximate Solutions in Social Networks written by Jianxiong Guo and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The online social platforms developed due to the popularity of the Internet and have become the mainstream way for daily communication as well as information spreading. The users and relationships between users in these social platforms can be abstracted by a social graph (social network). The most typical application in social networks is viral marketing, which takes the advantage of online advertisements to make information spread to more audiences in a short time. At the same time, we have to constrain the negative impact of misinformation spread. They can be formulated as combinatorial optimization problems in the directed graph, such as influence maximization, profit maximization, and rumor blocking. Influence spread can be characterized by different diffusion models. However, the existing models cannot portray the colorful real world. In this dissertation, we propose a series of new diffusion models, including a complementary products model, a multi-feature diffusion model, a k-hop collaborate game model, and an influence balancing model to adapt to some realistic applications in social networks, also study their related algorithmic problems. Because of their NP-hardness, we focus on designing efficient approximate algorithms. To overcome the #P-hardness of computing objective functions, we adopt the techniques of reverse influence sampling to improve efficiency without losing the approximation ratio.

Nonlinear Combinatorial Optimization

Author : Ding-Zhu Du
Publisher : Springer
ISBN 13 : 3030161943
Total Pages : 315 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Nonlinear Combinatorial Optimization by : Ding-Zhu Du

Download or read book Nonlinear Combinatorial Optimization written by Ding-Zhu Du and published by Springer. This book was released on 2019-05-31 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.

Recent Advances in Intelligent Informatics

Author : Sabu M. Thampi
Publisher : Springer Science & Business Media
ISBN 13 : 3319017780
Total Pages : 461 pages
Book Rating : 4.3/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Recent Advances in Intelligent Informatics by : Sabu M. Thampi

Download or read book Recent Advances in Intelligent Informatics written by Sabu M. Thampi and published by Springer Science & Business Media. This book was released on 2013-07-30 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Symposium on Intelligent Informatics (ISI 2013) held in Mysore, India during August 23-24, 2013. The 47 revised papers presented were carefully reviewed and selected from 126 initial submissions. The papers are organized in topical sections on pattern recognition, signal and image processing; data mining, clustering and intelligent information systems; multi agent systems; and computer networks and distributed systems. The book is directed to the researchers and scientists engaged in various fields of intelligent informatics.