[PDF] Influence Optimization Problems In Social Networks eBook

Author : Shuyang Gu
Publisher :
Page : pages
File Size : 39,55 MB
Release : 2020
Category : Influence (Psychology)
ISBN :

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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.

Author : Wen Xu
Publisher : Springer Nature
Page : 129 pages
File Size : 14,92 MB
Release : 2020-01-29
Category : Mathematics
ISBN : 303037775X

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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.

Author : Smita Ghosh
Publisher :
Page : 0 pages
File Size : 46,17 MB
Release : 2020
Category : Collective behavior
ISBN :

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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.

Author : Yuqing Zhu
Publisher :
Page : 204 pages
File Size : 50,98 MB
Release : 2014
Category : Approximation algorithms
ISBN :

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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.

Author : Guangmo Tong
Publisher :
Page : pages
File Size : 16,8 MB
Release : 2018
Category : Information networks
ISBN :

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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.

Author : Wei Chen
Publisher : Springer Nature
Page : 161 pages
File Size : 45,19 MB
Release : 2022-05-31
Category : Computers
ISBN : 3031018508

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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.

Author : Rajendra Akerkar
Publisher : Jones & Bartlett Publishers
Page : 375 pages
File Size : 35,68 MB
Release : 2009-08-25
Category : Computers
ISBN : 1449662706

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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.

Author : Hisao Ishibuchi
Publisher : Springer Nature
Page : 781 pages
File Size : 50,17 MB
Release : 2021-03-24
Category : Computers
ISBN : 3030720624

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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.

Author : Panos M. Pardalos
Publisher : Springer
Page : 330 pages
File Size : 22,25 MB
Release : 2018-12-04
Category : Mathematics
ISBN : 3319991426

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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.

Author : Nikhil Pandey
Publisher :
Page : pages
File Size : 13,38 MB
Release : 2012
Category :
ISBN :

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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.