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Influence Maximization In Public Private Social Networks
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Book Synopsis Influence Maximization in Public Private Social Networks by : Anusha Dudi
Download or read book Influence Maximization in Public Private Social Networks written by Anusha Dudi and published by . This book was released on 2018 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: The public-private model is very relevant to social networks today, such as Facebook, Twitter, Google+, etc. In these social networks, some information is public, and some information is private to each user because of privacy settings. In this model, the network is different from each user's perspective, i.e., the union of the public graph and the user's private graph. Algorithmic analysis on such networks has to be adapted to each user's perspective to ensure privacy guarantees. In this work, we propose an Influence Maximization algorithm, to find a most influential seed set of a given size in public-private model of social networks. This algorithm is extended from a sketch based influence maximization algorithm. The proposed algorithm, while upholding privacy requirements, gives better influence estimate on networks having privacy settings.
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.
Book Synopsis New Approaches to Maximizing Influence in Large-scale Social Networks by : David Scott Hunter (Ph.D.)
Download or read book New Approaches to Maximizing Influence in Large-scale Social Networks written by David Scott Hunter (Ph.D.) and published by . This book was released on 2020 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread adoption of social media in today’s society, the problem of identifying the most influential individuals whose adoption of a product or action will spread maximally in the network is of increased practical significance. This thesis considers new strategies and methods for this problem, which is known as the influence maximization problem, focusing on a setting where the influence is determined by some function of user opinions. In the first chapter, we introduce a new model of opinion dynamics that is motivated by research in both social psychology and political science. We present a series of theoretical results concerning the convergence of the opinions to an equilibrium, including conditions under which convergence to a fixed point occurs, an explicit characterization of the equilibrium, and the rate of convergence to the equilibrium. In the second chapter, we propose new approaches to the influence maximization problem in a social network when the dynamics adhere to the model in the first chapter. We consider applying these methods on several large-scale real-world social networks. In doing so, we attempt to measure the validity of the model we propose, consider estimating the relative importance of some special users via a centrality function approach, and highlight the computational efficiency of our influence maximization methods. In the final chapter, we introduce an alternative approach to maximizing influence in a social network that has as a solution a dynamic policy that considers when, what, and with whom an agent communicates. We motivate the necessity for a dynamic policy solution by highlighting some realistic behaviors that make modeling and analyzing real-world dynamics difficult. By leveraging reinforcement learning, we learn policies that account for some of these realistic behaviors and find that these policies exhibit impressive performance on large-scale networks.
Book Synopsis Influence and Behavior Analysis in Social Networks and Social Media by : Mehmet Kaya
Download or read book Influence and Behavior Analysis in Social Networks and Social Media written by Mehmet Kaya and published by Springer. This book was released on 2018-12-11 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely book focuses on influence and behavior analysis in the broader context of social network applications and social media. Twitter accounts of telecommunications companies are analyzed. Rumor sources in finite graphs with boundary effects by message-passing algorithms are identified. The coherent, state-of-the-art collection of chapters was initially selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. Original chapters coming from outside of the meeting round out the coverage. The result will appeal to researchers and students working in social network and social media analysis.
Book Synopsis Optimal Influence in Social Networks by : Wen Xu
Download or read book Optimal Influence in Social Networks written by Wen Xu and published by . This book was released on 2014 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social networks, which consist of individuals and relationships between them, are now popular communication platforms for the public. Especially, online social networks such as Facebook and Twitter, have emerged as an important medium for the widespread distribution of news, opinions or rumors in various social events. In this dissertation, we study two types of problems related to influence diffusion in social networks. First, to maximize the product adoption in social networks via the word-of-mouth effect, we study the problem of influence maximization, in which a small set of the most influential users are identified so that their aggregated influence in the network is maximized. We recast the problem to a weighted maximum cut based framework, which analyzes the influence flow among users in the network. Since the problem is NP-hard, we solve it by a semi-definite program based algorithm, which provides about 0.8 approximation of optimal solution with theoretical guarantees. Second, we study the inverse problem of influence diffusion, locating sources of information diffusion, which has important applications such as locating sources of epidemics or rumors in networks. Suppose the spread of rumor follows the probabilistic model, for example, Independent Cascade (IC), without any text or content information, we develop a reachability based score for ranking the importance of nodes as the rumor source. To extend our work, we consider detecting multiple rumors from a deterministic point on general graphs. The problem of Multiple Rumor Source Detection (MRSD) is formally defined as finding a Set Resolving Set (SRS) with the smallest cardinality in the network. Using an analysis framework of submodular functions, we propose a highly efficient greedy algorithm for the MRSD problem, which is polynomial time under some reasonable constraints, that is, there is a constant upper bound for the number of rumor sources.
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.
Book Synopsis Opinion Dynamics and the Evolution of Social Power in Social Networks by : Mengbin Ye
Download or read book Opinion Dynamics and the Evolution of Social Power in Social Networks written by Mengbin Ye and published by Springer. This book was released on 2019-02-19 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book uses rigorous mathematical analysis to advance opinion dynamics models for social networks in three major directions. First, a novel model is proposed to capture how a discrepancy between an individual’s private and expressed opinions can develop due to social pressures that arise in group situations or through extremists deliberately shaping public opinion. Detailed theoretical analysis of the final opinion distribution is followed by use of the model to study Asch’s seminal experiments on conformity, and the phenomenon of pluralistic ignorance. Second, the DeGroot-Friedkin model for evolution of an individual’s social power (self-confidence) is developed in a number of directions. The key result establishes that an individual’s initial social power is forgotten exponentially fast, even when the network changes over time; eventually, an individual’s social power depends only on the (changing) network structure. Last, a model for the simultaneous discussion of multiple logically interdependent topics is proposed. To ensure that a consensus across the opinions of all individuals is achieved, it turns out that the interpersonal interactions must be weaker than an individual’s introspective cognitive process for establishing logical consistency among the topics. Otherwise, the individual may experience cognitive overload and the opinion system becomes unstable. Conclusions of interest to control engineers, social scientists, and researchers from other relevant disciplines are discussed throughout the thesis with support from both social science and control literature.
Book Synopsis Influence Maximization in Social Networks by : Kangkang Wu
Download or read book Influence Maximization in Social Networks written by Kangkang Wu and published by . This book was released on 2016 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the social network era, every decision an individual makes, whether it is watching a movie or purchasing a book, is influenced by his or her personal network to a certain degree. This thesis investigates the power of the ?word-of-mouth? effect within social networks. Given a network G = (V, E, t) where t(v) denotes the threshold of node v, we model the spread of influence as follows. Influence propagates deterministically in discrete steps. An influenced node u exerts a fixed amount of influence bu,w on any neighbor w. For any uninfluenced node v, if the total amount of influence it receives from all its already influenced neighbors at time step t− 1 is at least t(v), node v will be influenced in step t. Given a social network G, we study the problem of introducing an already activated external influencer v into the network, and choosing links from v to nodes in G that can maximize the spread of influence in G. We study two problems: the Minimum Links problem, which is to choose the minimum number of links that can activate the entire network, and the Maximum Influence problem, which is to choose k neighbors that will maximize the size of the influenced set. We prove that the Maximum Influence problem is NP-hard, even for bipartite graphs in which thresholds of all nodes are either one or two. We also study both problems in paths, rings, trees and cliques, and we give polynomial time algorithms that find optimal solutions to both problems for all these topologies.
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.
Book Synopsis Community Detection and Influence Maximization in Online Social Networks by : Kanna Gharib Falahi
Download or read book Community Detection and Influence Maximization in Online Social Networks written by Kanna Gharib Falahi and published by . This book was released on 2014 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes.
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.
Book Synopsis Maximizing Influence Through Information-Overloaded Online Social Networks by : Aaron R. Sun
Download or read book Maximizing Influence Through Information-Overloaded Online Social Networks written by Aaron R. Sun and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online social communities have become an important communication channel for people to share and discover information. Pieces of information spread within the community via the underlying social network, from one individual to another. However, with the unprecedented ease and low cost of communication provided by online systems, information overload emerges as a negative factor that potentially threatens the effectiveness of communication. As such, traditional single-message based diffusion models, such as Independent Cascade Model (ICM), are inadequate for describing the role of information overload. We then proposed an extended version of ICM (EICM) that explicitly takes the message multiplicity into account, after examining the message exchange patterns observed from a real online social community. We extensively evaluated this new diffusion model and compared with standard ICM in addressing one fundamental algorithmic problem related to viral marketing: How to select a set of network nodes/individuals to facilitate information diffusion and maximize influence? The evaluation results obtained from using both real and simulated data sets show that our approach results in node-selection heuristics outperforming well-studied notions of various centrality measures based on the ICM. The study and findings presented in this research have important managerial implications. In particular, from a viral marketing perspective, information overload effect should be recognized in order for campaign managers to build advantages in their influence maximization decisions.
Book Synopsis ISGP: Influence Maximization on Dynamic Social Networks Using Influence SubGraph Propagation by :
Download or read book ISGP: Influence Maximization on Dynamic Social Networks Using Influence SubGraph Propagation written by and published by . This book was released on 2021 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis K-medians Approach for Influence Maximization in Large Social Networks by : 鄭立揚
Download or read book K-medians Approach for Influence Maximization in Large Social Networks written by 鄭立揚 and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis PRICAI 2016: Trends in Artificial Intelligence by : Richard Booth
Download or read book PRICAI 2016: Trends in Artificial Intelligence written by Richard Booth and published by Springer. This book was released on 2016-08-09 with total page 841 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Pacific Rim Conference on Artificial Intelligence, PRICAI 2016, held in Phuket, Thailand, in August 2016. The 53 regular papers and 15 short papers presented in this volume were carefully reviewed and selected from 161 submissions. Pricai covers a wide range of topics such as AI foundations; applications of AI; semantic web; information retrieval; constraint satisfaction; multimodal interaction; knowledge representation; social networks; ad-hoc networks; algorithms; software architecture; machine learning; and smart modeling and simulation.
Book Synopsis Engineering Scalable Influence Maximization by : Akshay Khot
Download or read book Engineering Scalable Influence Maximization written by Akshay Khot and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, social networks have become an important part of our daily lives. Billions of people daily use Facebook and other prominent social media networks. This makes them an effective medium for advertising and marketing. Finding the most influential users in a social network is an interesting problem in this domain, as promoters can reach large audiences by targeting these few influential users. This is the influence maximization problem, where we want to maximize the influence spread using as few users as possible. As these social networks are huge, scalability and runtime of the algorithm to find the most influential users is of high importance. We propose innovative improvements in the implementation of the state-of-the-art sketching algorithm for influence analysis on social networks. The primary goal of this thesis is to make the algorithm fast, efficient, and scalable. We devise new data structures to improve the speed of the sketching algorithm. We introduce the compressed version of the algorithm which reduces the space taken in the memory by the data structures without compromising the runtime. By performing extensive experiments on real-world graphs, we prove that our algorithms are able to compute the most influential users within a reasonable amount of time and space on a consumer grade machine. These modifications can further be enhanced to reflect the constantly updating social media graphs to provide accurate estimations in real-time.
Book Synopsis Security and Privacy Preserving in Social Networks by : Richard Chbeir
Download or read book Security and Privacy Preserving in Social Networks written by Richard Chbeir and published by Springer Science & Business Media. This book was released on 2013-10-17 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume aims at assessing the current approaches and technologies, as well as to outline the major challenges and future perspectives related to the security and privacy protection of social networks. It provides the reader with an overview of the state-of-the art techniques, studies, and approaches as well as outlining future directions in this field. A wide range of interdisciplinary contributions from various research groups ensures for a balanced and complete perspective.
