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

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

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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.

Influence Maximization in Social Networks

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ISBN 13 :
Total Pages : 117 pages
Book Rating : 4.:/5 (111 download)

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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.

Optimal Social Influence

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

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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.

Optimal Influence in Social Networks

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ISBN 13 :
Total Pages : 206 pages
Book Rating : 4.:/5 (913 download)

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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.

Complex Networks and Their Applications VII

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Publisher : Springer
ISBN 13 : 3030054144
Total Pages : 677 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Complex Networks and Their Applications VII by : Luca Maria Aiello

Download or read book Complex Networks and Their Applications VII written by Luca Maria Aiello and published by Springer. This book was released on 2018-12-05 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Social Network Data Analytics

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Publisher : Springer Science & Business Media
ISBN 13 : 1441984623
Total Pages : 508 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Social Network Data Analytics by : Charu C. Aggarwal

Download or read book Social Network Data Analytics written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2011-03-18 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

New Approaches to Maximizing Influence in Large-scale Social Networks

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ISBN 13 :
Total Pages : 182 pages
Book Rating : 4.:/5 (12 download)

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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.

Influence Maximization in Public Private Social Networks

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ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (16 download)

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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.

Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023)

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Publisher : Springer Nature
ISBN 13 : 303144146X
Total Pages : 379 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023) by : Katerina Kabassi

Download or read book Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023) written by Katerina Kabassi and published by Springer Nature. This book was released on 2023-10-24 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the research findings presented at the 3rd International Conference on Novel & Intelligent Digital Systems (NiDS 2023). NiDS 2023 held in Athens, Greece, during September 28–29, 2023, under the auspices of the Institute of Intelligent Systems. The conference was implemented hybrid, allowing participants to attend it either online or onsite. NiDS 2023 places significant importance on the innovations within intelligent systems and the collaborative research that empowers and enriches artificial intelligence (AI) in software development. It encourages high-quality research, establishing a forum for investigating the obstacles and cutting-edge breakthroughs in AI. The conference is designed for experts, researchers, and scholars in artificial and computational intelligence, as well as computer science in general, offering them the opportunity to delve into relevant, interconnected, and mutually complementary fields. By facilitating the exchange of ideas, the conference strengthens and broadens the network of researchers, academics, and industry representatives.

Influence Optimization Problems in Social Networks

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

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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.

Influence Maximization Over a Network

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ISBN 13 :
Total Pages : 132 pages
Book Rating : 4.:/5 (973 download)

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Book Synopsis Influence Maximization Over a Network by : Zied Ben Chaouch

Download or read book Influence Maximization Over a Network written by Zied Ben Chaouch and published by . This book was released on 2016 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of maximizing the spread of an opinion inside a social network has been investigated extensively during the past decade. The importance of this problem in applications such as marketing has been amplified by the major expansion of online social networks. In this thesis, we study opinion control policies, first under a broad class of deterministic dynamics governing the interactions inside a network, and then under the classical "Voter Model". In the former case, we design a policy that a controller can follow in order to spread an opinion inside a network with the smallest possible cost. In the latter case, we consider networks whose underlying graph is the d-dimensional integer torus Zd/n, and we design policies that minimize the expected time until the network reaches a consensus. We also show that, in dimension d >/= 2, dynamic policies do not perform significantly better than static policies, while, in dimension d = 1, optimal dynamic policies perform much better than optimal static policies..

Big Data over Networks

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Publisher : Cambridge University Press
ISBN 13 : 1107099005
Total Pages : 459 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Big Data over Networks by : Shuguang Cui

Download or read book Big Data over Networks written by Shuguang Cui and published by Cambridge University Press. This book was released on 2016-01-14 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examines the crucial interaction between big data and communication, social and biological networks using critical mathematical tools and state-of-the-art research.

Complex Networks & Their Applications XII

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Publisher : Springer Nature
ISBN 13 : 3031535030
Total Pages : 490 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Complex Networks & Their Applications XII by : Hocine Cherifi

Download or read book Complex Networks & Their Applications XII written by Hocine Cherifi and published by Springer Nature. This book was released on with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Prediction and Influence Maximization in Location-based Social Networks

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Publisher :
ISBN 13 :
Total Pages : 202 pages
Book Rating : 4.:/5 (93 download)

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Book Synopsis Prediction and Influence Maximization in Location-based Social Networks by : Zhe Zhu

Download or read book Prediction and Influence Maximization in Location-based Social Networks written by Zhe Zhu and published by . This book was released on 2012 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choosing the right users to deliver the coupons and maximizing the influence spread is also an important problem in LBSN, which is called influence maximization problem. In practice speed is an important issue to solve the influence maximization problem. In this thesis, we study the influence maximization problem in location-based social networks and propose a scalable partition approach to solve the influence maximization problem efficiently. Experimental results show that our partition approach achieves quite similar influence spread performance with the original influence maximization approach, while running much faster.

Engineering Scalable Influence Maximization

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

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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.

The Mim

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Publisher : Independently Published
ISBN 13 :
Total Pages : 34 pages
Book Rating : 4.4/5 (583 download)

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Book Synopsis The Mim by : Van Dyk

Download or read book The Mim written by Van Dyk and published by Independently Published. This book was released on 2021-08-22 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world can be a scary place. Filled with things we can't always understand, or even explain. This is just a little story about a young one's experience, and most importantly ... his hope.

Social Media Mining

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Publisher : Cambridge University Press
ISBN 13 : 1107018854
Total Pages : 337 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Social Media Mining by : Reza Zafarani

Download or read book Social Media Mining written by Reza Zafarani and published by Cambridge University Press. This book was released on 2014-04-28 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.