Temporal Modeling Of Information Diffusion In Online Social Networks

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Temporal Modeling of Information Diffusion in Online Social Networks

Author : Guolin Niu
Publisher : Open Dissertation Press
ISBN 13 : 9781361349762
Total Pages : pages
Book Rating : 4.3/5 (497 download)

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Book Synopsis Temporal Modeling of Information Diffusion in Online Social Networks by : Guolin Niu

Download or read book Temporal Modeling of Information Diffusion in Online Social Networks written by Guolin Niu and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Temporal Modeling of Information Diffusion in Online Social Networks" by Guolin, Niu, 牛国林, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The rapid development of online social networks (OSNs) renders them a powerful platform for information diffusion on a massive scale. OSNs generate enormous propagation traces. An important question is how to model the real-world information diffusion process. Although considerable studies have been conducted in this field, the temporal characteristics have not been fully addressed yet. This thesis addresses the issue of modeling the temporal dynamics of the information diffusion process. Based on empirical findings drawn from large-scale propagation traces of a popular OSN in China, we demonstrate that the temporal characteristics has a significant impact on the diffusion dynamics. Hence, a series of new temporal information diffusion models have been proposed by incorporating these temporal features. Experimental results demonstrate that these proposed models are more accurate and practical than existing discrete diffusion models. Moreover, one application of information diffusion models, i.e., the revenue maximization problem, is studied. Specifically, the thesis consists of three major parts: 1) preliminaries, i.e., introduction of research platform and collected dataset, 2) modeling social influence diffusion from three different temporal aspects, and 3) monetizing OSNs through designing intelligent pricing strategies in the diffusion process to realize the goal of revenue maximization. Firstly, the research platform is introduced and the statistical properties of the data derived from this platform are investigated. We choose Renren, the dominant social network website in China, as our research platform and study its information propagation mechanisms. Specifically, we concentrate on the propagation of "sharing video" behaviors, and collect data on more than 2.8 million Renren users and over 209 million diffusion traces. The analysis result shows that the video access patterns in OSNs differ significantly from Youtube-like systems, which makes understanding the video propagation behaviors in OSNs an important research task. Secondly, the temporal modeling of information diffusion is explored. By investigating temporal features using real diffusion traces, we find that three factors should be considered in building realistic diffusion models, including, information propagation latency, multiple influential sources and user diversities. We then develop models to explain the information propagation process by incorporating these factors, and demonstrate that the models reflect reality well. Finally, revenue maximization in the information diffusion process is studied. Specifically, the pricing factor is explicitly incorporated into the product diffusion process. To realize the goal of revenue maximization, we develop a Dynamic Programming Based Heuristic (DPBH) to obtain the optimal pricing sequence. Application of the DPBH in the revenue maximization problem shows that it performs well in both the expected revenue achieved and in running time. This leads to fundamental ramifications to many related OSN marketing applications. DOI: 10.5353/th_b5317018 Subjects: Online social networks

Temporal Modeling of Information Diffusion in Online Social Networks

Author : 牛国林
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (894 download)

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Book Synopsis Temporal Modeling of Information Diffusion in Online Social Networks by : 牛国林

Download or read book Temporal Modeling of Information Diffusion in Online Social Networks written by 牛国林 and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Modeling Information Diffusion in Online Social Networks with Partial Differential Equations

Author : Haiyan Wang
Publisher : Springer Nature
ISBN 13 : 3030388522
Total Pages : 153 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Modeling Information Diffusion in Online Social Networks with Partial Differential Equations by : Haiyan Wang

Download or read book Modeling Information Diffusion in Online Social Networks with Partial Differential Equations written by Haiyan Wang and published by Springer Nature. This book was released on 2020-03-16 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book lies at the interface of mathematics, social media analysis, and data science. Its authors aim to introduce a new dynamic modeling approach to the use of partial differential equations for describing information diffusion over online social networks. The eigenvalues and eigenvectors of the Laplacian matrix for the underlying social network are used to find communities (clusters) of online users. Once these clusters are embedded in a Euclidean space, the mathematical models, which are reaction-diffusion equations, are developed based on intuitive social distances between clusters within the Euclidean space. The models are validated with data from major social media such as Twitter. In addition, mathematical analysis of these models is applied, revealing insights into information flow on social media. Two applications with geocoded Twitter data are included in the book: one describing the social movement in Twitter during the Egyptian revolution in 2011 and another predicting influenza prevalence. The new approach advocates a paradigm shift for modeling information diffusion in online social networks and lays the theoretical groundwork for many spatio-temporal modeling problems in the big-data era.

Diffusion in Social Networks

Author : Paulo Shakarian
Publisher : Springer
ISBN 13 : 3319231057
Total Pages : 110 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Diffusion in Social Networks by : Paulo Shakarian

Download or read book Diffusion in Social Networks written by Paulo Shakarian and published by Springer. This book was released on 2015-09-16 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.

Online Social Networks

Author : Valerio Arnaboldi
Publisher : Elsevier
ISBN 13 : 0128030429
Total Pages : 118 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Online Social Networks by : Valerio Arnaboldi

Download or read book Online Social Networks written by Valerio Arnaboldi and published by Elsevier. This book was released on 2015-09-25 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online Social Networks: Human Cognitive Constraints in Facebook and Twitter provides new insights into the structural properties of personal online social networks and the mechanisms underpinning human online social behavior. As the availability of digital communication data generated by social media is revolutionizing the field of social networks analysis, the text discusses the use of large- scale datasets to study the structural properties of online ego networks, to compare them with the properties of general human social networks, and to highlight additional properties. Users will find the data collected and conclusions drawn useful during design or research service initiatives that involve online and mobile social network environments. Provides an analysis of the structural properties of ego networks in online social networks Presents quantitative evidence of the Dunbar’s number in online environments Discusses original structural and dynamic properties of human social network through OSN analysis

A Statistical Analysis and Modeling of Information Diffusion Across Online Social Networks

Author : Michael Piserchia
Publisher :
ISBN 13 :
Total Pages : 144 pages
Book Rating : 4.:/5 (939 download)

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Book Synopsis A Statistical Analysis and Modeling of Information Diffusion Across Online Social Networks by : Michael Piserchia

Download or read book A Statistical Analysis and Modeling of Information Diffusion Across Online Social Networks written by Michael Piserchia and published by . This book was released on 2015 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Temporal Patterns of Communication in Social Networks

Author : Giovanna Miritello
Publisher : Springer Science & Business Media
ISBN 13 : 3319001108
Total Pages : 166 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Temporal Patterns of Communication in Social Networks by : Giovanna Miritello

Download or read book Temporal Patterns of Communication in Social Networks written by Giovanna Miritello and published by Springer Science & Business Media. This book was released on 2013-04-23 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main interest of this research has been in understanding and characterizing large networks of human interactions as continuously changing objects. In fact, although many real social networks are dynamic networks whose elements and properties continuously change over time, traditional approaches to social network analysis are essentially static, thus neglecting all temporal aspects. Specifically, we have investigated the role that temporal patterns of human interaction play in three main fields of social network analysis and data mining: characterization of time (or attention) allocation in social networks, prediction of link decay/persistence, and information spreading. In order to address this we analyzed large anonymized data sets of phone call communication traces over long periods of time. Access to these observations was granted by Telefonica Research, Spain. The findings that emerge from our research indicate that the observed heterogeneities and correlations of human temporal patterns of interaction significantly affect the traditional view of social networks, shifting from a very steady to a highly complex entity. Since structure and dynamics are tightly coupled, they cannot be disentangled in the analysis and modeling of human behavior, though traditional models seek to do so. Our results impact not only the way in which social network are traditionally characterized, but more importantly also the understanding and modeling phenomena such as group formation, spread of epidemics, and the dissemination of ideas, opinions and information.

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)

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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.

Information Diffusion in Online Social Media

Author : Daniele Notarmuzi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (34 download)

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Book Synopsis Information Diffusion in Online Social Media by : Daniele Notarmuzi

Download or read book Information Diffusion in Online Social Media written by Daniele Notarmuzi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: I study the theoretical modelling and the statistical characterization of information diffusion in online social media from a statistical physics perspective. I consider a variety of dynamical models that aim at describing the propagation of information in social systems and investigating their critical behavior, with a focus on the interplay between networks and dynamics. We show that information cascades predicted by these models share the same critical properties, regardless of the network structure. We further consider the critical Hawkes process as a benchmark to develop a principled approach to study cascades in correlated time series. The focus is in particular on the interplay between the temporal resolution used to merge the events in the time series and the resulting cascade distribution. I identify an optimal temporal resolution with the critical point of a one-dimensional percolation model. This approach to the selection of the temporal resolution is used to characterize information cascades in a variety of online social media. Simple and complex contagion, two ways in which information can propagate, are distinguished as the latter is crucially influenced by the existence or absence of multiple sources. Empirical data reveal that both these contagion dynamics take place in online social media. Indeed, results indicate that all system display the same universal behaviour, whose modeling can not leave aside complex contagion dynamics. Overall, we challenge the paradigm that information spreads according to the rules of simple contagion only, first by investigating theoretical models and then comparing their predictions with the outcome of a principled and methodologically sound analysis of data collected from online social media.

Complex Spreading Phenomena in Social Systems

Author : Sune Lehmann
Publisher : Springer
ISBN 13 : 3319773321
Total Pages : 356 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Complex Spreading Phenomena in Social Systems by : Sune Lehmann

Download or read book Complex Spreading Phenomena in Social Systems written by Sune Lehmann and published by Springer. This book was released on 2018-06-21 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches. This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily. Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.

Online Social Media Content Delivery

Author : Zhi Wang
Publisher : Springer
ISBN 13 : 9811027749
Total Pages : 117 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Online Social Media Content Delivery by : Zhi Wang

Download or read book Online Social Media Content Delivery written by Zhi Wang and published by Springer. This book was released on 2018-07-31 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains how to use a data-driven approach to design strategies for social media content delivery. It first introduces readers to how social information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction models for social media content delivery. By addressing the resource allocation and content replication aspects of social media content delivery, the book presents the latest data-driven strategies. In closing, it outlines a number of potential research directions regarding social media content delivery.

Information Diffusion and Opinion Dynamics in Social Networks

Author : Julio Cesar Louzada Pinto
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (936 download)

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Book Synopsis Information Diffusion and Opinion Dynamics in Social Networks by : Julio Cesar Louzada Pinto

Download or read book Information Diffusion and Opinion Dynamics in Social Networks written by Julio Cesar Louzada Pinto and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics.

Social Network Analysis - Community Detection and Evolution

Author : Rokia Missaoui
Publisher : Springer
ISBN 13 : 331912188X
Total Pages : 282 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Social Network Analysis - Community Detection and Evolution by : Rokia Missaoui

Download or read book Social Network Analysis - Community Detection and Evolution written by Rokia Missaoui and published by Springer. This book was released on 2015-01-13 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edited work will appeal to researchers, practitioners and students interested in the latest developments of social network analysis.

Learning from Multiple Social Networks

Author : Liqiang Nie
Publisher : Springer Nature
ISBN 13 : 3031023005
Total Pages : 102 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Learning from Multiple Social Networks by : Liqiang Nie

Download or read book Learning from Multiple Social Networks written by Liqiang Nie and published by Springer Nature. This book was released on 2022-05-31 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dual-heterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants.

Epidemic Modelling

Author : D. J. Daley
Publisher : Cambridge University Press
ISBN 13 : 9780521640794
Total Pages : 160 pages
Book Rating : 4.6/5 (47 download)

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Book Synopsis Epidemic Modelling by : D. J. Daley

Download or read book Epidemic Modelling written by D. J. Daley and published by Cambridge University Press. This book was released on 1999-04-13 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a general introduction to the mathematical modelling of diseases.

Modeling Information Diffusion in Social Networks

Author : Hongxian Sun
Publisher :
ISBN 13 :
Total Pages : 77 pages
Book Rating : 4.:/5 (815 download)

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Book Synopsis Modeling Information Diffusion in Social Networks by : Hongxian Sun

Download or read book Modeling Information Diffusion in Social Networks written by Hongxian Sun and published by . This book was released on 2012 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Agent-based Model for Information Diffusion Over Online Social Networks

Author : Zhuo Chen
Publisher :
ISBN 13 :
Total Pages : 129 pages
Book Rating : 4.:/5 (17 download)

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Book Synopsis An Agent-based Model for Information Diffusion Over Online Social Networks by : Zhuo Chen

Download or read book An Agent-based Model for Information Diffusion Over Online Social Networks written by Zhuo Chen and published by . This book was released on 2016 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, social networks services such as Facebook, Twitter, Instagram, etc. have become popular platforms for either celebrities, news media, organizations, governors or general public to express their ideas and opinions. They have created a great opportunity for researchers to explore how information spread through online social networks. Benefited from this, this thesis studies the efficient way of information diffusion on online social networks using the approach of agent-based modeling (ABM). A NetLogo ABM was created to conduct the experiments and analyses, along with the real network dataset retrieved from Twitter. It shows that with the same number of nodes and edges, the network having higher average path length or lower average clustering coefficient tends to have wider information diffusion. In addition, how to locate optimal early adopters in order to satisfy efficient information diffusion mainly depends on the network structure and propagation probabilities among individuals in the network. This thesis aims at contributing to studies of online social networks on information diffusion from the perspective of efficient diffusion with agent-based modeling and simulation. Application of this thesis could benefit those from business or government who want to disseminate advertisement/information in a fast and economic way. Outcomes from this study should also provide hints to the geography likely behind information diffusion in social networks.