Information and Influence Propagation in Social Networks (Paperback)

Wei Chen, Laks V.S. Lakshmanan, Carlos Castillo

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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.

Table of Contents: Acknowledgments / Introduction / Stochastic Diffusion Models / Influence Maximization / Extensions to Diffusion Modeling and Influence Maximization / Learning Propagation Models / Data and Software for Information/Influence: Propagation Research / Conclusion and Challenges / Bibliography / Authors' Biographies / Index

商品描述(中文翻譯)

社交網絡研究在過去十年間蓬勃發展。在很大程度上,這是由社交媒體和線上社交網絡站的驚人增長推動的,這些平台仍然以非常快的速度增長,同時也受益於越來越多的大型社交網絡數據集供研究之用。這項研究的豐富成果致力於分析信息、影響力、創新、感染、實踐和習俗在網絡中的傳播。我們能否建立模型來解釋這些傳播的方式?我們如何將我們的模型與過去發生的社交網絡和傳播軌跡的真實數據進行驗證?這些只是這個領域的研究人員所研究的一些問題。信息傳播模型在病毒式營銷、爆發檢測、尋找關鍵博客文章以閱讀重要故事、尋找領導者或潮流引領者、信息供稿排序等方面有應用。在這些應用中出現的一些算法問題已經被研究人員在影響力最大化的名義下進行了抽象和廣泛研究。

本書首先詳細描述了一些成熟的擴散模型,包括獨立級聯模型和線性閾值模型,這些模型在解釋傳播現象方面取得了成功。我們描述了它們的特性以及對它們的多種擴展,引入了競爭、預算和時間關鍵性等方面。我們深入探討了影響力最大化的關鍵問題,該問題選擇關鍵個體以激活網絡中的大部分個體。在經典的擴散模型中,包括獨立級聯模型和線性閾值模型,影響力最大化是計算上難以處理的,更確切地說是#P困難的,我們描述了文獻中提出的幾種近似算法和可擴展的啟發式方法。最後,我們還處理了將這項研究轉化為實踐所需解決的關鍵問題,例如學習網絡中個體之間相互影響的強度,以及這項研究的實際方面,包括數據集和軟件工具的可用性以促進研究。我們以討論從技術角度和將研究結果轉化為工業應用的觀點來總結各種仍然存在的研究問題。

目錄:致謝 / 引言 / 隨機擴散模型 / 影響力最大化 / 擴散建模和影響力最大化的擴展 / 學習傳播模型 / 信息/影響力:傳播研究的數據和軟件 / 結論和挑戰 / 參考文獻 / 作者簡介 / 索引