標(biāo)題: Titlebook: Diffusion in Social Networks; Paulo Shakarian,Abhivav Bhatnagar,Ruocheng Guo Book 2015 The Author(s) 2015 artificial intelligence.diffusio [打印本頁] 作者: Suture 時(shí)間: 2025-3-21 18:07
書目名稱Diffusion in Social Networks影響因子(影響力)
書目名稱Diffusion in Social Networks影響因子(影響力)學(xué)科排名
書目名稱Diffusion in Social Networks網(wǎng)絡(luò)公開度
書目名稱Diffusion in Social Networks網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Diffusion in Social Networks被引頻次
書目名稱Diffusion in Social Networks被引頻次學(xué)科排名
書目名稱Diffusion in Social Networks年度引用
書目名稱Diffusion in Social Networks年度引用學(xué)科排名
書目名稱Diffusion in Social Networks讀者反饋
書目名稱Diffusion in Social Networks讀者反饋學(xué)科排名
作者: 意見一致 時(shí)間: 2025-3-21 22:21 作者: 有偏見 時(shí)間: 2025-3-22 01:36 作者: Exhilarate 時(shí)間: 2025-3-22 07:26 作者: FIS 時(shí)間: 2025-3-22 09:07 作者: Affection 時(shí)間: 2025-3-22 15:46 作者: Affection 時(shí)間: 2025-3-22 17:46
https://doi.org/10.1007/978-3-030-03721-5 topic from multiple fields. The availability of large social network datasets over nearly the past two decades have made it possible to explore network diffusion like never before. Having said that, the materials covered in this book is not limited to the online platforms, but rather are thought to作者: 方便 時(shí)間: 2025-3-22 21:17 作者: 迎合 時(shí)間: 2025-3-23 03:42 作者: Conserve 時(shí)間: 2025-3-23 08:56
https://doi.org/10.1007/978-3-030-03721-5We describe different properties of these models and how these properties affect solving problems such as influence maximization and influence spread. We describe approaches to address influence maximization problem in independent cascade model and linear threshold model that rely on the maximizatio作者: coltish 時(shí)間: 2025-3-23 12:15
https://doi.org/10.1007/978-3-030-03721-5that can consider not only the topology of the social network, but attributes of the nodes and edges as well. We then define a class of problems called . (SNDOPs). In this chapter, we show how various diffusion processes can be embedded as GAP’s and then study the algorithmic and complexity issues a作者: Hippocampus 時(shí)間: 2025-3-23 17:16
Refugees and Migrants in Law and Policyginal framework for EGT and the major work that has followed it. Here, we will study the calculation of the “fixation probability”—the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time 作者: 得體 時(shí)間: 2025-3-23 20:54 作者: FLIC 時(shí)間: 2025-3-24 00:18
https://doi.org/10.1057/9780230305700e Chap.?., are?really still in the early stages of development. We have noted that recent work of this type deals with issues such as predicting the influence of individuals nodes, predicting the outcome of a diffusion process, and identifying more realistic models. Work in this area spans from obse作者: Keratectomy 時(shí)間: 2025-3-24 06:11 作者: CUMB 時(shí)間: 2025-3-24 09:07
https://doi.org/10.1007/978-3-030-03721-5k Granovetter studied these ideas from a sociological perspective?[5]. However, it wasn’t until Kempe et al. article?[1] in 2003 that information diffusion became a significant line of research in computer science.作者: DUST 時(shí)間: 2025-3-24 12:33
Introduction,k Granovetter studied these ideas from a sociological perspective?[5]. However, it wasn’t until Kempe et al. article?[1] in 2003 that information diffusion became a significant line of research in computer science.作者: BULLY 時(shí)間: 2025-3-24 16:17
2191-5768 f 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作者: 吞沒 時(shí)間: 2025-3-24 19:26
https://doi.org/10.1007/978-3-030-03721-5o, we survey a variety of nodal measures based on centrality (degree, betweenness, etc.) and other methods (shell decomposition, nearest neighbor analysis, etc.). We then present a set of experiments that illustrate the relation of these nodal measures to spreading under the SIR model.作者: 字的誤用 時(shí)間: 2025-3-25 00:00
https://doi.org/10.1007/978-3-030-03721-5 We describe approaches to address influence maximization problem in independent cascade model and linear threshold model that rely on the maximization of submodular functions—as well as extensions to these approaches for larger datasets.作者: 共同時(shí)代 時(shí)間: 2025-3-25 04:17 作者: 畢業(yè)典禮 時(shí)間: 2025-3-25 07:56 作者: xanthelasma 時(shí)間: 2025-3-25 14:45
Book 2015s 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.作者: lesion 時(shí)間: 2025-3-25 17:06 作者: 分發(fā) 時(shí)間: 2025-3-25 21:12
Conclusion,rvational studies in disciplines such as sociology and economics to the machine learning approaches seen in the computer science community. As data on real-world diffusion traces become more available, we?expect this line of work to grow further.作者: 材料等 時(shí)間: 2025-3-26 01:45 作者: 壓迫 時(shí)間: 2025-3-26 06:36 作者: 碎石 時(shí)間: 2025-3-26 09:03
Logic Programming Based Diffusion Models,d . (SNDOPs). In this chapter, we show how various diffusion processes can be embedded as GAP’s and then study the algorithmic and complexity issues associated with SDNOP’s. Experimental results are also included.作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-26 15:01 作者: Forsake 時(shí)間: 2025-3-26 19:38
https://doi.org/10.1007/978-3-030-03721-5. We provide exact and heuristic methods for solving this problem as well as a suite of results to not only demonstrate the utility of these methods, but provide insight into the dynamics of the tipping model also.作者: 王得到 時(shí)間: 2025-3-26 23:05 作者: Loathe 時(shí)間: 2025-3-27 04:32
Book 2015es 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 discu作者: 好開玩笑 時(shí)間: 2025-3-27 05:25
Introduction, topic from multiple fields. The availability of large social network datasets over nearly the past two decades have made it possible to explore network diffusion like never before. Having said that, the materials covered in this book is not limited to the online platforms, but rather are thought to作者: 陳腐思想 時(shí)間: 2025-3-27 13:12 作者: FEAS 時(shí)間: 2025-3-27 15:35
The Tipping Model and the Minimum Seed Problem,ghbors currently exhibit the same. A key problem, with respect to this model, is to select an initial “seed” set from the network such that the entire network adopts any behavior given to the seed. In this chapter, we investigate the problem of identifying a seed set of minimum size—which is NP-hard作者: consent 時(shí)間: 2025-3-27 21:04
The Independent Cascade and Linear Threshold Models,We describe different properties of these models and how these properties affect solving problems such as influence maximization and influence spread. We describe approaches to address influence maximization problem in independent cascade model and linear threshold model that rely on the maximizatio作者: 一窩小鳥 時(shí)間: 2025-3-27 22:30 作者: SLING 時(shí)間: 2025-3-28 05:13 作者: 使成核 時(shí)間: 2025-3-28 10:16
Examining Diffusion in the Real World,this chapter, we study diffusion processes from a data-driven perspective—specifiably reviewing the early identification of information cascades that will diffuse through a large portion of the network.
