Total Influence and Hybrid Simulation of Independent Cascade Model using Rough Knowledge Granules
01 Feb 2020-
TL;DR: The paper defines a new theoretical measure Total Influence, of a node as well as a set of nodes in the social network, and proposes a new hybrid simulation methodology for the independent cascade model of diffusion to quantify the size of the spreading practically.
Abstract: The paper defines a new theoretical measure Total Influence, of a node as well as a set of nodes in the social network. Total influence uses probabilistic theory to obtain the expected size of the information spreading in the social network under the independent cascade model of diffusion. In order to quantify the size of the spreading practically, the paper proposes a new hybrid simulation methodology for the independent cascade model. The hybrid method uses rough set theory and defines rough knowledge agents around all the seed nodes from which the information is propagating. The lower approximation is calculated using the probabilistic approach, while the size of influence in the boundary region is quantified by Monte-Carlo simulation on a reduced network. The reduce network is formed by compacting all the nodes in the lower approximate region as a super-node. Experimental results on two synthetically generated directed network show that the hybrid method runs magnitude faster than its counterpart with a similar accuracy of the spreading size.
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01 Nov 2010
TL;DR: It is found that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time, suggesting that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight.
Abstract: Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.
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Book•
01 Jan 1962
TL;DR: A history of diffusion research can be found in this paper, where the authors present a glossary of developments in the field of Diffusion research and discuss the consequences of these developments.
Abstract: Contents Preface CHAPTER 1. ELEMENTS OF DIFFUSION CHAPTER 2. A HISTORY OF DIFFUSION RESEARCH CHAPTER 3. CONTRIBUTIONS AND CRITICISMS OF DIFFUSION RESEARCH CHAPTER 4. THE GENERATION OF INNOVATIONS CHAPTER 5. THE INNOVATION-DECISION PROCESS CHAPTER 6. ATTRIBUTES OF INNOVATIONS AND THEIR RATE OF ADOPTION CHAPTER 7. INNOVATIVENESS AND ADOPTER CATEGORIES CHAPTER 8. DIFFUSION NETWORKS CHAPTER 9. THE CHANGE AGENT CHAPTER 10. INNOVATION IN ORGANIZATIONS CHAPTER 11. CONSEQUENCES OF INNOVATIONS Glossary Bibliography Name Index Subject Index
38,750 citations
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Abstract: Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
33,771 citations
"Total Influence and Hybrid Simulati..." refers methods in this paper
...NetBA is generated using networkx internal graph generating algorithm which follows Barabási-Albert preferential attachment model [20]....
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01 Jan 2006
TL;DR: Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system by concerned with the spread of messages that are perceived as new ideal.
Abstract: Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. Diffusion is a special type of communication concerned with the spread of messages that are perceived as new ideal. Communication is a process in which participants create and share information with one another in order to reach a mutual understanding. Diffusion has a special character because of the newness of the idea in the message content. Thus some degree of uncertainty and perceived risk is involved in the diffusion process. An individual can reduce this degree of uncertainty by obtaining information. Information is a difference in matter energy that affects uncertainty in a situation where a choice exists among a set of alternatives.
9,295 citations
24 Aug 2003
TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.
5,887 citations
TL;DR: This article developed models of collective behavior for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative, and the key...
Abstract: Models of collective behavior are developed for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative. The key...
5,195 citations
"Total Influence and Hybrid Simulati..." refers background in this paper
...One of the pioneering work [4], [5] on viral marketing triggers a series of research studies on diffusion models [1], [2], [6], [7] and several application of information diffusion, especially in the online social networks [1], [5], [8]–[13]....
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...Diffusion of innovation first studied by the sociologist [2], [3] more than half a century back....
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