A Generalized Linear Threshold Model for Multiple Cascades

Pathak, Nishith; Banerjee, Arindam; Srivastava, Jaideep

doi:10.1109/icdm.2010.153

Cited by 103 publications

(47 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diffusion of several contagions has been the focus of several recent works [10], [12], [18], [19], [23]. In each of these works, however, it is assumed that being infected with one contagion is mutually exclusive to be infected by another.…”

Section: Related Workmentioning

confidence: 99%

Clash of the Contagions: Cooperation and Competition in Information Diffusion

Myers

Leskovec

2012

2012 IEEE 12th International Conference on Data Mining

151

124

View full text Add to dashboard Cite

Abstract-In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network.While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other's probability of spreading, while cooperating contagions help each other in being adopted throughout the network. We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model learns how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by 71% on the average.

show abstract

Section: Related Workmentioning

confidence: 99%

Clash of the Contagions: Cooperation and Competition in Information Diffusion

Myers

Leskovec

2012

2012 IEEE 12th International Conference on Data Mining

151

124

View full text Add to dashboard Cite

show abstract

“…On the other hand, [5] proposes topic-aware extensions of LT model. In [60] the authors consider multiple cascades of LT model and they allow nodes to switch between them, whereas [10] introduces a number of modifications to the competing model variant: the authors force nodes to draw one cascade they join at the end of the process or consider the mutual influence of cascades on each other.…”

Section: Theorem 141 the Influence Maximization Problem Is Np-hard Fmentioning

confidence: 99%

Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges

Michalski

Kazienko

2015

Intelligent Systems Reference Library

View full text Add to dashboard Cite

The following chapter aims to present the current research in the area of modelling and maximizing social influence in networks. Apart from describing the most popular models for this process, it focuses on presenting the advances in maximizing the spread of influence in social networks. Since most of the research was suited for static networks case, nowadays it is necessary to move it toward the networks that are everywhere around us-the dynamic ones. As is widely agreed in the scientific community, static networks are unacceptable simplification of the real world processes, so current research is moving toward the temporal networks. It is especially important when modelling propagation phenomena, such as the spread of influence, epidemics or diffusion of innovations. In this chapter it is presented how the research on maximizing the spread of influence is starting to explore real-world cases and how the early attempts of solving this problem for temporal networks look like. Moreover, it is shown how to benefit from the temporal properties of the social network in order to achieve better results for spread of influence compared to the static approach.

show abstract

“…Additionally, most existing work assumes only two concepts, while in reality there could be many interacting concepts. It is also often assumed that once activated a node remains active, though there are exceptions to this [17].…”

Section: Related Workmentioning

confidence: 99%

Maximising Influence in Non-blocking Cascades of Interacting Concepts

Archbold

Griffiths

2016

Multi-Agent Based Simulation XVI

View full text Add to dashboard Cite

Abstract. In large populations of autonomous individuals, the propagation of ideas, strategies or infections is determined by the composite effect of interactions between individuals. The propagation of concepts in a population is a form of influence spread and can be modelled as a cascade from a set of initial individuals through the population. Understanding influence spread and information cascades has many applications, from informing epidemic control and viral marketing strategies to understanding the emergence of conventions in multi-agent systems. Existing work on influence spread has mainly considered single concepts, or small numbers of blocking (exclusive) concepts. In this paper we focus on non-blocking cascades, and propose a new model for characterising concept interaction in an independent cascade. Furthermore, we propose two heuristics, Concept Aware Single Discount and Expected Infected, for identifying the individuals that will maximise the spread of a particular concept, and show that in the non-blocking multi-concept setting our heuristics out-perform existing methods.

show abstract

A Generalized Linear Threshold Model for Multiple Cascades

Cited by 103 publications

References 22 publications

Clash of the Contagions: Cooperation and Competition in Information Diffusion

Clash of the Contagions: Cooperation and Competition in Information Diffusion

Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges

Maximising Influence in Non-blocking Cascades of Interacting Concepts

Contact Info

Product

Resources

About