Understanding information interactions in diffusion: an evolutionary game-theoretic perspective

Su, Yuan; Zhang, Xi; Liu, Lixin; Song, Shouyou; Fang, Binxing

doi:10.1007/s11704-015-5008-y

Cited by 21 publications

(8 citation statements)

References 22 publications

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“…Nonetheless, IMM model is implemented as a baseline to compare with. Other studies [10], [11], [35] also neglect the influence of user roles' interactions, and do not discover the interactions of actual categories of contagions, which are different from our work.…”

Section: Information Diffusioncontrasting

confidence: 93%

IAD: Interaction-Aware Diffusion Framework in Social Networks

Zhang,

Su,

et al. 2017

Preprint

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In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing information diffusion models are built on the assumption that each individual contagion spreads independently, regardless of their interactions. Gaining insights into such interaction is crucial to understand the contagion adoption behaviors, and thus can make better predictions. In this paper, we study the contagion adoption behavior under a set of interactions, specifically, the interactions among users, contagions' contents and sentiments, which are learned from social network structures and texts. We then develop an effective and efficient interaction-aware diffusion (IAD) framework, incorporating these interactions into a unified model. We also present a generative process to distinguish user roles, a co-training method to determine contagions' categories and a new topic model to obtain topic-specific sentiments. Evaluation on the large-scale Weibo dataset demonstrates that our proposal can learn how different users, contagion categories and sentiments interact with each other efficiently. With these interactions, we can make a more accurate prediction than the state-of-art baselines. Moreover, we can better understand how the interactions influence the propagation process and thus can suggest useful directions for information promotion or suppression in viral marketing.

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Section: Information Diffusioncontrasting

confidence: 93%

IAD: Interaction-Aware Diffusion Framework in Social Networks

Zhang,

Su,

et al. 2017

Preprint

Self Cite

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“…Past theoretical research has made significant progress in mapping the behavior of complex contagions within a range of network topologies. Newer work has begun to explore the complexities that arise when multiple kinds of contagions interact in the same network (Su et al 2016). Moreover, while the content of a contagion undoubtedly influences the spread and interaction of competing behaviors, it may have an impact on network structure as well.…”

Section: Ecologies Of Complex Contagionsmentioning

confidence: 99%

Complex Contagions: A Decade in Review

Guilbeault

Becker

Centola

2018

Computational Social Sciences

134

104

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Since the publication of "Complex Contagions and the Weakness of Long Ties" in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers, and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.

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“…The competitive setting has resulted in several game theoretic studies of this problem [26], [27], [28]. There have been preliminary studies addressing interaction among different informations, where the spread of one information influences the spread of the others [29], [30].…”

Section: B Related Workmentioning

confidence: 99%

Optimal Investment Strategies for Competing Camps in a Social Network: A Broad Framework

Dhamal

Ben‐Ameur

Chahed

et al. 2019

IEEE Trans. Netw. Sci. Eng.

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We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time.

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Understanding information interactions in diffusion: an evolutionary game-theoretic perspective

Cited by 21 publications

References 22 publications

IAD: Interaction-Aware Diffusion Framework in Social Networks

IAD: Interaction-Aware Diffusion Framework in Social Networks

Complex Contagions: A Decade in Review

Optimal Investment Strategies for Competing Camps in a Social Network: A Broad Framework

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