Patterns of Influence in a Recommendation Network

By Jure Leskovec, Ajit Singh, Jon M. Kleinberg. 2006

361 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

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Citations in the corpus

A framework for quantitative analysis of cascades on networks (2010, background): “Fig. 2 illustrates the cascade plots computed for a variety of contagion processes, which include several prototypes of cascades frequently observed in recommendation and blog networks [1, 2 ]. We label nodes in the order in which they are activated and take (i; ) = 1 when i is the seed of the casc”
The structure of online diffusion networks (2012, background): “Accordingly, theoretical models of diffusion have generally imitated disease models in the sense that popular products are assumed to diffuse multiple steps from their origin in the manner of epidemics, “infecting” large numbers of people in the process [Watts 2002; Leskovec et al. 2006].”
Tractable Models for Information Diffusion in Social Networks (2006, background): “Recently, considerable attention has been devoted to investigating social networks [9,5,4,7,11,6], since the progress of the Internet, the World Wide Web, and blogs has enabled us to collect real large-scale social networks.”
Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, background): “(2006) [88] have conducted experiments on recommendation cascades, which are sequences of accepted and forwarded recommendations.”
On the Convexity of Latent Social Network Inference (2010, background): “This dramatically reduces the number of variables as in practice the true A does not induce large cascades, causing the cascades to be sparse in the number of nodes they infect [14, 17].”
Learning Networks of Heterogeneous Influence (2012, background): “Similarly, when consumers rush to buy some particular products, marketers can know when purchases occurred, but they cannot track in further where the recommendations originally came from [12].”
The dynamics of viral marketing (2005, background): “A detailed enumeration and analysis of observed topological cascade patterns for this dataset is made in [10].”
Social contextual recommendation (2012, methodology): “Recognizing that influence is a subtle force that governs the dynamics of social networks [16, 17], influence-based recommendation [10] involves interpersonal influence brought by senders and receivers into social recommendation cases.”
The impact of YouTube recommendation system on video views (2010, background): “study the influence patterns in user-to-user recommendation network in [8].”
TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, methodology): “By combining the original node content features and the new structure features, we develop an iterative classification algorithm, TrGraph, that utilizes label dependency to jointly classify nodes in the target network.”
Universality in network dynamics (2013, background): “The distribution of cascade sizes induced by perturbations, P(C), is frequently measured in social [42–44] technological [45, 46] and biological [41, 47] systems, finding that P(C) is often fat-tailed, an observation whose origins remain unclear.”
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015, background): “2There is a large body of research literature on peer influence [22, 6, 30].”
Patterns of Cascading Behavior in Large Blog Graphs (2007, methodology): “Empirical analysis of the topological patterns of cascades in the context of a large product recommendation network is in [16] and [14].”