Microscopic evolution of social networks

By Jure Leskovec, Lars Backstrom, Ravi Kumar, Andrew Tomkins. 2008

831 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

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In Statistical and Nonlinear Physics

Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, neutral): “5Ending with “ bot” or “Bot”; or containing “transcriber” or “automoderator”.”
Community detection in graphs (2009, background): “Leskovec et al. performed low-conductance cuts with several methods, to ensure that the result is not a simple artefact of a particular chosen technique.”
Supervised random walks: predicting and recommending links in social networks (2010, methodology): “, a person connects to a friend of a friend [19].”
Mapping the world's photos (2009, background): “, [7, 12, 14, 15, 24]), or on content, such as studies of image tagging (e.”
Community Detection and Mining in Social Media (2010, background): “This phenomenon of decreasing probability of new connections between two nodes with increasing distance between them, is also confirmed later in (Leskovec et al., 2008).”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, background): “Moreover, the majority of new links in typical online social networks close triangles [Leskovec et al. 2008; Backstrom and Leskovec 2011], that is, are 2-hop links.”
Modeling relational events via latent classes (2010, methodology): “[18]) with stochastic models such as the mixed membership stochastic blockmodel [1] (see below) or MPMM to automatically share statistical strength among similar groups of nodes or edges.”
Potential networks, contagious communities, and understanding social network structure (2013, background): “The author thanks the Simons foundation for their generous support of this research. lying network structure from data about a network that is generated via a cascade spread over the underlying network.”
Sensing Trending Topics in Twitter (2013, background): “As a result, social media data mining, originally aimed at understanding and predicting the evolution of the online worlds [5], [6], [7], [8], [9], is now increasingly leveraged to study the dynamics of real-world events.”
No country for old members: user lifecycle and linguistic change in online communities (2013, background): “For example, previous research has examined the dynamics of online social networks, studying the evolution of the whole network structure [21, 28], of groups inside these networks [2, 10, 16, 50], as well as the social tie creation between individual users [29].”
#mytweet via Instagram: Exploring user behaviour across multiple social networks (2015, background): “Other user activities, such as social connection [5], content generation [6], participation in conversation [7], have also been studied to gain better understanding of OSNs.”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “A considerable amount of research has been conducted on social network analysis [3, 15, 18].”
Toward link predictability of complex networks (2015, background): “…damping according to path lengths, i.e., s Katzxy = α A xy + α 2 A 2 xy + α 3 A 3 xy + ⋯ , [9] which can be rewritten in the compact form, when j α j 1 = λ max , as S = ð I − α A Þ − 1 − I , [10] where I is the identity matrix, A is the adjacency matrix, and λ max is the largest eigenvalue of A .”
Evolution of social-attribute networks: measurements, modeling, and implications using google+ (2012, background): “Nodes in a SAN corresponding to nodes in G are called social nodes and denoted as the set V s , while nodes representing attributes are called attribute nodes and denoted as the set V a .”
Customer churn prediction in telecom using machine learning in big data platform (2019, background): “This feature tells us how close the customer’s friends are (number of existing connections in a neigh - borhood divided by the number of all possible connections) [24].”
Inferring anchor links across multiple heterogeneous social networks (2013, background): “Social network analysis [16, 12], especially the link prediction problem in social networks, has been intensively studied in recent years [13, 10, 23].”