Friendship prediction and homophily in social media

By Luca Maria Aiello, Alain Barrat, Rossano Schifanella, Ciro Cattuto, Benjamin Markines, Filippo Menczer. TWEB, 2012

438 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fe50a1194b3bd1cfbbd92703415cd3b6f419cefe

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In Statistical and Nonlinear Physics

Citations in the corpus

Link prediction in social networks: the state-of-the-art (2014, background): “First, it can be applied to recommender systems in information retrieval and e-commerce, which can help people to ﬁnd new friends [106] and potential collaborators [107, 108], provide interesting items in online shopping [109], recommend patent partners in enterprise social networks [108] and…”
The spreading of misinformation online (2016, background): “Such a phenomenon is particularly evident on the WWW where users, embedded in homogeneous clusters (14 – 16), process information through a shared system of meaning (10, 11, 17, 18) and trigger collective framing of narratives that are often biased toward self-confirmation.”
Homophily, Structure, and Content Augmented Network Representation Learning (2016, background): “Homophily (or the first-order proximity): Homophily phenomenon [6] is a known fact in social networks where nodes with similar attributes (content) tend to be connected through relational ties, which contribute to local structure of individual nodes [7].”
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.”
On the Behaviour of Deviant Communities in Online Social Networks (2016, neutral): “This tendency has been observed with quantitative studies in several online social media (Leskovec and Horvitz 2008; Aiello et al. 2012).”
Computational Fact Checking from Knowledge Networks (2015, background): “The truth value τ(e) maximizes the semantic proximity defined by Eq 1, which is equivalent to finding the shortest path between s and o [27], or the one that provides the maximum information content [28, 29] in the WKG.”
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior (2015, background): “Studies have found that people tend to interact with others who belong to a similar socio-economic class [16, 29] or share similar interests [25, 4].”
In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media (2015, background): “…may not actively choose their Facebook friends (and thus what they see on Facebook) on the basis of shared political or social beliefs (Vraga, Thorson, Kligler-Vilenchik, & Gee, 2015), de facto selection biases may still mean that friends circles are relatively homogeneous (Aiello et al., 2012).”