Modeling relationship strength in online social networks

By Rongjing Xiang, Jennifer Neville, Monica Rogati. The Web Conference, 2010

727 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3bddba3214fe9e7248a934455a2624aa5b781778

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In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

mTrust: discerning multi-faceted trust in a connected world (2012, background): “develop a latent variable model to estimate relationship strength from interaction activities and user similarities [24].”
Link prediction in social networks: the state-of-the-art (2014, background): “Some works focus on the relationship strength in social networks, such as predicting tie strength with social media[16] and modeling relationship strength in online social networks [15].”
A Survey of Signed Network Mining in Social Media (2015, background): “Furthermore, the strengths of positive ties are modeled as the hidden causes of user interactions since they affects the frequency and nature of user interactions [Xiang et al. 2010].”
Microscopic Social Influence (2012, background): “…on users’ 1 activities [4], verifying the existence of correlation between personal behavior and social connection [5], ﬁnding the most inﬂuential nodes in social networks [6, 7], estimating the inﬂuence strength of social ties [8], and inferring inﬂuence channels for implicit networks [9].”
Graph Neural Networks for Social Recommendation (2019, background): “opinions between users and items jointly. In addition, the low cost of link formation in online worlds can result in networks with varied tie strengths (e.g., strong and weak ties are mixed together) [36]. Users are likely to share more similar tastes with strong ties than weak ties. Considering soc”
Topic-aware social influence propagation models (2012, neutral): “The approaches discussed above, assume a weighted social graph as input and do not address how the link influence weights (or probabilities) can be obtained; [10], [11], [12], [13] instead focus on the latter problem and propose specific solutions.”
Inferring social ties across heterogenous networks (2012, background): “[33] develop a latent variable model to estimate relationship strength from interaction activity and user similarity.”
Recommendations in location-based social networks: a survey (2015, background): “User recommendations, which includes popular user discovery [12, 38, 98], friend recommendation [5, 16, 82, 104, 117], and community discovery [60, 103], have been extensively studied in the context of traditional social networks.”
Analysis of Physical Activity Propagation in a Health Social Network (2014, background): “Another line of study has focused on the problem of learning the inﬂuence probabilities on every edge of a social network given an observed log of propagations over this network [4, 21, 24, 28].”