Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

By Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song. 2014

208 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Graph-based classification methods have been widely applied to various security problems such as malware detection [14, 59], fraudulent user detection in social networks [10, 25, 36, 67, 68, 72, 74], fake review detection [1, 65], auction fraud detection [47], APT infection detection [46], and attri”
Random Walk Based Fake Account Detection in Online Social Networks (2017, background): “We note that our label-augmented social network can also be viewed as a Social-Attribute Network [37] or Social-BehaviorAttribute network [38], where the two label nodes are treated as attributes.”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, background): “an edge between a review and a product means that the review is for the product; a positive node means a fake review while a negative node means a genuine review. In attribute inference attacks [31], [32], [34], [35], the graph is the social graph between users; a node is positive if the node has a ”
You Are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors (2016, methodology): “LINK represents each user as a binary feature vector, and a feature has a value of 1 if the user is a friend of the person that corresponds to the feature.”
AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, methodology): “One problem of increasing interest revolves around these private attributes [16, 19, 34, 28, 13, 22, 18, 32, 4, 17, 12].”
Attribute Inference Attacks in Online Social Networks (2018, methodology): “We note that the SBA network model extends our social-attribute-network (SAN) model [23] by further including behavior information into the framework.”
Latent Space Model for Multi-Modal Social Data (2015, background): “However, a number of recent studies makes it increasingly clear that focusing only on network properties while discarding rich contextual information limits our comprehensive understanding of complex social systems dynamics [52, 36, 4, 56, 18, 32, 49, 37, 20, 25, 27, 2].”