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  • Influence
Learning influence probabilities in social networks
TLDR
This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
TLDR
It is proved that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard, and a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques is proposed.
k-nearest neighbors in uncertain graphs
TLDR
Novel distance functions that extend well-known graph concepts, such as shortest paths are proposed that outperform previously used alternatives in identifying true neighbors in real-world biological data and scale for graphs with tens of millions of edges.
The query-flow graph: model and applications
TLDR
This paper introduces the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior, and proposes a methodology that builds such a graph by mining time and textual information as well as aggregating queries from different users.
A Data-Based Approach to Social Influence Maximization
TLDR
A new model is introduced, which is called credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread, and is time-aware in the sense that it takes the temporal nature of influence into account.
Fast shortest path distance estimation in large networks
TLDR
This paper proves that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed, and explores theoretical insights to devise a variety of simple methods that scale well in very large networks.
Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees
TLDR
This paper defines a novel density function, which gives subgraphs of much higher quality than densest sub graphs: the graphs found by the method are compact, dense, and with smaller diameter.
FA*IR: A Fair Top-k Ranking Algorithm
TLDR
This work defines and solves the Fair Top-k Ranking problem, and presents an efficient algorithm, which is the first algorithm grounded in statistical tests that can mitigate biases in the representation of an under-represented group along a ranked list.
Topic-aware social influence propagation models
TLDR
Novel topic-aware influence-driven propagation models that are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature are introduced.
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