F. Bonchi

Publications199
h-index48
Citations8,738
Highly Influential Citations714
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  • Publications
  • Influence
Learning influence probabilities in social networks
TLDR
We show that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance, and we develop techniques for predicting the time by which a user may be expected to perform an action. Expand
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Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
TLDR
We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Expand
  • 443
  • 58
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The query-flow graph: model and applications
TLDR
Query logs record the queries and the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences, and the behavior of users, as well as their implicit feedback to search engine results. Expand
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k-nearest neighbors in uncertain graphs
TLDR
We propose novel distance functions that extend well-known graph concepts, such as shortest paths. Expand
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Fast shortest path distance estimation in large networks
TLDR
We study approximate landmark-based methods for point-to-point distance estimation in very large networks. Expand
  • 262
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A Data-Based Approach to Social Influence Maximization
TLDR
We study influence maximization as defined by Kempe et al., but from a novel, data-based perspective. Expand
  • 407
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Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees
TLDR
We introduce a novel density function, which gives subgraphs of much higher quality than densestsubgraphs: the graphs found by our method are compact, dense and with smaller diameter. Expand
  • 199
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Topic-aware social influence propagation models
TLDR
We introduce novel topic-aware influence-driven propagation models that, as we show in experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. Expand
  • 177
  • 20
  • PDF
Anonymization of moving objects databases by clustering and perturbation
TLDR
We propose a novel concept of k-anonymity based on co-localization, that exploits the inherent uncertainty of the moving object's whereabouts. Expand
  • 127
  • 19
Anonymizing moving objects: how to hide a MOB in a crowd?
TLDR
In this paper, we study the problem of privacy-preserving publishing of moving object database. Expand
  • 151
  • 15
  • PDF