• Publications
  • Influence
Topic-aware social influence propagation models
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
Fast detection of XML structural similarity
We present an approach for detecting structural similarity between XML documents which significantly differs from standard methods based on graph-matching algorithms, and allows a significant reduction of the required computation costs. Expand
Topic-Aware Social Influence Propagation Models
We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-worldExpand
How Can SMEs Benefit from Big Data? Challenges and a Path Forward
Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Expand
A Tree-Based Approach to Clustering XML Documents by Structure
We propose a novel methodology for clustering XML documents by structure, which is based on the notion of XML cluster representative. Expand
Cascade-based community detection
We propose the Community-Cascade Network (CCN) model, a stochas- tic mixture membership generative model that can fit, at the same time, the social graph and a set of cas- cades. Expand
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. Expand
Who to follow and why: link prediction with explanations
We propose a stochastic topic model for link prediction with explanations for user recommendation in social networks. Expand
Detecting Structural Similarities between XML Documents
We propose a technique for detecting the similarity in the structure of XML documents by exploiting the Discrete Fourier Transform of the corresponding signals. Expand
Towards An Adaptive Mail Classifier
The problem of detecting unsolicited (spam) e-mails, analyzing newsletter and mailing-listmessages, and rapidly detecting important messages (and separating them from unimportantones), has become readily actual. Expand