Lionel Martin

ICML Conference 2018 Conference Paper

Fast Approximate Spectral Clustering for Dynamic Networks

• Lionel Martin
• Andreas Loukas
• Pierre Vandergheynst

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clustering, i. e. , computing the graph eigenvectors, by a polynomial-based randomized sketching technique. We show that the proposed algorithm achieves clustering assignments with quality approximating that of spectral clustering and that it can yield significant complexity benefits when the graph dynamics are appropriately bounded. In our experiments, our method clusters 30k node graphs 3. 9$\times$ faster in average and deviates from the correct assignment by less than 0. 1%.

AAAI Conference 2014 Conference Paper

Prediction of Helpful Reviews Using Emotions Extraction

• Lionel Martin
• Pearl Pu

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners’ attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.

ECAI Conference 2004 Conference Paper

PoBOC: An Overlapping Clustering Algorithm, Application to Rule-Based Classification and Textual Data

• Guillaume Cleuziou
• Lionel Martin
• Christel Vrain