Tools designed to extract main content from web pages require thorough evaluation, yet existing benchmarks disproportionately focus on English-language datasets. Consequently, previous studies have shown that while these extractors are well-optimized for English, their effectiveness partially or entirely diminishes in other languages. This study reproduces and extends recent benchmarks by incorporating multilingual datasets as a key factor. We analyze extractor performance across five languages-Greek, English, Polish, Russian, and Chinese-highlighting the need to adapt extraction models to linguistic variations. Our results show that while some extractors maintain stable performance, others suffer significant drops in precision and recall on non-English or structurally irregular pages.

This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We propose a function called compactness, designed to assess the quality of a solution to this problem. Our algorithm is based on a graph traversal algorithm, the LexDFS. The time complexity of our method is in O(n × log(n)). Experiments show that our algorithm creates highly compact clusters.

We propose a flexible proactive data dissemination approach for data gathering in self-organized Wireless Sensor Networks (WSN). Our protocol Supple, effectively distributes and stores monitored data in WSNs such that it can be later sent to or retrieved by a sink. Supple empowers sensors with the ability to make on the fly forwarding and data storing decisions and relies on flexible and self-organizing selection criteria, which can follow any predefined distribution law. Using formal analysis and simulation, we show that Supple is effective in selecting storing nodes that respect the predefined distribution criterion with low overhead and limited network knowledge.

To make sure they can quickly respond to a specific query, the main search engines have several mechanisms. One of them consists in ranking web pages according to their importance, regardless of the semantic of the web page. Indeed, relevance to a query is not enough to provide a high quality result, and popularity is used to arbitrate between equally relevant web pages. Webspam widely denotes any web page created with the only purpose of fooling ranking algorithms such as the PageRank. The aim of Webspam is to promote a target page by increasing its rank. It is an important issue for Web search engines to spot and discard Webspam to provide their users with a non biased list of results. Webspam techniques have to evolve constantly to remain efficient but most of the time they consist in creating a specific linking architecture around the target page to increase its rank. In this paper we propose to study the effects of graph clustering on the well known ranking algorithm of Google (the PageRank) in presence of Webspam. Since the web graph is way to big to apply classic clustering techniques, we present three lightweight techniques to realise a clustering of the web graph. Experimental results show the interest of the approach, which is moreover confirmed by statistical evidence.