We study the fundamental problem of information spreading (also known as gossip) in dynamic networks. In gossip, or more generally, k-gossip, there are k pieces of information (or tokens) that are initially present in some nodes and the problem is to disseminate the k tokens to all nodes. The goal is to accomplish the task in as few rounds of distributed computation as possible. The problem is ...

We present a new methodology for predicting the spread of information in a social network. We focus on the Twitter network, where information is in the form of 140 character messages called tweets, and information is spread by users forwarding tweets, a practice known as retweeting. Using data of who and what was retweeted, we train a probabilistic collaborative filter model to predict future r...

We want computer systems that can help us assess the similarity or relevance of existing objects (e.g., documents, functions, commands, etc.) to a statement of our current needs (e.g., the query). Towards this end, a variety of similarity measures have been proposed. However, the relationship between a measure’s formula and its performance is not always obvious. A geometric analy sis is advance...

Most taxonomies and thesauri offer their users a huge amount of structured data. However, this volume of data is often excessive, and, thus does not fulfill the needs of the users, who are trying to find specific information related to a certain concept. While there are techniques that may partially alleviate this problem (e.g. visual representation of the data), some of the effects of the info...

The authors model the coevolution dynamics of information diffusion, resource allocation and epidemic spreading, show that diffusion may worsen spreading subject to allocations limited resources.