Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

The strong influence of substrate conductivity on droplet evaporation

We report the results of physical experiments that demonstrate the strong influence of the thermal conductivity of the substrate on the evaporation of a pinned droplet. We show that this behaviour can be captured by a mathematical model including the variation of the saturation concentration with temperature, and hence coupling the problems for the vapour concentration in the atmosphere and the temperature in the liquid and the substrate. Furthermore, we show that including two ad hoc improvements to the model, namely a Newton's law of cooling on the unwetted surface of the substrate and the buoyancy of water vapour in the atmosphere, give excellent quantitative agreement for all of the combinations of liquid and substrate considered

A "water shell" model for the dielectric properties of hydrated silica-filled epoxy nano-composites

The electrical properties of epoxy resin have been studied as a function of hydration. The epoxy was studied in an un-filled state, filled with 40 µm SiO2 particles, and filled with 50 nm SiO2 particles. The relative humidity was controlled by saturated salt solutions at ambient temperatures from 298-353 K. Measurements were made using dielectric spectroscopy over the frequency range 10-3-105 Hz. The hydration isotherm (i.e. the mass uptake of water) was established by measuring the mass as a function of relative humidity (RH). It was found that the nanocomposites absorb up to 60% more water than the unfilled and micro-filled epoxies. Dielectric spectroscopy shows different conduction and quasi-DC behaviours at very low frequencies (<10-2 Hz) with activation energies dependent on the hydration and temperature. These observations have led to the development of a “water shell” model to explain this phenomenon