Probing Limits of Information Spread with Sequential Seeding

We consider here information spread which propagates with certain probability from nodes just activated to their not yet activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. Such activation is commonly performed in a single stage. A novel approach based on sequential seeding is analyzed here resulting in three fundamental contributions. First, we propose a coordinated execution of randomized choices to enable precise comparison of different algorithms in general. We apply it here when the newly activated nodes at each stage of spreading attempt to activate their neighbors. Then, we present a formal proof that sequential seeding delivers at least as large coverage as the single stage seeding does. Moreover, we also show that, under modest assumptions, sequential seeding achieves coverage provably better than the single stage based approach using the same number of seeds and node ranking. Finally, we present experimental results showing how single stage and sequential approaches on directed and undirected graphs compare to the well-known greedy approach to provide the objective measure of the sequential seeding benefits. Surprisingly, applying sequential seeding to a simple degree-based selection leads to higher coverage than achieved by the computationally expensive greedy approach currently considered to be the best heuristic

A picture is worth a thousand words: an empirical study on the influence of content visibility on diffusion processes within a virtual world

Studying information diffusion and the spread of goods in the real world and in many digital services can be extremely difficult since information about the information flows is challenging to accurately track. How information spreads has commonly been analysed from the perspective of homophily, social influence, and initial seed selection. However, in virtual worlds and virtual economies, the movements of information and goods can be precisely tracked. Therefore, these environments create laboratories for the accurate study of information diffusion characteristics that have been difficult to study in prior research. In this paper, we study how content visibility as well as sender and receiver characteristics, the relationship between them, and the types of multilayer social network layers affect content absorption and diffusion in virtual world. The results show that prior visibility of distributed content is the strongest predictor of content adoption and its further spread across networks. Among other analysed factors, the mechanics of diffusion, content quality, and content adoption by users’ neighbours on the social activity layer had very strong influences on the adoption of new content.</p

Finding Influential Users in Social Media Using Association Rule Learning

Abstract: Influential users play an important role in online social networks since users tend to havean impact on one other. Therefore, the proposed work analyzes users and their behavior in orderto identify influential users and predict user participation. Normally, the success of a social mediasite is dependent on the activity level of the participating users. For both online social networkingsites and individual users, it is of interest to find out if a topic will be interesting or not. In thisarticle, we propose association learning to detect relationships between users. In order to verify thefindings, several experiments were executed based on social network analysis, in which the mostinfluential users identified from association rule learning were compared to the results from DegreeCentrality and Page Rank Centrality. The results clearly indicate that it is possible to identify themost influential users using association rule learning. In addition, the results also indicate a lowerexecution time compared to state-of-the-art methods