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We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides...
The large amount of user generated data that Online Social Networks produce has remarkably drawn the attention for researchers on human behavior in the recent years. In this work, we use temporal series and complex network analysis to unveil the users' behavioral patterns during the Spanish presidential electoral campaigns in Twitter. We introduce a new measure to study political sentiment in Twitter,...
In this preliminary work we propose an approach to tracking network communities in time. We describe a methodology to study the dynamics and evolution of the MEDLINE bibliographic database using network-based analysis. We explore how the temporal characteristics of the network can be used to provide insight into the historical evolution of the broad field of biomedicine.
Social Networks are evolving rapidly to have a significant number of users using or joining their platforms daily, and a huge volume of information exchange and flow continuously. As human is the center of all information traffic, the information propagation became subjected to the relations and influences between the sources of these information and their followers or fans, these influences might...
Dynamics of interactions play an increasingly important role in the analysis of complex networks. A modeling framework to capture this are temporal graphs. We focus on enumerating Δ-cliques, an extension of the concept of cliques to temporal graphs: for a given time period Δ, a Δ-clique in a temporal graph is a set of vertices and a time interval such that all vertices interact with each other at...
Previous work introducing the idea of distribution-based network control determined that some seemingly similar networks can have significantly different levels of controllability. This work investigates these differences in controllability in more detail and finds that one of the driving factors behind controllability may be the influence dynamics within the network. These results suggest that existing...
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