DyNo 2016 - 2nd International Workshop on Dynamics in Networks (DyNo 2016)

In the last years we witnessed to a shift from static network analysis to a dynamic networks analysis, i.e., the study of networks whose structure change over time.
As time goes by, all the perturbations which occur on the network topology due to the rise and fall of nodes and edges have repercussions on the network phenomena we are used to observe. As an example, evolution over time of social interactions in a network can play an important role in the diffusion of an infectious disease.
Nowadays, one of the most fascinating challenges is to analyze the structural dynamics of real world networks and how they impact on the processes which occurs on them, i.e. the spreading of social influence and diffusion of innovations. Results in this field will enable for a better understanding of important aspects of human behaviors as well as to a more detailed characterization of the complex interconnected society we inhabit. Since the last decades, diffusive and spreading phenomena were facilitated by the enormous popularity of the Internet and the evolution of social media that enable an unprecedented exchange of information. For this reason, understanding how social relationships unravel in these rapidly evolving contexts represents one of the most intriguing field of research ever.
The purpose of this workshop is to encourage principled research that will lead to the advancement of the social science in time-evolving networks. The workshop will seek top- quality submissions addressing important topics such as: dynamic network modeling, time- aware network mining approaches, social influence spreading, diffusion processes in dynamic networks and forecast of network topology perturbation.
Topics of interest to the workshop include, but are not limited to, the following:
Temporal network modeling
Dynamic Social Network Analysis
Dynamical processes on temporal networks
Social Prominence and Influence maximization
Diffusion of information and innovations
Epidemic models on graphs
Evolutionary Community Discovery
Time-aware Link Prediction
Dynamic network generative models
Multiplex network dynamics
Dynamic Network Visualization