The development of wireless devices led the scientific community to focus more and more on systems of interaction composed of moving entities. In this context, different models have been proposed in an attempt to capture properties of the observed dynamics. Among those models, the edge-Markovian evolving graph model is appealing since it enables to highlight temporal dependencies in the evolution of the graphs. This model relies on two parameters accounting respectively for the creation and suppression of links in the graph. Thus it assumes that these two parameters are sufficient to characterise the dynamics during all the evolution of the graph. In this paper, we test this hypothesis by confronting the model to 6 datasets recording real traces of evolving networks. In particular, we study the proportion of created and deleted links over the time. The results show that 5 of the 6 case studies present an heterogeneous distribution of those fractions which contradicts the underlying hypothesis of the model. Besides, in order to understand the importance this might have as regard structural properties of real networks, we also study the impact the Markovian model has on the mean degree over the time. It turns out that even in the suitable case, the model fails to reproduce correctly this property which indicates its inadequacy for even more complex properties of real evolving networks

### Next Event(s)

**Community detection in attributed graphs.**Christine Largeron*2017, April 25, Room 24-25/405*- affinity index algorithm analysis antipaedo attack bipartite blog network blogs capitalisme social Cascade centrality clustering communities community detection community structure complex network complex networks complex systems compression connected graphs data mining debian degree distribution degree peeling diameter diffusion diffusion phenomena distributed measurements DynamicNetworks dynamics edge-Markovian evolving graph eDonkey ego-centered ego-centered communities email epidemiology event detection evolving graphs evolving networks exploration failure fixed points formal concepts gossip graph graph algorithm graph decompositions Graphs hierarchical clustering honeypot influence influence ranking interaction networks internal links internet Internet topology intrinsic time IP-level ip exchanges lattice leaders link prediction long term communities markovian model measurement mesure dâ€™influence metrics Metrology mobile networks Modelling modularity multi-ego-centered communities multi-scale multipartite graph network dynamics node proximity node similarity opinion dynamics outliers p2p P2P dynamics P2P networks parametric paris paris-traceroute path-vector routing pedophile activity phone power-law radar random graph random walks reachability robustness routing routing tables scale-free security simulation simulations sir social networks spreading spreading cascades stability statistical analysis stochastic process three-state cellular automata time-varying Topology traceroute triangles twitter UDP user profiles viral marketing visualization web wifi