Understanding the spread of information on complex networks is a key issue from a theoretical and applied perspective. Despite the effort in developing theoretical models for this phenomenon, gauging them with large-scale real-world data remains an important challenge due to the scarcity of open, extensive and detailed data. In this paper, we explain how traces of peer-to-peer file sharing may be used to this goal. We reconstruct the underlying social network of peers sharing content and perform simulations on it to assess the relevance of the standard SIR model to mimic key properties of real spreading cascades. First we examine the impact of the network topology on observed properties. Then we turn to the evaluation of two heterogeneous extensions of the SIR model. Finally we improve the social network reconstruction, introducing an affinity index between peers, and simulate a SIR model which integrates this new feature. We conclude that the simple, homogeneous model is insufficient to mimic real spreading cascades. Moreover, none of the natural extensions of the model we considered, which take into account extra topological properties, yielded satisfying results in our context. This raises an alert against the careless, widespread use of this model.

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**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 d’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