- 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

Le séminaire de l'équipe ComplexNetworks est un rendez-vous bi-mensuel
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diffusion, contacter
Pedro Ramaciotti Morales.

Drawing and Visualising Event-Based Dynamic Graphs.

May 27th, 11h Room 24-25-405. UPMC - Sorbonne Université.

4 Place de Jussieu, 75005 Paris.

**Abstract**

One of the most important types of data in data science is the graph or network. Networks encode relationships between entities: people in social network, genes in biological network, and many others forms of data. These networks are often dynamic and consist of a set of events -- edges/nodes with individual timestamps. In the complex network literature, these networks are often referred to as temporal networks. As an example, a post to a social media service creates an edge existing at a specific time and a series of posts is a series of such events. However, the majority of dynamic graph visualisations use the timeslice, a series of snapshots of the network at given times, as a basis for visualisation. In this talk, I present two recent approaches for event-based network visualisation: DynNoSlice and the Plaid. DynNoSlice is a method for embedding these networks directly in the 2D+t space-time cube along with methods to explore the contents of the cube. The Plaid is an interactive system for visualising long in time dynamic networks and interaction provenance through interactive timeslicing.