Phil 12.18.17

7:15 – 4:15 ASRC MKT

  • I’m having old iPhone problems. Trying a wipe and restart.
  • Exploring the ChestXray14 dataset: problems
    • Interesting article on using tagged datasets. What if the tags are wrong? Something to add to the RB is a random re-introduction of a previously tagged item to see if tagging remains consistent.
  • Continuing Consensus and Cooperation in Networked Multi-Agent Systems here
  • Visualizing the Temporal Evolution of Dynamic Networks (ACM MLG 2011)
    • Many developments have recently been made in mining dynamic networks; however, effective visualization of dynamic networks remains a significant challenge. Dynamic networks are typically visualized via a sequence of static graph layouts. In addition to providing a visual representation of the network topology at each time step, the sequence should preserve the “mental map” between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network and gain valuable insights that are difficult to convey by summary statistics alone. We propose two regularized layout algorithms for visualizing dynamic networks, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). These algorithms discourage node positions from moving drastically between time steps and encourage nodes to be positioned near other members of their group. We apply the proposed algorithms on several data sets to illustrate the benefit of the regularizers for producing interpretable visualizations.
    • These look really straightforward to implement. May be handy in the new flocking paper
  • Opinion and community formation in coevolving networks (Phys Review E)
    • In human societies, opinion formation is mediated by social interactions, consequently taking place on a network of relationships and at the same time influencing the structure of the network and its evolution. To investigate this coevolution of opinions and social interaction structure, we develop a dynamic agent-based network model by taking into account short range interactions like discussions between individuals, long range interactions like a sense for overall mood modulated by the attitudes of individuals, and external field corresponding to outside influence. Moreover, individual biases can be naturally taken into account. In addition, the model includes the opinion-dependent link-rewiring scheme to describe network topology coevolution with a slower time scale than that of the opinion formation. With this model, comprehensive numerical simulations and mean field calculations have been carried out and they show the importance of the separation between fast and slow time scales resulting in the network to organize as well-connected small communities of agents with the same opinion.
  • I can build maps from trajectories of agents through a labeled belief space: mapFromTrajectories
    • This would be analogous to building a map based on terms or topics used by people during multiple group polarization discussion. Densely connected central area where all the discussions begin, sparse ‘outer region’ where the poles live. In this case, you can clearly see the underlying grid that was used to generate the ‘terms’
  • Progress for today. Size is the average time spent ‘over’ a topic/term. Brightness is the number of distinct visitors: mapFromTrajectories2

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