To Do

1. Email Glenn
2. Think (hard) about evolving networks, and the representation and computation issues with regard to these measures that we’re interested in
   • Write down the measures we’re interested in
   • Create a list of the measures, the group of measures that we’re interested in and that make sense (in or out of our domains, but domain-specific relations would be helpful)
     • Along with the measures write down the computation issues that could arise with each
3. Write a list of the expanse of our domain and problems
   • Write down the modes of variation (what counts as evolution)
     • Characteristics of nodes change
     • Characteristics of edges change
     • Edges come and go
     • Nodes come and go
     • …
   • Types of Time
     • Event-driven time
     • Wall-clock time
   • Consider all temporal ways any measure could be computed over the evolving network
     • All time
     • One time point (snapshot)
     • Interval
       • Has a start point, end point, and temporal-width
     • Changing Interval (perhaps sliding over time, sliding window)
     • Contiguous intervals
     • Non-contiguous intervals
     • Union of intervals (contiguous and non)
   • Comparison across graphs
     • Variations that make comparison hard
     • Comparison of intervals
       • Are they in lock step?
         • Do the events happen at the same time?
         • Are they both using wall clock time with the same time-step?
         • Are they the same length?
       • Do the intervals in the two different graphs under comparison have different, independent trajectories?
     • If one is a fast-event time and the other is a slow-event time, is it fair to compare them? Is it able to compare them? Is it a fair comparison?
   • Get out the whole spectrum of evolving characteristics and variations that we are considering
     • What can people look at for their datasets and networks that will say whether or not our work is a reasonable thing to consider computing over their data. Or if it is even possible.
     • Getting out all the expanses could bring to light some good measures that will fit into these spaces
     • Also for the proposal: put some bounds on things
       • “We’re not doing this”
         • Limit what we’re considering
         • We’re not solving everyone’s network problems, only this subset of issues

Agenda

1. Committee Members: Dave, Alf, Gabe, Worthy, Glenn (need to email him)
   • Who would be a good person to add as a minor representative?
2. New diagrams
   • Thoughts?
   • Does it convey the story?
3. Sundry
   • Finding errors in the database: people mislabeled from data entry
   • Joseph should be finishing the adoptions, I still don’t have them yet
   • Should we alter the database to have this new marriage events table?
4. Proposal timeline
   • Would like to have the final draft to committee before wedding, no later than Nov 7, 2014.
   • Propose early-to-mid December
5. Research questions
   • Should I look into submitting viz to a CS conference, as Luther suggested? Would I need to present something different than the workshop?

Pre-Notes

Other great datasets to consider:

• Web graph, where nodes consist of companies
  • Companies come and go, also buy, create, and retire websites
• Citeseer citation graph, using universities/corporations as nodes, co-authorship as edges

Other notes:

• Rewrite chord to advance to next event
• Can we avoid storing snapshots? Using snapshots??

Notes

• Possible Committee Members and Ideas
  • Jeff Holt, in Statistics
  • Hongning Wang (New CS Faculty): would be good to talk to, but not necessarily add to the committee (discuss with him his work with Bing)
  • Dan Keenan, in Statistics
• Good contacts
  • Karen Kafadar (Chair of Statistics)
  • Amber Tomas, postdoc in Statistics interested in evolving networks
• Why stats would be interesting to work with
  • Wall clock versus event time
    • Intervals and measures over intervals
    • Intervals that change and have pieces/changes of the network fall off the beginning of the interval
    • Trajectories over the interval(s)
    • Distributions over times, and the characteristics of these distributions
• How much change in attributes (of a node or edge) needs to happen before there is a change in identity (of that node or edge)?
  • When does the node/eedge change it’s character?
    • This change is then an imposed event on the graph (the change is an event)
    • If we relax or tighten the identity changing function, then that affects the events in the graph
  • Identity change is a function of attributes in the node (or edge)
• What are the measures we’re talking about? This NEEDS to be hammered down NOW so that I can have a chance of proposing this fall and writing a proposal document before the wedding.
  • Measures over an interval
  • What about letting the interval move across time?
    • Incremental calculation sounds like a good candidate here
    • Obviously could naively recalculate the measure, but incremental updating would be much better
  • Expanding and contracting the window/interval. What would that look like?
• Proposal has two main functions
  • Working committee, giving ideas and engaged in the problem
  • One-sided contract: if you do what they’ve agreed on, it’s enough. But other things could also be considered enough if you continue discussions with the committee passed the proposal.
• Look toward writing a succinct statement of the thesis
  • After writing some measures and the computational issues therein, start thinking about combining them into a thesis:
    • This measure is interesting in this application, and you can do it in this manner.
      • Can become sort of an algorithms thesis at this point.
      • Do you want to move in the algorithms direction?
        • Ex: Statements of time complexity or lower bounds, “you can’t do it faster than $O(-)$
        • Ex: Can amortize the costs over …
        • Ex: If you have $V$ nodes and $E$ edges, then you can expect …
• Start writing down the list (see above)