To Do

  • Add to flatten definition
    • Any edge that can be traversed in the interval can be included. That is, first include all edges that exist for the entirety of the interval, then add those edges that can be reached from any of those edges.
  • What about cost functions? What about the weight of the edge as well as how long it takes to traverse the edge?
  • Temporal metric definitions
    • For , consider also the measure here with varying intervals around the timepoint . So, .
    • One thing to think about: sampling an image every and might be deceiving if all the dark pixels fall in that sampling pattern. So instead, you’d blur the image with a Gaussian and then sample, so that the picture looked more “gray.” This harkens to the image pyramids from computer vision, where the image was downsampled to get various different views and resolutions out of it, and could be upsampled to produce a fuzzy replication image. Can we do something like this for our graph over time points? Specifically, if we look in varying-size neighborhoods of when computing a metric at that point in time–utilizing one of our flattening methods–can we get different but interesting pictures of the dynamics of the graph?
    • Formally define in terms of and our definitions of flattening. What does that look like? Do we apply multiple different flattening schemes? (Worthy was focused on applying them in order, as varying levels of edge inclusion.)
      • Could one option be: the transitive closure of all paths that could legitimately be taken over the rest of the graph’s lifetime? Write that up as an option
    • With these new definitions and notions, start with connectedness measures over these graphs.
    • Some different ideas:
      • From our TIVG , perform the operator to get new graph . Compare the output of metrics on both of those graphs, and as changes.
      • Also do the same with TIVG after applying identity function $f_i$.
      • Next, try applying the identity function $f_i$ directly to the original and transformed graphs, and . How do their identity-function variants compare? Are they similar to the ones where the order of operations is reversed?
        • How does applying then transform compare with applying transform and then ?
  • Start moving the copy into the proposal document for continuity
  • Pushing these variations on time back into the measure definitions should help bring dynamics into the measure definitions
  • Characterizing the dynamics of the measures so they are not so flat (as they originally were)
    • start with connectedness
  • Unrelated to dos
    • Emailed Give Shayne access to my git repo for Nauvoo. Also make sure he knows about creating the empty writable submissions.txt
    • Emailed Speak with Barbara Graves or Pam Norris
      • Part time vs On Leave
      • What is the cost of tuition and fees for part time?
      • What are the special conditions for leave status? Is a full-time job enough? What about conditions for part-time status?
      • What are the drawbacks of on leave if I’m a staffer at the university?
      • Does leave count against the 7-year graduation window? What about part-time? (Worthy wants to double check)
    • If I want to do leave, I’d need to email Worthy and CC Norris and Graves and ask for it
    • Talk with student health and UVA HR
      • Is there an extension to the graduate health insurance that would cover me until the job starts? In case it’s not until Sept 1?
    • DONE Go ahead and email Jeff Holt again
    • Course to sit-in in the fall is now a maybe (after earlier saying it would be fine)

To Do

  • If we have an event driven time version of our graph, and we want to specify a , what is the method/mapping for doing that flattening?
    • This is the mapping of event driven time to an evenly spaced time (wall clock time)
    • Or, from one to another which could be larger
    • What happens to the node identities with regard to this flatting down of time?
      • What about if a node merges with another or splits?
    • What if an edge comes and goes inside of one ? Do we consider that a connection there, or not? Is it shown or not? How does that translate under mapping to a different value?
      • DONE Look back at Bruno’s paper to see what they considered if an edge both came and went inside of one timestep. Did they consider it? If it doesn’t say, email and ask him about it.
        • They just do the union of all edges during the interval.
    • When mapping, what do we do?
      • aggregate the changes that happen inside one time unit? If there are two things (edge create, edge delete), what happens if they undo each other in the same timestep?
      • aggregate the changes into the next timestep? (everything that happens changes the graph, and it’s apparent at the next time step)
      • morph time so that an even number of things happen in each timestep, although we’ve now messed up ’s spacing?
  • (low prio) Merging and splitting of nodes in a graph
    • Even under one identity mapping, what happens when nodes split? Think circuses or corporate identities
    • If “flattening” the graph for the future (think ), what do you do if a node has split (or merged)? Do you consider them to be separate nodes? Are the edges still connected to the one node or multiple nodes? One thought would be a super node with the smaller nodes inside of it (so some connection to each other, but not an edge-like connection–an identity connection).
    • We need to know this when comping a measure for the “rest of time.”
  • (low prio) Write paragraphs on the distance measures (and how they might take into account ordering of edges)
    • Physical constraints on what counds as a path (ie ordering in time)
      • This would be things like traffic patterns, information that spreads through sets of phone calls, etc
    • Kinds of things that can be encoded in the distance metric itself. There’s an inherent caveat on the distance metric in a directed graph vs an undirected graph, namely that an edge can only be traversed (distance calculated) if it’s travelling in the right direction.
  • We want to characterize the dynamics of the graph, which these current metric extensions I’ve done is missing
    • This is the value of the metrics we’re creating
  • What about doing the metrics over time intervals? (so instead of t, have (t1, t2)). What about all the other segments of time from the earlier Evolving Networks definition?
  • Possible changes in edges (without changing the identity) could be a reversal of direction
  • Parameterize the definitions better. For example, , where and we’re considering a specific identity . The entire graph should be a parameter, not just the identity.
  • Specify exactly the definition of “flattening” for .
  • DONE Fix ’s definition to use .
  • Better define as it relates to . Worthy would rather see everything parameterized than using subscripts and superscripts.
  • DONE Review Tang’s paper again to know exactly what their path definition is (are they inserting transitive links within each snapshot, or is the path actually listing every node at every time?), the exact definition of , and centrality as it relates to time . ( has received or is holding a message at time ?)

To Do

  • If looking at UVA on data entry page, can you add links to Brown data there?
    • Update the if statement to show Brown IDs if available
  • Add a link to the UVA all people list from the data entry page and rename the Brown list link

Discussion

  • There is a strong desire to merge with BYU’s newest DB
  • Familial order was the shadow structure of this other structure (church organization), then she needs to know the ordination information
    • Where baptism occurred, when baptism, time between baptism and sealing, etc
    • Affinities created out of this structure: ordination levels, heirarchies, etc
    • She doesn’t see these questions being answered for decades

Discuss

  • Luther on Committee? (Has relevant research in time and graphs)
  • Still reaching out to Jeff Holt; haven’t heard anything in the last week.
  • Tuition for the fall? How should I register?
  • CS1010 would be cool

To Do

  • Continue writing up formal definitions and metrics
  • Start writing proposal outline into proposal document

To Do

  1. do

Notes