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Mike Langston’s Progress Report Fall, 2004

PROGRESS REVIEW Mike Langston’s Research Team Department of Computer Science University of Tennessee with collaborative efforts at Oak Ridge National Laboratory 30 November 2004.

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Mike Langston’s Progress Report Fall, 2004

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  1. PROGRESS REVIEWMike Langston’s Research TeamDepartment of Computer ScienceUniversity of Tennesseewith collaborative efforts atOak Ridge National Laboratory30 November 2004

  2. Team Members in AttendanceNicole Baldwin, John Eblen, Mike Langston, Jon Scharff, Josh Steadmon, Henry Suters, Chris Symons, Yun ZhangTeam Members AbsentDaniel Lucio, Ian Watkins, Xinxia Peng

  3. Mike Langston’s Progress ReportFall, 2004 • Team Changes • Graduating: Nicole Baldwin, Henry Suters • Professional Leave Update • ORNL, Collaborations, Team Opportunities • Recent Talks • IWPEC (Bergen, Norway), TMGC (Fall Creek Falls) • Recent Visits • LBNL (San Francisco), UCSD (San Diego), SC2004 (Pittsburgh) • Upcoming Conference Trips • AICCSA (Cairo), RTST (Lebanon) • Recent Panel Duties • NSF • Recent Program Committee Service • AICCSA, PDCS, IWPEC, HiCOMB

  4. Nicole BaldwinGeneral Conclusions • Bron and Kerbosch (modified version) • “Best worst-case” algorithm • Best experimental times (unless very sparse) • Kose et al. • Too much core memory, or • Too many I/O operations • Useful on Altix?? • Preprocessing useful for mid-range density • Paracliques appear to correspond well with QTL data

  5. Work at ORNL • Infrastructure proposal • Learning experience • Help extract what participants REALLY need • Sandia-ORNL GTL proposal • Postulate queries for integrated database • Research regulatory/metabolic pathway reconstruction. • Goal: build an in-house computational biology center

  6. John EblenORNL Research • Installed direct maximum clique codes • Installing maximal clique codes • Installing parallel versions of above on Altix supercomputer • Currently processing Dr. Gerling’s mouse genome data set • General goal, though, is a pipeline with tools, procedures, etc. for processing any large data set

  7. Other Research • Clique common neighbor algorithms • Current algorithm suffers from data explosion • Many different possible approaches • Random graph walks • Idea: collect statistics to help guess clique locations • Current algorithm seems to give little or no gain over simply using vertex degrees • Chordal graphs • Clique solvable in polynomial time if graph is chordal • Correlation graphs should be very close to chordal

  8. Updates from Xinxia • Sept.: MS in CS defense • Oct.: poster presentation on 7th Annual Conference on Computational Genomics (http://www.tigr.org/conf/cg/) • Nov.: oral presentation on CAMDA 2004 (http://www.camda.duke.edu/camda04)

  9. Jon Scharff Possible Future Directions • Look into possibility of adding threads to vc branching (with Chris?) • Look at other possible maximal clique algorithms (see Faisal) • Look into parallelizing maximal clique codes (with Yun?)

  10. Group Webpage We have a design, now we need content…

  11. Henry Suters • Defended thesis: Crown Reductions and Decompositions: Theoretical Results and Practical Methods • Beginning part time position at ORNL • Beginning a project on using the structure MCS graphs to aid in kernelization • Triplets!

  12. FPT Solvable in O(1.92k + n3) Implemented O(3k + n3) version Easily parallelizable Produces a disjoint union of cliques Chris SymonsCluster Editing k=3: 2 insertions; 1 deletion

  13. The following 2 rules produce a k2 kernel Rule 1: If 2 vertices have more than k common neighbors, they must have an edge between them If 2 vertices have more than k non-common neighbors, they must not have an edge between them If they have both k common and non-common neighbors, then this is a “no” instance Rule 2: Delete connected components that are cliques. Cluster Editing Rule 1: If k=1, edge (u,v) must be inserted. v u Rule 2: component 2 can be removed. component 2 component 1

  14. Yun ZhangWork at ORNL • Maximum Common Subgraph problem (with Chris) • Transform to Maximum Clique (MC) problem by constructing an association graph • MC in association graph  MCS of two graphs • Solve MC problem using VC codes • Can handle directed, labeled graphs • Can’t handle very large graphs • Due to the huge size of association graph (|V1|×|V2|) • Preprocess association graph (with Henry) • Applications: biology, chemistry, pattern recognition

  15. Other Works • Cleanup and update VC codes (with Faisal) • Modulization all codes • Make VC codes as libraries • Provide a suite of applications: VC, MC, MCS, SAT, … • Three kernelization methods: LP, Network Flow, Crown Reduction • Look into parallelizing maximal clique codes

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