Computer Science PhD Candidate

9/2/2010

2:00pm-4:00pm

A graph is connected if there is a path between any two of its vertices and k-connected if there are at least k disjoint paths between any two vertices. A graph is k-edge-connected if none of the k paths share any edges and k-vertex-connected (or k-connected) if they do not share any intermediate vertices. We examine some problems related to k-connectivity and an application.

We have looked at the k-edge-connected spanning subgraph problem: given a k-edge-connected graph, find the smallest spanning subgraph that is still k-edge-connected. We improved two algorithms for approximating solutions to this problem. The first algorithm transforms the problem into an integer linear program, relaxes it into a real-valued linear program and solves it, then obtains an approximate solution to the original problem by rounding non-integer values. We have improved the approximation ratio by giving a better scheme for rounding the edges and bounding the number of fractional edges. The second algorithm finds a subgraph where every vertex has a minimum degree, then augments the subgraph by adding edges until it is k-edge-connected. We improve this algorithm by bounding the number of edges that could be added in the augmentation step.

We have also applied the idea of k-connectivity to protein-protein interaction (PPI) networks, biological graphs where vertices represent proteins and edges represent experimentally determined physical interactions. Because few PPI networks are even 1-connected, we developed algorithms to find the most highly connected subgraphs of a graph. We applied our algorithms to a large network of yeast protein interactions and found that the most highly connected subgraph was a 16-connected subgraph of membrane proteins that had never before been identified as a module and is of interest to biologists. We also looked at graphs of proteins known to be co-complexed and found that a significant number contained 3-connected subgraphs, one of the features that most differentiated complexes from random graphs.

Committee: |
Debra Goldberg, Assistant Professor (Co-Chair)Harold (Hal) Gabow, Professor (Co-Chair)John Black, Associate ProfessorAndrzej Ehrenfeucht, Distinguished ProfessorLawrence Hunter, University of Colorado School of MedicineSan Skulrattanakulchai, Gustavus Adolphus College |

Department of Computer Science

University of Colorado Boulder

Boulder, CO 80309-0430 USA

webmaster@cs.colorado.edu

University of Colorado Boulder

Boulder, CO 80309-0430 USA

webmaster@cs.colorado.edu