PPT-ON THE EDGES OF FLOW:

STUDENT ENGAGEMENT IN PROBLEM SOLVING Peter Liljedahl Liljedahl P under review On the edges of flow Student problem solving behavior In S Carreira N Amado amp K Jones eds . An or ta es on is how much of ou cargo can be sh ipp desti na io using dif feren flig ava il ab le answ er th quest we ex pl ore wha is ed et or f lo gra ph a nd sh ho m del fferen proble ms using such gra et or lo gra ph V E is re te grap wit wo ec Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Efficiency of Iterative Algorithms. Reducible Flow Graphs. Jeffrey D. Ullman. Stanford University. Flow Graph Theory. 2. Roadmap. Proper ordering of nodes of a flow graph speeds up the iterative algorithms: “. Overview. . Recap:. Min Cost Flow, Residual Network. Potential and Reduced Cost. Polynomial Algorithms. Approach. Capacity Scaling. Successive . Shortest . Path Algorithm Recap. Incorporating Scaling. COSC 494 – Graph Theory. 4/9/2014. Definitions. History. Examplse. (graphs, sample problems, . etc. ). Applications. State of the art, open problems. References. HOmework. Connectivity. Separating Set. Bipartite Matching. Alexandra Stefan. Flow Network. A . flow network. is a . directed. graph G = (V,E) in which each edge, (. u,v. ) has a . non-negative capacity, c(. u,v. ) ≥ 0. , and for any pair of vertices (. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. CS302, Spring . 2013 . David Kauchak. Admin. CS lunch today. Grading. Flow graph/networks. S. A. B. T. 20. 20. 1. 0. 1. 0. 30. Flow network. directed, weighted graph (V, E). positive edge . Polymatroidal. Networks. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Joint work with . Sreeram. . Kannan. , Adnan Raja, . Pramod. . Viswanath. (UIUC ECE Department). Paper available at http. Hans Bodlaender. Teacher. 2. nd. Teacher Algorithms and Networks. Hans Bodlaender. Room 503, Buys Ballot Gebouw. Schedule:. Mondays: Hans works in Eindhoven. A&N: Maximum flow. 2. A&N: Maximum flow. Jeff Edmonds. York University. COSC 6111. Lecture. . 3. Network Flow. Linear Programming. Grad Algorithms. Ingredients: . Instances:. The possible inputs to the problem. . . Solutions for Instance:. Network flow. Network flow provides a way to model a wide range of algorithmic problems. Many problems can be solved by reducing to a network-flow problem!. Does not seem to be efficiently solvable using the techniques we have discussed so far. for. . (. s,t. )-. mincuts. Surender Baswana. Department of CSE, IIT Kanpur. Joint work with . Koustav. . Bhanja. Research supported by . Tapas Mishra Memorial Chair. (. ,. ). -. cut.  . ,. . with.