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Efficient Identification of Web Communities
 In Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2000
"... We de ne a community on the web as a set of sites that have more links (in either direction) to members of the community than to nonmembers. Members of such a community can be eciently identi ed in a maximum ow / minimum cut framework, where the source is composed of known members, and the sink c ..."
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Cited by 228 (12 self)
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We de ne a community on the web as a set of sites that have more links (in either direction) to members of the community than to nonmembers. Members of such a community can be eciently identi ed in a maximum ow / minimum cut framework, where the source is composed of known members, and the sink consists of wellknown nonmembers. A focused crawler that crawls to a xed depth can approximate community membership by augmenting the graph induced by the crawl with links to a virtual sink node. The effectiveness of the approximation algorithm is demonstrated with several crawl results that identify hubs, authorities, web rings, and other link topologies that are useful but not easily categorized. Applications of our approach include focused crawlers and search engines, automatic population of portal categories, and improved ltering.
A Simple Combinatorial Proof of Duality of Multiroute Flows and Cuts
, 2004
"... A classical ow is a nonnegative linear combination of unit ows along simple paths. A multiroute ow, rst considered by Kishimoto and Takeuchi, generalizes this concept. The basic building blocks are not single paths with unit ows but rather tuples consisting of k edge disjoint paths, each path with ..."
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Cited by 9 (4 self)
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A classical ow is a nonnegative linear combination of unit ows along simple paths. A multiroute ow, rst considered by Kishimoto and Takeuchi, generalizes this concept. The basic building blocks are not single paths with unit ows but rather tuples consisting of k edge disjoint paths, each path with a unit ow. A multiroute ow is a nonnegative linear combination of such tuples.
The Maximum Flow Algorithm Applied to the Placement and Distributed SteadyState Control of FACTS Devices
 Control of FACTS Devices, Proceedings of the 2005 North American Power Symposium
, 2005
"... The bulk power system is one of the largest manmade networks and its size makes control an extremely difficult task. This paper presents a method to control a power network using UPFCs set to levels determined by a maximum flow (maxflow) algorithm. The graphtheorybased maxflow is applied to the p ..."
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Cited by 5 (0 self)
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The bulk power system is one of the largest manmade networks and its size makes control an extremely difficult task. This paper presents a method to control a power network using UPFCs set to levels determined by a maximum flow (maxflow) algorithm. The graphtheorybased maxflow is applied to the power system for UPFC placement and scheduling. A distributed version of maxflow is described to coordinate the actions of the UPFCs distributed in a power network. Two sample power systems were tested using maxflow for UPFC placement and settings. The resulting system characteristics are examined over all singleline contingencies and the appropriateness of the maximum flow algorithm for power flow control is discussed.
Linear Discrepancy of Totally Unimodular Matrices
 Combinatorica
, 2001
"... We show that the linear discrepancy of a totally unimodular mn matrix A is at most lindisc(A) 1 1 n+1 : This bound is sharp. In particular, this result proves Spencer's conjecture lindisc(A) (1 1 n+1 ) herdisc(A) in the special case of totally unimodular matrices. If m 2, we also show lin ..."
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Cited by 5 (3 self)
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We show that the linear discrepancy of a totally unimodular mn matrix A is at most lindisc(A) 1 1 n+1 : This bound is sharp. In particular, this result proves Spencer's conjecture lindisc(A) (1 1 n+1 ) herdisc(A) in the special case of totally unimodular matrices. If m 2, we also show lindisc(A) 1 1 m . Finally we give a characterization of those totally unimodular matrices which have linear discrepancy 1 1 n+1 : Besides m 1 matrices containing a single nonzero entry, they are exactly the ones which contain n + 1 rows such that each n thereof are linearly independent. A central proof idea is the use of linear programs. A preliminary version of this result appeared at SODA 2001. This work was partially supported by the graduate school `Eziente Algorithmen und Multiskalenmethoden', Deutsche Forschungsgemeinschaft y A similar result has been independently obtained by T. Bohman and R. Holzman and presented at the Conference on Hypergraphs (Gyula O.H. Katona is 60), Budapest, in June 2001. Mathematics Subject Classication (2000): Primary 11K38, 90C05. Secondary 05C65. Proposed abbreviated title: Linear Discrepancy. 2 1
Optimal MinimumSurface Computations Using Network Flow
"... We give a computationallyefficient solution to a discrete version of the "Plateau problem" on minimal surfaces. Our approach is based on a novel transformation using network flows to find minimumcost slabs, which correspond to minimal "surfaces" of prescribed thickness. An implementation confir ..."
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Cited by 2 (0 self)
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We give a computationallyefficient solution to a discrete version of the "Plateau problem" on minimal surfaces. Our approach is based on a novel transformation using network flows to find minimumcost slabs, which correspond to minimal "surfaces" of prescribed thickness. An implementation confirmed that this approach is viable for computing minimal surface solutions for a variety of problem instances.
Bornholm Network Analysis
, 2002
"... dk+itc.dk edu Figure 3: Centrality. Most cited Danish and Swedish universities from the \edu" and \ac.uk" domains. Number of links obtained with AllTheWeb search engine. Normalization? Students: 18220 for AU, 28356 for KU, 5755 for DTU. Citation count on Danish and Swedish universities from \edu ..."
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dk+itc.dk edu Figure 3: Centrality. Most cited Danish and Swedish universities from the \edu" and \ac.uk" domains. Number of links obtained with AllTheWeb search engine. Normalization? Students: 18220 for AU, 28356 for KU, 5755 for DTU. Citation count on Danish and Swedish universities from \edu and \ac.uk" domains. Conclusion(?): A rhus University and Swedish (Link oping, Uppsala, Lund) universities \better" than Copenhagen (note diku.dk and nbi.dk). NORMALIZED CENTRALITY Normalization count in scientometrics: Journal impact factor for journal evaluation with Science Citation Index (SCI) (Gar eld, 1972) Journal impact factor = Times cited Articles published (4) Journal A: 6/5 = 1.2 Journal B: 9/2 = 4.5 A1 B1 A2 A3 A4 A5 B2 C1 C2 C3 C4 C5 C6 Figure 4: Normalized centrality. Which one has the highest rank: A2 or B2? ITERATIVEBASED CENTRALITY Inherite the centrality of inlinks (Bonacich, 1972), (Scott, 1991, p. 91) c j = i a ij c i (5)