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Titlebook: Algorithms and Models for the Web Graph; 9th International Wo Anthony Bonato,Jeannette Janssen Conference proceedings 2012 Springer-Verlag

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樓主: 嚴(yán)峻
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發(fā)表于 2025-3-23 12:01:31 | 只看該作者
12#
發(fā)表于 2025-3-23 14:54:06 | 只看該作者
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發(fā)表于 2025-3-23 21:24:19 | 只看該作者
Uro? J. Jovanovi? (Priv.-Dozent)ery fixed .?>?0, there is no (..)-approximation algorithm, even if the input graph is restricted to have unit weight arcs, maximum out-degree three, and two sinks. We also present a polynomial time algorithm for solving the DAG Partitioning problem in graphs with bounded pathwidth.
14#
發(fā)表于 2025-3-23 22:11:24 | 只看該作者
Herdaktivation durch Schlaf oder Medikamentehe spatial embedding of the nodes models the background knowledge or identity of the node, which influences its link environment. In this paper, we focus on the (directed) diameter, small separators, and the (weak) giant component of the model.
15#
發(fā)表于 2025-3-24 04:34:17 | 只看該作者
On a DAG Partitioning Problem,ery fixed .?>?0, there is no (..)-approximation algorithm, even if the input graph is restricted to have unit weight arcs, maximum out-degree three, and two sinks. We also present a polynomial time algorithm for solving the DAG Partitioning problem in graphs with bounded pathwidth.
16#
發(fā)表于 2025-3-24 08:31:09 | 只看該作者
17#
發(fā)表于 2025-3-24 11:25:43 | 只看該作者
Conference proceedings 2012of topics related to the complex networks such hypergraph coloring games and voter models; algorithms for detecting nodes with large degrees; random Appolonian networks; and a sublinear algorithm for Pagerank computations.
18#
發(fā)表于 2025-3-24 16:46:31 | 只看該作者
19#
發(fā)表于 2025-3-24 19:19:16 | 只看該作者
M. Hennerici,M. Daffertshofer,S. Meairs. We demonstrate the effectiveness of the evolving teleportation on the Wikipedia graph and the Twitter social network. The external interest is given by the number of hourly visitors to each page and the number of monthly tweets for each user.
20#
發(fā)表于 2025-3-25 03:06:07 | 只看該作者
Physiologie der Hirndurchblutung,he situation that the demand distribution evolves mostly in clusters around the initial vertices and the case that the demands spread to the whole network. We establish sharp upper bounds for the probability that the demands are satisfied as a function of PageRank vectors.
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