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Titlebook: Algorithms and Models for the Web-Graph; 7th International Wo Ravi Kumar,Dandapani Sivakumar Conference proceedings 2010 The Editor(s) (if

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31#
發(fā)表于 2025-3-26 23:00:09 | 只看該作者
A Sharp PageRank Algorithm with Applications to Edge Ranking and Graph Sparsification,e integer .. The improved PageRank algorithm is crucial for computing a quantitative ranking of edges in a given graph. We will use the edge ranking to examine two interrelated problems – graph sparsification and graph partitioning. We can combine the graph sparsification and the partitioning algori
32#
發(fā)表于 2025-3-27 01:51:11 | 只看該作者
Efficient Triangle Counting in Large Graphs via Degree-Based Vertex Partitioning,is to combine the sampling algorithm of [31,32] and the partitioning of the set of vertices into a high degree and a low degree subset respectively as in [1], treating each set appropriately. We obtain a running time . and an . approximation (multiplicative error), where . is the number of vertices,
33#
發(fā)表于 2025-3-27 09:02:57 | 只看該作者
Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types,ts can be defined by many different metrics and aggregation of these metrics into a single one poses several important challenges, such as recovering this aggregation function from ground-truth, investigating the space of different clusterings, etc. In this paper, we address how to find an aggregati
34#
發(fā)表于 2025-3-27 13:31:49 | 只看該作者
35#
發(fā)表于 2025-3-27 14:07:18 | 只看該作者
36#
發(fā)表于 2025-3-27 19:03:16 | 只看該作者
37#
發(fā)表于 2025-3-28 00:27:57 | 只看該作者
Finding and Visualizing Graph Clusters Using PageRank Optimization, given graph ., we use the personalized PageRank vectors to determine a set of clusters, by optimizing the jumping parameter . subject to several cluster variance measures in order to capture the graph structure according to PageRank. We then give a graph visualization algorithm for the clusters usi
38#
發(fā)表于 2025-3-28 03:21:13 | 只看該作者
39#
發(fā)表于 2025-3-28 06:19:53 | 只看該作者
The Geometric Protean Model for On-Line Social Networks,es are identified with points in Euclidean space, and edges are stochastically generated by a mixture of the relative distance of nodes and a ranking function. With high probability, the GEO-P model generates graphs satisfying many observed properties of OSNs, such as power law degree distributions,
40#
發(fā)表于 2025-3-28 14:15:35 | 只看該作者
Constant Price of Anarchy in Network Creation Games via Public Service Advertising,t to construct a connection graph among themselves. Each node wants to minimize its average or maximum distance to the others, without paying much to construct the network. Many generalizations have been considered, including non-uniform interests between nodes, general graphs of allowable edges, bo
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