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ICML
2010
IEEE
13 years 8 months ago
Random Spanning Trees and the Prediction of Weighted Graphs
We show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the...
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio...
ESA
2008
Springer
130views Algorithms» more  ESA 2008»
13 years 9 months ago
Linear Time Planarity Testing and Embedding of Strongly Connected Cyclic Level Graphs
Abstract. A level graph is a directed acyclic graph with a level assignment for each node. Such graphs play a prominent role in graph drawing. They express strict dependencies and ...
Christian Bachmaier, Wolfgang Brunner
NIPS
2008
13 years 9 months ago
Online Prediction on Large Diameter Graphs
We continue our study of online prediction of the labelling of a graph. We show a fundamental limitation of Laplacian-based algorithms: if the graph has a large diameter then the ...
Mark Herbster, Guy Lever, Massimiliano Pontil
SDM
2009
SIAM
138views Data Mining» more  SDM 2009»
14 years 4 months ago
ShatterPlots: Fast Tools for Mining Large Graphs.
Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A deep, recurring...
Ana Paula Appel, Andrew Tomkins, Christos Faloutso...
PAKDD
2004
ACM
131views Data Mining» more  PAKDD 2004»
14 years 28 days ago
Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models
This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the ...
Qiming Huang, Qiang Yang, Joshua Zhexue Huang, Mic...