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» Approximate Discovery of Random Graphs
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KDD
2005
ACM
162views Data Mining» more  KDD 2005»
14 years 8 months ago
Discovering frequent topological structures from graph datasets
The problem of finding frequent patterns from graph-based datasets is an important one that finds applications in drug discovery, protein structure analysis, XML querying, and soc...
Ruoming Jin, Chao Wang, Dmitrii Polshakov, Sriniva...
BMCBI
2010
124views more  BMCBI 2010»
13 years 7 months ago
Estimation and efficient computation of the true probability of recurrence of short linear protein sequence motifs in unrelated
Background: Large datasets of protein interactions provide a rich resource for the discovery of Short Linear Motifs (SLiMs) that recur in unrelated proteins. However, existing met...
Norman E. Davey, Richard J. Edwards, Denis C. Shie...
EJC
2007
13 years 7 months ago
Deterministic random walks on the integers
Jim Propp’s P-machine, also known as the ‘rotor router model’ is a simple deterministic process that simulates a random walk on a graph. Instead of distributing chips to ran...
Joshua N. Cooper, Benjamin Doerr, Joel H. Spencer,...
AAAI
2007
13 years 10 months ago
Compact Spectral Bases for Value Function Approximation Using Kronecker Factorization
A new spectral approach to value function approximation has recently been proposed to automatically construct basis functions from samples. Global basis functions called proto-val...
Jeffrey Johns, Sridhar Mahadevan, Chang Wang
FOCS
2008
IEEE
14 years 2 months ago
Beating the Random Ordering is Hard: Inapproximability of Maximum Acyclic Subgraph
We prove that approximating the Max Acyclic Subgraph problem within a factor better than 1/2 is Unique-Games hard. Specifically, for every constant ε > 0 the following holds:...
Venkatesan Guruswami, Rajsekar Manokaran, Prasad R...