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» Graph-Based Induction for General Graph Structured Data
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POPL
2006
ACM
15 years 10 months ago
Verifying properties of well-founded linked lists
We describe a novel method for verifying programs that manipulate linked lists, based on two new predicates that characterize reachability of heap cells. These predicates allow re...
Shuvendu K. Lahiri, Shaz Qadeer
SAC
2009
ACM
15 years 4 months ago
Applying latent dirichlet allocation to group discovery in large graphs
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Keith Henderson, Tina Eliassi-Rad
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
15 years 10 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
SDM
2009
SIAM
161views Data Mining» more  SDM 2009»
15 years 7 months ago
Polynomial-Delay and Polynomial-Space Algorithms for Mining Closed Sequences, Graphs, and Pictures in Accessible Set Systems.
In this paper, we study efficient closed pattern mining in a general framework of set systems, which are families of subsets ordered by set-inclusion with a certain structure, pro...
Hiroki Arimura, Takeaki Uno
ICML
2004
IEEE
15 years 10 months ago
Semi-supervised learning using randomized mincuts
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...