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» Small world models for social network algorithms testing
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CIDM
2007
IEEE
14 years 1 months ago
Structure Prediction in Temporal Networks using Frequent Subgraphs
— There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective i...
Mayank Lahiri, Tanya Y. Berger-Wolf
CORR
2008
Springer
123views Education» more  CORR 2008»
13 years 7 months ago
Networks become navigable as nodes move and forget
Abstract. We propose a dynamic process for network evolution, aiming at explaining the emergence of the small world phenomenon, i.e., the statistical observation that any pair of i...
Augustin Chaintreau, Pierre Fraigniaud, Emmanuelle...
IJACTAICIT
2010
163views more  IJACTAICIT 2010»
13 years 4 months ago
Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance
In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot. It permits the detecti...
Bahaa I. Kazem, Ali H. Hamad, Mustafa M. Mozael
KDD
2012
ACM
271views Data Mining» more  KDD 2012»
11 years 10 months ago
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Many data are modeled as tensors, or multi dimensional arrays. Examples include the predicates (subject, verb, object) in knowledge bases, hyperlinks and anchor texts in the Web g...
U. Kang, Evangelos E. Papalexakis, Abhay Harpale, ...
AI
2002
Springer
13 years 7 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...