In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
We propose a visualization approach for large dynamic graph structures with high degree variation and low diameter. In particular, we reduce visual complexity by multiple modes of ...
Despite significant research on state-space reductions, the poor scalability of model checking for reasoning about behavioral models of large, complex systems remains the chief ob...
Matthew B. Dwyer, Robby, Xianghua Deng, John Hatcl...
: Recent work on social networks has tackled the measurement and optimization of these networks’ robustness and resilience to both failures and attacks. Different metrics have be...
Software systems of today are characterized by the increasing size, complexity, distribution and heterogeneity. Understanding and supporting the interaction between software requir...