Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
From Architecture to Function (and Back) in Bio-networks
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Exponential random graph modeling of communication networks to understand organizational crisis
Proceedings of the 49th SIGMIS annual conference on Computer personnel research
Computing subgraph isomorphic queries using structural unification and minimum graph structures
Proceedings of the 2011 ACM Symposium on Applied Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational & Mathematical Organization Theory
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Static analysis and exponential random graph modelling for micro-blog network
Journal of Information Science
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Motivation: The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus on identifying simple, but prominent, structural components, as such components are easier to understand, and, once identified, can be used as building blocks to succinctly describe the network. Results: In recent social network studies, exponential random graph models have been used extensively to model global social network structure as a function of their ‘local features’. Starting from those studies, we describe the exponential random graph models and demonstrate their utility in modeling the architecture of biological networks as a function of the prominence of local features. We argue that the flexibility, in terms of the number of available local feature choices, and scalability, in terms of the network sizes, make this approach ideal for statistical modeling of biological networks. We illustrate the modeling on both genetic and metabolic networks and provide a novel way of classifying biological networks based on the prevalence of their local features. Contact: saul@cs.ucdavis.edu