In recent years, with the explosive growth of real networks and structured data sets, a new class of graphs came to light. This kind of graphs are huge and very sparse in general, with some prevailing characteristics. The structure of such networks is hard to describe in general and, moreover, the structure is only a starting point. When we think about complex networks, we should take into account connectedness both at the level of structure and of behavior. This means that, in addition to tools to analyze network structure, we also need a framework for reasoning about behavior and interaction in network contexts, where a single event may cause subtle cause-effect events. Although it is commonly accepted that structure has influence on behavior, to our knowledge little work has been done on how dynamics influence network structure. On the other hand, since complete observation may not be possible and tinkering with real systems may lead to unexpected disruptions, suitable simulation models and tools are a must. This project comes in this line of research, with the aim of developing new models and tools for the study the relationship between large networks structure and processes dynamics.
Not seeing the forest for the trees: Size of the minimum spanning trees (MSTs) forest and branch significance in MST-based phylogenetic analysis, by A. S. Teixeira, P. T. Monteiro, M. Ramirez, J. A. Carriço, and A. P. Francisco. PLoS One, to appear. Code and data.
TypOn: The Microbial Typing Ontology, by C. Vaz, A. P. Francisco, M. Silva, K. A. Jolley, J. E. Bray, H. Pouseele, J. Rothgaenger, M. Ramirez, and J. A. Carriço. Journal of Biomedical Semantics, 2014. Website. TypOn is also available at BioPortal. [pdf]
PHYLOViZ: Phylogenetic inference and data visualization for sequence based typing methods, by A. P. Francisco, C. Vaz, P. T. Monteiro, J. Melo-Cristino, M. Ramirez, and J. A. Carriço. BMC Bioinformatics, 13:87, 2012. PHYLOViZ website. Source code is also available at Bitbucket.
Quick HyperVolume, by L. M. S. Russo and A. P. Francisco. IEEE Transactions on Evolutionary Computation, 2013. A preliminary version is available at CoRR abs/1207.4598. Website.
Spanning edge betweenness, by A. S. Teixeira, P. T. Monteiro, J. A. Carriço, M. Ramirez, and A. P. Francisco. In Mining and Learning with Graphs - Knowledge Discovery and Data Mining (MLG-KDD'13), ACM 2013. [pdf]
Degrees of separation on a dynamic social network, by A. Domingos, H. Ferreira, P. Rijo, C. Vaz, and A. P. Francisco. In Mining and Learning with Graphs - Knowledge Discovery and Data Mining (MLG-KDD'13), ACM 2013. Details on how we used the Webgraph framework are also available. [pdf]
Unravelling communities of ALS patients using network mining, by A. V. Carreiro, S. C. Madeira, and A. P. Francisco. In Data Mining on Healthcare - Knowledge Discovery and Data Mining (DMH-KDD'13), ACM 2013. [pdf]
Topological representation of the within-species evolutionary structure of bacterial populations: the SLV graph, by P. T. Monteiro, A. P. Francisco, M. Ramirez, and J. A. Carriço. INESC-ID Technical Report 16/2014. Source code.
Large scale simulation of bacterial population evolution over host contact networks, by A. S. Teixeira, V. Ribeiro, P. T. Monteiro, M. Ramirez, J. A. Carriço, and A. P. Francisco. INESC-ID Technical Report 17/2014. Source code.