Abstract: A network disruption detection engine gathers and stores network routing path update messages that routinely modify routing tables used by internet routers for transporting message traffic between a source and a destination. Routing path messages continually maintain a network transport infrastructure both for intranets used for particular entities such as corporations, and internet traffic between arbitrary source and destination nodes. Major disruptions or outages typically result in an increase in routing path messages, typically focused on a particular set, region or network entity where the disruption occurred, as other routing entities seek to avoid the troubled region. Analysis of this sudden activity of routing messages and extracting message content about the network region they seek to avoid allows identification and queries of a widespread network outage.

Abstract: A software library recommendation engine provides an analysis and aggregate dashboard comparison of metrics denoting maintainability trends in a plurality of libraries from a publicly available software repository. Maintainability trends include regularity and magnitude of changes (commits), resolution of user inquiries for issues, problems and bugs, and an estimation of core contributors for estimating inertia and longevity of a plurality of candidate libraries under consideration for a particular usage. Usage metrics coalesce and summarize usage data of libraries under consideration for comparison, and a dashboard of computed metrics provides an indication of trends that indicate reliability or longevity to mitigate vulnerability of the library user from dependence on the collaborative intent of the library author and contributors.

Abstract: A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.

Abstract: A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.

Abstract: A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.