Abstract: Techniques are described for generating graphs that describe relationships between tags included in items published on a network, and for analyzing the graphs to develop a model that describes the changes in relationships between tags over time. Implementations provide an analysis platform in which published items are analyzed, using machine learning-trained model(s), to model and predict relationships between tags and/or changes in the strength and presence of the relationships between tags. The relationships between tags can be used to generate one or more graphs. Through graph-based modeling of the manner in which correlated pairs of tags change in the strength of their correlation (e.g., their relationship strength) over time, implementations can generate predictions regarding how a correlation between tags is likely to change in the future, and can also generate recommendations regarding how a particular correlation may be maintained over time.

Abstract: Techniques are described for predicting location information and/or other characteristics of an author of item(s) published on a network, based on one or more tags (e.g., hashtags) that are included in the published item(s). Published items that are geotagged with location information are used to train, using machine learning techniques, a model that predicts the location of the author of non-geotagged item(s) based on the tag(s) included in the non-geotagged item(s). Model(s) may also be trained to predict other characteristics of authors of items. Implementations predict the location, and/or other characteristics, of individuals on networks (e.g., social networks) in instances where location and/or other characteristics are not otherwise known, thus enabling more effective targeting of individuals for marketing, advertising campaigns, and/or other types of influence application that may be performed on the network.

Abstract: Techniques are described for network data analysis and graph clustering analysis to determine clusters of users publishing on networks. Network data, such as items published on social networks or other online networks, is analyzed to determine categories for the published items, and a strength of a correlation between the category and the user who published the item. The category and/or correlation strength are determined based on an analysis (e.g., natural language analysis) of text data included in the published item. Based the various correlations between users and categories, correlations may be determined between pairs of users. A graph may be generated that graphically depicts the various category correlations and/or user correlations as determined based on a set of network data. Clustering is performed to determine cluster(s) of users who are (e.g., strongly) correlated and similar to one another with regard to their category correlations.

Abstract: Techniques are described for network data analysis and graph clustering analysis to determine clusters of users publishing on networks. Network data, such as items published on social networks or other online networks, is analyzed to determine categories for the published items, and a strength of a correlation between the category and the user who published the item. The category and/or correlation strength are determined based on an analysis of image data included in the published item. Based the various correlations between users and categories, correlations may be determined between pairs of users. A graph may be generated that graphically depicts the various category correlations and/or user correlations as determined based on a set of network data. Clustering is performed to determine cluster(s) of users who are (e.g., strongly) correlated and similar to one another with regard to their category correlations.

Abstract: Techniques are described for measuring and ranking the influence of users in a network such as a social network. Influence metric(s) may be calculated that indicate the influence of publishing user(s) in a network based on calculating a number of users who are exposed to published and republished item(s) at one or more levels, or ripples, as the item(s) propagate in the network through republication. Publishing users may be ranked according to their degree of influence in general, or their degree of influence with respect to one or more particular categories of subject matter for the published item(s). Categories may include hierarchically related categories, sub-categories, and so forth, of varying specificity.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a first data stream from a sensor of a network of sensors monitoring well-site parameters. Obtaining a first feature vector associated with the first data stream. Determining a potential well-site event by identifying, among a stored set of well-site event models, a second feature vector from an event model that correlates with the first feature vector, where the event model includes the potential well-site event. Then, sending an alert to a user device, where the alert informs a user of the potential well-site event.