Tag relationship modeling and prediction

A system, comprising:

• at least one processor;
• and
• a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• receiving items that are published on a network, each of the items including one or more tags specified by an author of a respective item;
• analyzing the items to determine a plurality of graphs, wherein each graph models co-occurrences, during a respective time period, of pairs of tags in the items, each of the graphs including:
• a plurality of nodes, each node corresponding to a tag that is included in the items published during the respective time period;
• and
• one or more edges, each edge having a weight that indicates a number of co-occurrences of a respective pair of tags in the items published during the respective time period;
• generating a model that describes one or more changes, over time, in the plurality of graphs;
• and
• employing the model to predict the number of future co-occurrences of at least one pair of tags in items that are subsequently published on the network.

One or more computer-readable media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• receiving items that are published on a network, each of the items including one or more tags specified by an author of a respective item;
• analyzing the items to determine a plurality of graphs, wherein each graph models co-occurrences, during a respective time period, of pairs of tags in the items, each of the graphs including:
• a plurality of nodes, each node corresponding to a tag that is included in the items published during the respective time period;
• and
• one or more edges, each edge having a weight that indicates a number of co-occurrences of a respective pair of tags in the items published during the respective time period;
• generating a model that describes one or more changes, over time, in the plurality of graphs;
• and
• employing the model to predict the number of future co-occurrences of at least one pair of tags in items that are subsequently published on the network.