8. A computer-readable medium containing instructions for controlling a computer system to identify a community of objects of a designated type, by a method comprising: providing indications of objects of different types and of relationships between objects of different types, each relationship having an associated time; generating a heterogeneous graph with vertices representing distinct objects and edges representing the relationships between objects, each edge having a time period and a weight; generating a time vector heterogeneous graph from the heterogeneous graph, the time vector heterogeneous graph having a vertex for each vertex of the heterogeneous graph and an edge between objects representing a relationship between the objects, each edge having a time vector representing the weights of the relationship over time periods extracting features relating to objects of the designated type from the objects and their relationships as indicated by the generated time vector heterogeneous graph, the extracted features representing evolution of the features over time periods; providing training data including features of objects within the community, the training data including best community training data generated by, for each time period and each object of the designated type that is within the community for the time period: setting best community training data to the extracted features for that object; labeling the best community training data for that object with a rank of the highest ranking community that that object is within; and setting a time period for the best community training data for that object; and the training data further including multi-class community classifier training data generated by, for each time period, each community, and object within the community within that time period: setting multi-class community training data to the extracted features for that object; labeling the multi-class community training data for that object with the community; and setting a time period for the multi-class community training data for that object; and classifying an object of the designated type as being within the community based on closeness of the features of the object to the features of the training data that includes best community training data and multi-class community training data such that when the object is classified as a potential community member for multiple communities based on the best community training data, classifying the object as a potential community member for a single community based on the multi-class community training data.
8. A computer-readable medium containing instructions for controlling a computer system to identify a community of objects of a designated type, by a method comprising: providing indications of objects of different types and of relationships between objects of different types, each relationship having an associated time; generating a heterogeneous graph with vertices representing distinct objects and edges representing the relationships between objects, each edge having a time period and a weight; generating a time vector heterogeneous graph from the heterogeneous graph, the time vector heterogeneous graph having a vertex for each vertex of the heterogeneous graph and an edge between objects representing a relationship between the objects, each edge having a time vector representing the weights of the relationship over time periods extracting features relating to objects of the designated type from the objects and their relationships as indicated by the generated time vector heterogeneous graph, the extracted features representing evolution of the features over time periods; providing training data including features of objects within the community, the training data including best community training data generated by, for each time period and each object of the designated type that is within the community for the time period: setting best community training data to the extracted features for that object; labeling the best community training data for that object with a rank of the highest ranking community that that object is within; and setting a time period for the best community training data for that object; and the training data further including multi-class community classifier training data generated by, for each time period, each community, and object within the community within that time period: setting multi-class community training data to the extracted features for that object; labeling the multi-class community training data for that object with the community; and setting a time period for the multi-class community training data for that object; and classifying an object of the designated type as being within the community based on closeness of the features of the object to the features of the training data that includes best community training data and multi-class community training data such that when the object is classified as a potential community member for multiple communities based on the best community training data, classifying the object as a potential community member for a single community based on the multi-class community training data. 11. The computer-readable medium of claim 8 including training a classifier based on the training data to classify objects of the designated type based on their features as being members of the community.