System and method for attentive clustering and analytics

1. A method, comprising:

constructing an online author network, wherein constructing the online author network comprises selecting a set of source nodes (S), a set of outlink targets (T), and a set of edges (E) between S and T defined by the at least one selected type of hyperlink from S to T during a specified time period;

deriving a set of nodes, T′, by normalizing nodes in T;

transforming the online author network into a matrix of source nodes in S linked to targets in T′;

partitioning the online author network into at least one set of source nodes with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle;

generating a graphical representation of attentive clusters and/or outlink bundles in the network to enable interpretation of network features and behavior and calculation of comparative statistical measures across the attentive clusters and outlink bundles,

wherein at least one element of the graphical representation depicts a measure of an extent of a type of activity within the network; and

measuring frequencies of links between attentive clusters and outlink bundles enabling identification and measurement of large-scale regularities in the distribution of attention by online authors across sources of information.

2. The method of claim 1 , wherein the element of the graphical representation uses at least one of size, thickness, color and pattern to depict a type of activity.

3. The method of claim 1 , wherein attentive clusters are differentiated in the graphical representation by at least one of a color, a shape, a shading, and a size.

4. The method of claim 1 , wherein the size of the element representing the attentive clusters in the graphical representation correlates with a metric.

5. The method of claim 1 , wherein the nodes, targets, and edges are collected from public and private sources of information.

6. The method of claim 1 , wherein constructing the matrix comprises applying at least one threshold parameter from the group consisting of: maxnodes, targetmax, nodemin, targetmin, maxlinks, and linkmin.

7. The method of claim 1 , wherein constructing the matrix comprises applying a minimum threshold for the number of included nodes that must link to a target to qualify it for inclusion in the matrix.

8. The method of claim 1 , wherein constructing the matrix comprises applying a minimum threshold for the number of included targets that must link to a node to qualify it for inclusion in the matrix.

9. The method of claim 1 , wherein the matrix is a graph matrix.

10. The method of claim 1 , wherein the nodes are normalized to a selected level of abstraction.

11. The method of claim 1 , wherein the set of outlink targets (T) are selected from at least one selected type of hyperlink.

12. The method of claim 1 , wherein the set of source nodes (S) are selected from at least one selected type of node.

13. The method of claim 1 , further comprising, applying any lists specifying inclusion or exclusion of particular nodes.

14. A method, comprising:

constructing an online author network, wherein constructing the online author network comprises selecting a set of source nodes (S), a set of outlink targets (T), and a set of edges (E) between S and T defined by the at least one selected type of hyperlink from S to T during a specified time period;

deriving a set of nodes, T′, by normalizing nodes in T;

transforming the online author network into a matrix of source nodes in S linked to targets in T′;

partitioning the online author network into at least one set of source nodes with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle; and

measuring frequencies of links between attentive clusters and outlink bundles enabling identification and measurement of large-scale regularities in the distribution of attention by online authors across sources of information.

15. The method of claim 14 , further comprising:

generating a graphical representation of attentive clusters and/or outlink bundles in the network to enable interpretation of network features and behavior and calculation of comparative statistical measures across the attentive clusters and outlink bundles,

wherein at least one element of the graphical representation depicts a measure of an extent of a type of activity within the network.

16. The method of claim 14 , wherein the set of outlink targets (T) are selected from at least one selected type of hyperlink.

17. The method of claim 14 , wherein the set of source nodes (S) are selected from at least one selected type of node.

18. The method of claim 14 , wherein the nodes are normalized to a selected level of abstraction.

19. The method of claim 14 , further comprising, applying any lists specifying inclusion or exclusion of particular nodes.

20. A method of attentive clustering, comprising:

defining at least one semantic bundle; and

calculating relevance scores for nodes based on that bundle; and

selecting a subset of nodes based in whole or in part on the relevance scores; and

partitioning an online author network of the subset into at least one set of source nodes with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle.

21. A method, comprising:

partitioning an online author network into at least one set of source nodes with a similar linking history to form an attentive cluster and at least one set of outlink targets with a similar citation profile to form an outlink bundle;

generating a graphical representation of link targets, semantic events, and node-associated metadata scattered in an x-y coordinate space, wherein the dimensions of the graph are custom-defined using sets of attentive clusters grouped to represent substantive dimensions of interest for a particular analysis.