PROPAGATING INFORMATION THROUGH NETWORKS

• US 20140115010A1
• Filed: 02/27/2013
• Published: 04/24/2014
• Est. Priority Date: 10/18/2012
• Status: Abandoned Application

• Current Assignee: Google Inc. (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Seth, Rohan, Baluja, Shumeet, Covell, Michele
• Application Number:
  US13/778,361
• Publication Number:
  US 20140115010A1
• Time in Patent Office: Days
• US Class Current: 707/798
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Methods, and systems, including computer programs encoded on computer-readable storage mediums, including a method for providing a graph that includes entity nodes, label nodes and weighted connecting edges. The method comprises computing an aggregated incoming between-entity edge weight for the entity nodes. When there are positively-weighted incoming between-entity edges into the entity node, the method comprises replacing each of the between-entity edge weights by a pre-normalized between-entity edge weights. The method comprises computing an aggregated from-label weight for the entity node. When there are positively-weighted from-label node edges, the method comprises replacing the corresponding label weights by pre-normalized from-label weights. The method comprises determining influence values for a first, second and third influence factors, where the influence factors have values that sum to one. The method further comprises using the pre-normalized weights and influence factors as a set of linear constraints to determine final label weightings for the entity nodes.

Claims

1. A computer implemented method comprising:

providing data representing a data structure that includes a plurality of nodes, a portion of the nodes being entity nodes and a portion of the nodes being label nodes, wherein at least some entity nodes are connected to other entity nodes by one or more incoming or outgoing weighted edges, and wherein at least some label nodes are connected to entity nodes by one or more outgoing weighted edges;

for each entity node;

computing an aggregated incoming between-entity edge weight for the entity node including adding the weights of the edges that are incoming to the entity node from other entity nodes;

when there are one or more positively-weighted incoming between-entity edges into the entity node, replacing each of the between-entity edge weights by a respective initial edge weight of the between-entity edge divided by the aggregated incoming between-entity edge weight to generate pre-normalized between-entity edge weights;

computing an aggregated from-label weight by adding the label weights from label nodes with edges that are incoming to the entity node;

when there are one or more positively-weighted from-label node edges into the entity node, replacing each of the corresponding label weights from the label nodes by a respective initial label weight from the label node divided by the aggregated from-label weight to generate pre-normalized from-label weights;

determining influence values for each of a plurality of influence factors, where each influence factor is associated with a degree of propagation through the data structure of label weights to the entity nodes, and where the influence values are all non-negative and sum to one; and

using the pre-normalized from-label weights, the pre-normalized between-entity edge weights and the influence values as a set of linear constraints to determine final label weightings for the entity nodes.

2. The method of claim 1, further comprising:

when there are no positively-weighted from-label edges into the entity node, identifying the entity node as a without-direct-label node; and

for each entity node that is both a disconnected entity node and a without-direct-label node, removing the entity node from the data structure and removing any edges that originate from the entity node.

3. The method of claim 1, further comprising:

determining the final label weighting for the entity nodes including, for one or more iterations;

propagating labels from a previous iteration from each entity node to the other entity nodes to which the entity node is directly connected using a first propagation weighting given by a multiplicative combination of the pre-normalized between-entity edge weights and a second influence factor from incoming-from-entity-node edges, from the plurality of influence factors, for an entity node receiving the propagated label weights;

propagating labels from each label node to the entity nodes which the label node is directly connected using a second propagation weighting given by a multiplicative combination of the pre-normalized from-label edge weights and a first influence factor from direct-from-label injections, from the plurality of influence factors, for the receiving entity node; and

summing the propagated labels on a per-label basis to provide a current iteration'"'"'s label weight for each entity node.

4. The method of claim 1, further comprising:

determining the final label weightings for the entity nodes by solving a set of linear constraints using bi-conjugate gradient descent.

5. The method of claim 4, wherein:

determining the final label weightings comprises separating a computation of the bi-conjugate gradient descent to solve for each label independently.

6. The method of claim 5, wherein:

determining the final label weightings comprises eliminating label nodes and bypassing the computation of a label'"'"'s distribution for all labels whose final distribution does not substantially affect a solution to the set of linear constraints.

7. The method of claim 5, wherein:

determining the final label weightings comprises eliminating entity nodes from the computation for all entity nodes whose final label weighting does not substantially affect a solution to the set of linear constraints.

8. The method of claim 7, wherein eliminating entity nodes from the computation comprises eliminating entity nodes by Gaussian elimination of the entity node while maintaining the entity node'"'"'s influence on a final weighting distribution.

9. The method of claim 1, wherein the entity nodes represent social entities and the label nodes represent interest.

10. The method of claim 9, wherein the edge weights between entity nodes are determined by a number, length, or recentness of a message exchanged between the social entities.

11. The method of claim 1, wherein the entity nodes represent advertisement sites and the label nodes represent advertisement triggering keywords.

12. The method of claim 11, wherein the edge weights between entity nodes are determined by a number, consistency, or recentness of user visits to the advertisement sites within a single user session and within a pre-defined time period.

13. The method of claim 1, wherein the entity nodes represent content sites and the label nodes represent content topics.

14. The method of claim 13, wherein the edge weights between entity nodes are determined by a number, consistency, or recentness of user visits to the content sites within a single user session and within a pre-defined time period.

15. The method of claim 1, further comprising:

when there are no positively-weighted incoming between-entity edges into the entity node, identifying the entity node as a disconnected entity node.

16. The method of claim 1, wherein an influence value for a first influence factor from direct-from-label injections, from the plurality of influence factors, is set to zero for the entity nodes that are identified as a without-direct-label node and an influence value for a second influence factor from incoming-from-entity-node edges, from the plurality of influence factors, is set to zero for entity nodes that are identified as a disconnected entity node.

17. A method for propagating labels in a social graph comprising:

providing a social graph that includes a plurality of nodes and includes edges connecting the nodes, a portion of the nodes being user nodes, a portion of the nodes being injection nodes that inject a label into a respective user node and a portion of the nodes being uncertainty nodes that inject a measure of attenuation to be applied when propagating labels between user nodes;

determining for a given node an influence value for label weights for each of three factors;

influence for injections, influence for neighbors and influence for uncertainty;

determining weights for labels at each node including normalizing at receipt weights for labels that are propagated or injected into a user node including;

normalizing the weights for labels injected into a node, and adjusting the normalized weights by the influence factor for injections to produce an injected label weight contribution for the label for the node;

adjusting the weights for labels received from a neighbor by the influence factor for neighbors to produce a neighbor label weight contribution for the label for the node; and

using the weights and influence values as a set of linear constraints to determine final label weightings for the nodes.

18. The method of claim 17, wherein a sum of the influences is equal to one.

19. The method of claim 17, wherein normalizing at receipt weights for labels includes determining for each neighbor a contribution for a label being propagated to a target user node from the neighbor.

20. The method of claim 19, wherein normalizing on receipt further includes determining an influence factor for each neighbor that is contributing labels to a target user node, adjusting a label weight for a label propagated from the neighbor in accordance with the neighbors influence to produce a first adjusted label weight, and adjusting the first adjusted label weight by the influence factor for neighbors to produce the neighbor label weight contribution for the node.

21. The method of claim 20, wherein the influence factors for each neighbor sums to one.

22. The method of claim 20, wherein the influence factors for each neighbor are the same.

23. A method for propagating labels in a social graph comprising:

identifying a first user node in a social graph;

identifying one or more labels that are injected into the first user node, thereby associating respective labels with the first user node;

normalizing weights of the labels that are injected into the first user node to be equal to one;

determining an influence factor for injected labels for the first user node;

adjusting the normalized weights for the labels using the influence factor for injected labels to produce a injected label weight contribution for each label associated with the first user node;

determining if there are other neighbor nodes that contribute labels to the first user node;

determining an influence factor for the neighbor nodes;

when there are neighbor nodes, determining a neighbor node contribution for each label propagated from a respective neighbor node including identifying a label weight for a label from the neighbor node, adjusting the label weight based on an effective influence of the neighbor node on the first user node, and further adjusting the adjusted label weight based on the determined influence factor for the neighbor node; and

summing the neighbor node contribution with the injected weight contribution for each label for the first user node;

storing the sum for each label, producing a label weight for label for the first user node; and

propagating the label weight for each label to one or more neighbor nodes to the first user node.

24. The method of claim 23, further comprising attenuating a contribution of one or both of the neighbor nodes and injected labels to the label weights using an uncertainty factor.

25. The method of claim 24, wherein the values of influence factors for injected labels, the influence factor for neighbors and the uncertainty factor sum to equal 1.0.

26. The method of claim 25, wherein the attenuation is equal for both the influence factor for labels and the influence factor for neighbors.

27. A method for propagating labels in a social graph comprising:

identifying one or more labels associated with a user in a social graph, where the user is connected to one or more other users in the social graph;

identifying an influence value for label weights for each of at least two factors for the user;

influence for injections and influence for neighbors;

determining weights for labels for the user including normalizing at receipt weights for labels that are propagated or associated with a user including;

normalizing the weights for labels associated with the user and adjusting the normalized weights by the influence value for injections to produce an injected label weight contribution for the user;

adjusting the weights for labels received from a neighbor by the influence value for neighbors to produce a neighbor label weight contribution for the user; and

using the weights and influence values as a set of linear constraints to determine final label weightings for the user.

28. The method of claim 27, wherein identifying an influence value for label weights for each of at least two factors for the user further includes identifying an influence for uncertainty, wherein the influence for uncertainty is an attenuation factor for use in propagating the labels for a user to other neighbors.

29. The method of claim 28 wherein the sum of the influence factors is equal to one.

30. The method of claim 29, wherein the influence for uncertainty, when a positive non-zero value, effects to attenuate the influence of injected or neighbor weights on the weights of labels to be associated with a user.

31. A method implemented by a computer system, the method comprising:

identifying, by the computer system, a set of labels to be associated with users of a social network, the labels including one or more designators for specifying areas of interest for a respective user;

labeling, using the identified labels, nodes in a graph that represents the social network, wherein the users are represented by user nodes in the graph;

assigning, by the computer system and for each respective node, weights for the labels, each weight reflecting a magnitude of a contribution of an associated label to a characterization of the respective node, wherein assigning weights includes;

determining initial weights for each label that is either injected into a node or propagated from a neighbor node;

determining an influence factor for labels that are injected to a node and labels that are propagated from other neighbor nodes;

adjusting the initial weights based on the influence factors; and

using the adjusted weights and influence factors as a set of linear constraints to determine final label weightings for the nodes.

32. The method of claim 31, wherein identifying the set of labels further comprises retrieving the labels from a profile associated with a user associated with a respective node.

33. The method of claim 31, further comprising implicitly assigning labels to users based on historical activities of the user.

34. The method of claim 31, wherein labeling includes assigning labels based on an evaluation of content associated with a user.

35. The method of claim 31, wherein labeling includes assigning labels based on a combination of explicit designations and implicit determinations.

36. The method of claim 35, wherein implicit determinations are made based on user interaction with content.

37. The method of claim 36, wherein the user interactions are click-throughs.

38. The method of claim 31, further comprising determining a relative level of interest that is associated with each label for a given user.

39. The method of claim 38, wherein the relative level of interest is expressed in terms of a percentage.

40. The method of claim 38, wherein the relative level of interest is normalized.

41. The method of claim 31, wherein propagating the labels includes propagating the labels to a neighbor node based at least in part on the assigned weights and types of connections between respective nodes.

42. The method of claim 41, wherein propagating the labels includes determining a label weight to propagate for each label to an adjoining node based at least in part on an assigned weight of a respective label and a weight associated with a connection to the adjoining node.

43. The method of claim 31, wherein identifying a set of labels includes identifying one or more groups that a user is included in and includes identifying one or more labels associated with a respective group and assigning the one or more labels to each member of the respective group.

44. The method of claim 31, further comprising targeting content to respective users based at least in part on the labels and the label weights.

45. The method of claim 31, wherein the labels are keywords.

46. The method of claim 31, wherein the labels are uncommon keywords associated with a user.

47. The method of claim 31, wherein each node is connected to less than all of the other user nodes with which a user has a relationship.

48. The method of claim 31, wherein edges connect the user nodes and are weighted based on one or more of:

a user'"'"'s explicit designation of a relative weighting between relationships, two users'"'"' mutual explicit designation of affirmation of a relationship, or a measure of degree of relatedness of a node to other nodes in the graph.

49. The method of claim 31, wherein the user represents a group of users.

50. The method of claim 31, wherein the labels are advertisements.

51. The method of claim 31, wherein the graph includes one or more uncharacterized label nodes that are used to reduce the effect of far away nodes on a respective node.

52. The method of claim 31, wherein a connection between nodes represents a relationship.

53. The method of claim 52, wherein the relationship is a social relationship.

54. The method of claim 52, wherein the relationship is a friendship specified by a user represented by a respective node.

55. The method of claim 52, wherein the relationship is selected from a group consisting of a relationship based on group membership specified by a user represented by a respective node, a relationship based on similarity between content generated by the users represented by the respective node and the neighboring nodes, and a relationship based on similarity of a web-site visiting pattern.

56. The method of claim 31, wherein the user nodes representing the users of the social network comprise group nodes representing groups of users and individual user nodes representing individual users.

57. The method of claim 31, further comprising generating the graph that includes the nodes and reflects relationships between the nodes as edges connecting the nodes.

58. The method of claim 57, wherein the edges are weighted based on factors selected from a group consisting of whether the relationships are bi-directional, a number of links between the nodes, a frequency that a user visits another user'"'"'s profile, and payment terms specified by advertisers.

59. The method of claim 31, further comprising outputting, for each respective node, weights for the labels based on weights of labels associated with neighboring nodes, which are related to the respective node by a relationship.

60. A computer implemented method comprising:

identifying one or more labels of interest to be associated with users of a user group;

classifying each user including determining an association between the one or more labels and each respective user where the labels include areas of interest as either explicitly or implicitly determined for a given user;

providing a first graph of the user group that includes a plurality of nodes, one node for each user and one or more label nodes that inject weights for the labels into each respective user node, including providing edges between user nodes that represent relationships between the users and assigning labels to each user based on the classifying;

determining weights for each label for each user node in the first graph including;

normalizing the weights for labels injected into a node, and adjusting the normalized weights by an influence factor for injections to produce an injected label weight contribution for the label for the node;

adjusting the weights for labels received from a neighboring node by an influence factor for neighbor nodes to produce a neighbor label weight contribution for the label for the node; and

using the weights and influence factors as a set of linear constraints to determine final label weightings for the user nodes.