USER CATEGORIZATION IN COMMUNICATIONS NETWORKS

• US 20150236910A1
• Filed: 02/18/2014
• Published: 08/20/2015
• Est. Priority Date: 02/18/2014
• Status: Abandoned Application

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: SHIVASHANKAR, Subramanian, Vijayan, Priyesh
• Application Number:
  US14/182,337
• Publication Number:
  US 20150236910A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04W 24/10 Scheduling measurement reports ; Arrangements for measurement reports

Abstract

There is provided categorization of users in a communications network. Information is acquired from a plurality of information sources in a communications network. The information is associated with a plurality of users of the communications network and represented by a multi-relation network representation. The multi-relation network representation comprises a plurality of node types and relationship types. At least one categorization criterion is acquired. A categorization routine is repeatedly to performed to determine a relation between the users. The users are categorized according to the determined relation.

Claims

1. A method for categorizing users in a communications network, the method being performed by a network node, comprising the steps of:

acquiring information from a plurality of information sources in a communications network, said information being associated with a plurality of users of said communications network and represented by a multi-relation network representation, said multi-relation network representation comprising a plurality of node types and relationship types;

acquiring at least one categorization criterion;

repeatedly performing a categorization routine comprising;

transforming said multi-relation network representation into a single-relation network representation representing an aggregated expected network comprising a single node type and a single relationship type by combining multiple node types and relationship types in said multi-relation network representation;

determining a relation between said users based on said at least one categorization criterion and by associating said acquired information with said aggregated expected network; and

updating said multi-relation network representation based on said determined relation;

and

categorizing said users according to said determined relation.

2. The method according to claim 1, wherein said categorization routine further comprises:

determining a first correlation between said plurality of relationship types based on said plurality of information sources; and

updating said multi-relation network representation also based on said first correlation.

3. The method according to claim 2, wherein determining said correlation further comprises:

learning a joint model of said plurality of information sources explicitly capturing said correlation between said plurality of relationship types.

4. The method according to claim 1, wherein at least two categorization criteria are acquired, and wherein said categorization routine further comprises:

determining a second correlation between said at least two categorization criteria; and

updating said multi-relation network representation also based on said second correlation.

5. The method according to claim 1, wherein transforming said multi-relation network representation further comprises:

combining said plurality of node types into said single node type; and

transforming multiple vectorized representations of links between each pair of linked nodes in said multi-relation network representation to a single vectorized representation between each pair of nodes in said single-relation network representation.

6. The method according to claim 1, wherein determining said relation further comprises:

repeatedly performing multi-score learning between said acquired information by learning a first hypothesis function, H_a, on attributes of said acquired information and links between nodes in said aggregated expected network by learning a second hypothesis function, H_l*, on link types of said aggregated expected network.

7. The method according to claim 1, wherein each one of said information sources is an independent and identically distributed information source.

8. The method according to claim 1, wherein said categorization routine is repeated until said relation changes less than a predetermined threshold value between two consecutive iterations thereof.

9. The method according to claim 1, wherein said categorization routine is repeated for a predetermined amount of times.

10. The method according to claim 1, wherein at least parts of said information is tagged, said tagged information relating to at least one of:

call and/or messaging patterns of said users;

call and/or messaging graphs of said users;

web browsing information of said users;

network graphs, user activities, and/or group affiliation of said users in an online social networking service and/or an online microblogging service;

demographics of said users in said communications network; and

infrastructure information of a network coverage area of said communications network.

11. The method according to claim 10, wherein said tagged information is represented by said plurality of node types and/or said plurality of relationship types in said multi-relation network representation.

12. The method according to claim 1, further comprising:

dividing said users into at least two groups based on said categorized users.

13. The method according to claim 12, further comprising:

providing at least one of said at least two groups to a recommendations engine for said users.

14. The method according to claim 13, wherein said recommendations engine relates to services, such as subscriptions, offered in said communications network, or network resource configurations, such as resource allocation, associated with said users.

15. The method according to claim 1, further comprising:

predicting categorization of further users of said communications network based on said on said categorized users.

16. The method according to claim 1, wherein said multi-relation network representation is modelled according to a first expression defined as:

argmax Σ

^T<sub>iε

1</sub>Σ

^V<sub>kε

1</sub>θ

_iv(V_k)+Σ

^L<sub>jε

1</sub>θ

_il(L_j)+Σ

^V<sub>kε

1</sub>Σ

^L<sub>jε

1</sub>Ψ

_i(V_k,L_j)

with an objective to find 0 such that said first expression is maximized, where i=1, . . . , T denotes task i, Vk denotes attributes for user k, Lj denotes relationship types in domain j, θ

is estimated based on said information sources, and Ψ

denotes task based correlations.

17. The method according to claim 1, wherein said single-relation network representation is modelled according to a second expression defined as:

argmax Σ

^T<sub>iε

1</sub>Σ

^V<sub>kε

1</sub>θ

_iv(V_k)+θ

_i*(L*)+Σ

^V<sub>kε

1</sub>Ψ

_i(V_k,L*),

with an objective to find θ

such that said second expression is maximized, where i=1, . . . , T denotes task i, Vk denotes attributes for user k, L* denotes relationship type, θ

is the parameter estimated on the sources, and Ψ

denotes task based correlations.

18. The method according to claim 1, wherein said single-relation network representation for each mode type m is modelled according to a third expression defined as:

argmax Σ

^M<sub>mε

1</sub>Σ

^T<sub>iε

1</sub>Σ

^V<sub>kε

1</sub>θ

_imv(V_km)+θ

_i*(L_m*)+Σ

^V<sub>kε

1</sub>Ψ

_im(V_km,L_m*)

with an objective to find θ

such that said third expression is maximized, where i=1, . . . , T denotes task i, Vk denotes attributes for user k, L* denotes relationship type, θ

is the parameter estimated on the sources, and Ψ

denotes task based correlations.

19. A network node for categorizing users in a communications network, the network node comprising a processing unit and a non-transitory computer readable storage medium, said non-transitory computer readable storage medium comprising instructions executable by said processing unit whereby said network node is operative to:

acquire information from a plurality of information sources in a communications network, said information being associated with a plurality of users of said communications network and represented by a multi-relation network representation, said multi-relation network representation comprising a plurality of node types and relationship types;

acquire at least one categorization criterion;

repeatedly perform a categorization routine comprising;

transforming said multi-relation network representation into a single-relation network representation representing an aggregated expected network comprising a single node type and a single relationship type by combining multiple node types and relationship types in said multi-relation network representation;

determining a relation between said users based on said at least one categorization criterion and by associating said acquired information with said aggregated expected network; and

updating said multi-relation network representation based on said determined relation;

and

categorize said users according to said determined relation.

20. A computer program product for categorizing users in a communications network, the computer program product being stored on a non-transitory computer readable storage medium and comprising computer program instructions that, when executed by a processing unit, causes the processing unit to:

acquire information from a plurality of information sources in a communications network, said information being associated with a plurality of users of said communications network and represented by a multi-relation network representation, said multi-relation network representation comprising a plurality of node types and relationship types;

acquire at least one categorization criterion;

repeatedly perform a categorization routine comprising;

transforming said multi-relation network representation into a single-relation network representation representing an aggregated expected network comprising a single node type and a single relationship type by combining multiple node types and relationship types in said multi-relation network representation;

determining a relation between said users based on said at least one categorization criterion and by associating said acquired information with said aggregated expected network; and

updating said multi-relation network representation based on said determined relation; and

categorize said users according to said determined relation.