Ranking of address book contacts based on social proximity

Abstract

In one embodiment communications made using one or more applications are monitored. The communication frequency for the communications with respect to each communication channel and each contact is logged. A social-proximity score for each of the contacts is determined for each contact. The social-proximity score is determined based on social-graph information and the social-graph information comprises a degree-of-separation coefficient for each contact with respect to the user. Each social-proximity score for the one or more contacts is updated based on a frequency coefficient derived from the communication frequency log.

What is claimed is: 
     
         1 . A method comprising, by one or more computing devices:
 monitoring communications made using one or more applications over a plurality of communication channels between a user and one or more contacts on a mobile communication device;   logging a communication frequency for each of the communication channels and each of the contacts, wherein the logging comprises monitoring communication activity of one or more applications on the mobile communication device;   determining a social-proximity score for each of the contacts, wherein each social-proximity score is determined based on social-graph information, wherein the social-graph information comprises a degree-of-separation coefficient for each contact with respect to the user; and   updating each social-proximity score for the one or more contacts based on a frequency coefficient derived from the communication frequency log.   
     
     
         2 . The method of  claim 1 , wherein the monitoring comprises further updating the social-proximity scores in response to determining a new communication between the user and one of the contacts. 
     
     
         3 . The method of  claim 1 , wherein the communication frequency information is truncated at a predetermined number of days. 
     
     
         4 . The method of  claim 1 , wherein the social-proximity score for each contact is further determined based on one or more user-declared relationship types between the user and other contacts of the contacts list. 
     
     
         5 . The method of  claim 1 , further comprising determining a ranked list of the contacts based on the social-proximity scores. 
     
     
         6 . The method of  claim 1 , wherein the frequency coefficient is weighted based on a weighting assigned to each communication channel. 
     
     
         7 . The method of  claim 1 , wherein the social-graph information further comprises an affiliation coefficient for each contact with respect to the user. 
     
     
         8 . The method of  claim 7 , wherein the social-proximity score for each contact is based on a summation of the degree-of-separation coefficient, the affiliation coefficient, and a weighted average of the communication frequency across the communication channels. 
     
     
         9 . The method of  claim 6 , wherein the weighting assigned to a telephone call is greater than the weighting assigned to a text message. 
     
     
         10 . The method of  claim 1 , wherein the monitoring and logging is performed on a client application on the mobile communication device. 
     
     
         11 . A system comprising:
 one or more processors; and   a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to:
 monitor communications made using one or more applications over a plurality of communication channels between a user and one or more contacts on a mobile communication device; 
 log a communication frequency for each of the communication channels and each of the contacts, wherein the logging comprises monitoring communication activity of one or more applications on the mobile communication device; 
 determine a social-proximity score for each of the contacts, wherein each social-proximity score is determined based on social-graph information, wherein the social-graph information comprises a degree-of-separation coefficient for each contact with respect to the user; and 
 update each social-proximity score for the one or more contacts based on a frequency coefficient derived from the communication frequency log. 
   
     
     
         12 . The system of  claim 11 , wherein the monitoring comprises further updating the social-proximity scores in response to determining a new communication between the user and one of the contacts. 
     
     
         13 . The system of  claim 11 , wherein the communication frequency information is truncated at a predetermined number of days. 
     
     
         14 . The system of  claim 11 , wherein the social-proximity score for each contact is further determined based on one or more user-declared relationship types between the user and other contacts of the contacts list. 
     
     
         15 . The system of  claim 11 , further comprising determining a ranked list of the contacts based on the social-proximity scores. 
     
     
         16 . The system of  claim 11 , wherein the frequency coefficient is weighted based on a weighting assigned to each communication channel. 
     
     
         17 . The system of  claim 11 , wherein the social-graph information further comprises an affiliation coefficient for each contact with respect to the user. 
     
     
         18 . The system of  claim 17 , wherein the social-proximity score for each contact is based on a summation of the degree-of-separation coefficient, the affiliation coefficient, and a weighted average of the communication frequency across the communication channels. 
     
     
         19 . The system of  claim 16 , wherein the weighting assigned to a telephone call is greater than the weighting assigned to a text message. 
     
     
         20 . One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
 monitor communications made using one or more applications over a plurality of communication channels between a user and one or more contacts on a mobile communication device;   log a communication frequency for each of the communication channels and each of the contacts, wherein the logging comprises monitoring communication activity of one or more applications on the mobile communication device;   determine a social-proximity score for each of the contacts, wherein each social-proximity score is determined based on social-graph information, wherein the social-graph information comprises a degree-of-separation coefficient for each contact with respect to the user; and   update each social-proximity score for the one or more contacts based on a frequency coefficient derived from the communication frequency log.