DETERMINING MEASURES OF INFLUENCE OF USERS OF A SOCIAL NETWORK
A method and system for evaluating the reputation of a member of a social networking system is disclosed. Consistent with an embodiment of the invention, one or more attributes associated with a social networking profile of a member of a social network are analyzed. Based on the analysis, a ranking, rating or score is assigned to a particular category of reputation. When requested, the ranking, rating or score is displayed to a user of the social network.
1 . A computer-implemented method for determining a measure of influence of a particular member of a social network, the method comprising:
determining a collective influence of a plurality of endorsers of the particular member of the social network, wherein determining the collective influence of the plurality of endorsers of the particular member of the social network includes excluding measures of influence of endorsers having fewer than a minimum number of connections;
calculating the measure of influence of the particular member of the social network based on the collective influence of the plurality of endorsers.
2 . The method of claim 1 , further comprising calculating a collective influence of a plurality of connections of the particular member, wherein calculating the measure of influence of the particular member of the social network is further based on the collective influence of the plurality of connections of the particular member.
3 . The method of claim 1 , wherein determining the collective influence of the plurality of endorsers of the particular member of the social network is based, at least in part, on a popularity of each of the plurality of endorsers of the particular member of the social network.
4 . The method of claim 3 , further comprising determining the popularity of each of the plurality of endorsers within the social network based on at least one of:
a first frequency at which other members of the social network invite each endorser of the plurality of endorsers to connect, or
a second frequency at which the other members of the social network accept invitations by each endorser of the plurality of endorsers to connect.
5 . The method of claim 1 , wherein determining the collective influence of the plurality of endorsers of the particular member of the social network includes giving a higher weight to endorsers, of the plurality of endorsers, that have a higher influence than other endorsers of the plurality of endorsers.
6 . The method of claim 1 , further comprising:
generating a ranking of the measure of influence of the particular member of the social network in comparison to measures of influence of additional members of the social network.
7 . One or more non-transitory computer-readable media storing instructions for determining a measure of influence of a particular member of a social network, wherein the instructions, when executed by the one or more processors, cause:
determining a collective influence of a plurality of endorsers of the particular member of the social network, wherein determining the collective influence of the plurality of endorsers of the particular member of the social network includes excluding measures of influence of endorsers having fewer than a minimum number of connections;
calculating the measure of influence of the particular member of the social network based on the collective influence of the plurality of endorsers.
8 . The one or more computer-readable media of claim 7 , further comprising calculating a collective influence of a plurality of connections of the particular member, wherein calculating the measure of influence of the particular member of the social network is further based on the collective influence of the plurality of connections of the particular member.
9 . The one or more computer-readable media of claim 7 , wherein determining the collective influence of the plurality of endorsers of the particular member of the social network is based, at least in part, on a popularity of each of the plurality of endorsers of the particular member of the social network.
10 . The one or more computer-readable media of claim 9 , further comprising determining the popularity of each of the plurality of endorsers within the social network based on at least one of:
a first frequency at which other members of the social network invite each endorser of the plurality of endorsers to connect, or
a second frequency at which the other members of the social network accept invitations by each endorser of the plurality of endorsers to connect.
11 . The one or more computer-readable media of claim 7 , wherein determining the collective influence of the plurality of endorsers of the particular member of the social network includes giving a higher weight to endorsers, of the plurality of endorsers, that have a higher influence than other endorsers of the plurality of endorsers.
12 . The one or more computer-readable media of claim 7 , further comprising:
generating a ranking of the measure of influence of the particular member of the social network in comparison to measures of influence of additional members of the social network.
13 . A method for determining a measure of influence of a particular user of an online service, the method comprising:
determining a collective influence of a plurality of raters of the particular user of the online service,
wherein determining the collective influence of the plurality of raters of the particular user of the online service includes excluding or reducing, from the collective influence, measures of influence of raters that satisfy one or more criteria;
calculating the measure of influence of the particular user of the online service based on the collective influence of the plurality of raters;
wherein the method is performed by one or more computing devices.
14 . The method of claim 13 , wherein the raters that satisfy the one or more criteria include raters that rate each other to a certain degree or raters that the particular user has rated.
15 . The method of claim 13 , wherein determining the collective influence of the plurality of raters of the particular user of the online service includes giving a higher weight to raters, of the plurality of raters, that have a higher influence than other raters of the plurality of raters.
16 . The method of claim 13 , further comprising:
generating a ranking of the measure of influence of the particular user of the online service in comparison to measures of influence of additional users of the online service.
17 . The method of claim 13 , wherein calculating the measure of influence of the particular user of the online service is with respect to a particular skill, of multiple skills, of the particular user of the online service.
18 . The method of claim 13 , further comprising:
receiving, from a set of raters, a plurality of ratings of users of the online service;
storing rating data that indicates, for each user of said users, one or more ratings of the plurality of ratings;
wherein the rating data for the particular user of the online service indicates the plurality of raters.
19 . The method of claim 13 , wherein determining the collective influence of the plurality of raters of the particular user of the online service is based, at least in part, on a popularity of each of the plurality of raters.
20 . The method of claim 19 , further comprising determining a popularity of each of the plurality of raters based on at least one of:
a first frequency at which other users of the online service invite each rater of the plurality of raters to connect, or
a second frequency at which the other users of the online service accept invitations by each rater of the plurality of raters to connect.
21 . The method of claim 13 , further comprising:
calculating a collective influence of a plurality of connections of the particular user, wherein calculating the measure of influence of the particular user of the online service is further based on the collective influence of the plurality of connections of the particular user.
22 . One or more non-transitory computer-readable media storing instructions for determining a measure of influence of a particular user of an online service, wherein the instructions, when executed by one or more processors, cause:
determining a collective influence of a plurality of raters of the particular user of the online service,
wherein determining the collective influence of the plurality of raters of the particular user of the online service includes excluding or reducing, from the collective influence, measures of influence of raters that satisfy one or more criteria;
calculating the measure of influence of the particular user of the online service based on the collective influence of the plurality of raters;
wherein the method is performed by one or more computing devices.
23 . The one or more computer-readable media of claim 22 , wherein the raters that satisfy the one or more criteria include raters that rate each other to a certain degree or raters that the particular user has rated.
24 . The one or more computer-readable media of claim 22 , wherein determining the collective influence of the plurality of raters of the particular user of the online service includes giving a higher weight to raters, of the plurality of raters, that have a higher influence than other raters of the plurality of raters.
25 . The one or more computer-readable media of claim 22 , wherein the instructions, when executed by the one or more processors, further cause:
generating a ranking of the measure of influence of the particular user of the online service in comparison to measures of influence of additional users of the online service.
26 . The one or more computer-readable media of claim 22 , wherein calculating the measure of influence of the particular user of the online service is with respect to a particular skill, of multiple skills, of the particular user of the online service.
27 . The one or more computer-readable media of claim 22 , wherein the instructions, when executed by the one or more processors, further cause:
receiving, from a set of raters, a plurality of ratings of users of the online service;
storing rating data that indicates, for each user of said users, one or more ratings of the plurality of ratings;
wherein the rating data for the particular user of the online service indicates the plurality of raters.
28 . The one or more computer-readable media of claim 22 , wherein determining the collective influence of the plurality of raters of the particular user of the online service is based, at least in part, on a popularity of each of the plurality of raters.
29 . The one or more computer-readable media of claim 28 , wherein the instructions, when executed by the one or more processors, further cause determining a popularity of each of the plurality of raters based on at least one of:
a first frequency at which other users of the online service invite each rater of the plurality of raters to connect, or
a second frequency at which the other users of the online service accept invitations by each rater of the plurality of raters to connect.
30 . The one or more computer-readable media of claim 22 , wherein the instructions, when executed by the one or more processors, further cause:
calculating a collective influence of a plurality of connections of the particular user, wherein calculating the measure of influence of the particular user of the online service is further based on the collective influence of the plurality of connections of the particular user.