Network-aware product rollout in online social networks
First Claim
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1. A method comprising, by one or more processors associated with one or more computing devices:
- accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively;
identifying a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes;
providing a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and
determining, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure.
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Abstract
In one embodiment, a method includes accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each node corresponding to a user of an online social network, identifying a plurality of clusters in the social graph using graph clustering, providing a treatment to a first set of users based on the clusters, and determining a treatment effect treatment for the users in the first set based on a network exposure to the treatment for each user.
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Citations
19 Claims
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1. A method comprising, by one or more processors associated with one or more computing devices:
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accessing a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identifying a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes; providing a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and determining, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
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access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identify a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes; provide a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and determine, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure.
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19. A system comprising:
- one or more processors; and
a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identify a plurality of non-overlapping clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes; provide a treatment to at least a first set of users and a second set of users, the first and second sets of users corresponding to a first set of clusters and a second set of clusters of the plurality of clusters, respectively, the first set of clusters being discrete from the second set of clusters; and determine, for each of at least the first and second sets of users, a treatment effect of the treatment on the users of the set of users based on a network exposure to the treatment for each user, wherein, for each respective cluster, the network exposure of the nodes in the cluster is absolute k-neighborhood exposure, absolute k-core exposure, fractional q-neighborhood exposure, or fractional q-core exposure.
- one or more processors; and
Specification