measuring behavioral trust in social networks sibel adali, et al. ieee international conference on...
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Measuring Behavioral Trust in Social NetworksSibel Adali, et al.
IEEE International Conference on Intelligence and
Security Informatics
Presented by: Liang ZhaoNorthern Virginia Center
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OutlineIntroductionBehavior TrustTwitter dataExperiment ResultsConclusion
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IntroductionTrust vs. Social NetworkEvaluate Trust in Social NetworkAssumptionsPurpose of this paper
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Trust vs. Social Network
Trust → Social Network (SN)◦Forms coalitions◦Identifies influential nodes in SN◦Depicts the flow of information
Social Network → Trust◦Communities induce greater
trust◦Information flow enhances trust
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Evaluate Trust in Social Network
Our own predisposition to trust.Relationship with others.Our opinions towards others.
Whether we trust others?
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AssumptionsDoes not consider semantic
information.Only consider social tiesTrust is a social tie between
a trustor and trustee.Social ties can be observed
by communication behaviors.
Degree of Trust can change.
Behavior Trust: Measure of trust is based on social behavior.
Social behaviors can conversely enhanceor reduce the trust.
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Purpose of this paperMeasure trust based on the
communi-cation behavior of the actors in SN.
Input:◦Communication Stream of Social
Network: {<sender, receiver, time>,…,<sender,
receiver, time>}Output:
◦Behavior trust graph Nodes: actors in SN, e.g., . Edges’ weights: strength of trust, e.g., .
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Behavior TrustConversations & PropagationsConversations behavior based
◦Conversations grouping◦Conversation Trust Computation
Propagation behavior based◦Propagation Trust◦Potential Propagations Counting◦Propagation Trust Computation
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Conversations & PropagationsThis paper considers two kinds of
behavior:◦ Conversations: Two nodes converse means
they are more likely to trust each other.
◦ Propagations: A propagates info from B indicates A trust B.
undirected directed
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Conversations groupingThe set of messages exchanged
between A and B is: .
Average time between messages is:
Rule: two consecutive messages ,
are in the same conversation if .
𝑡1 𝑡 2 𝑡 3 𝑡 4 𝑡5 𝑡 6 𝑡7
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Conversation Trust Computation
Rules:◦ Longer Conversations imply more trust.◦ More Conversations imply more trust.◦ Balanced participation between two
actors imply more trust.Trust (namely Edge’s weight in trust
graph):
Entropy function:
: the fraction sent by one actor; the fraction sent by the other actor.
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Propagation Trust
Given communication statistics alone, we cannot definitely determine which messages from B are propagations from A.
So we turn to counting “potential propagations”.
𝐴
details
?
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Potential Propagations CountingPotential Propagations must
satisfy the following constraint:
Matching “incoming to B” messages with “outgoing from B” messages:
𝑠1−𝑡1<𝜏𝑚𝑖𝑛𝜏𝑚𝑖𝑛<𝑠2−𝑡 1<𝜏𝑚𝑎𝑥𝑠3−𝑡 2>𝜏𝑚𝑎𝑥𝜏𝑚𝑖𝑛<𝑠3− 𝑡3<𝜏𝑚𝑎𝑥
No cross
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Propagation Trust ComputationNotations:
◦ the number of propagations by B.◦ the number of potential
propagations.◦the number of messages A sent to B.
Strategy 1: Strategy 2:
The fraction of B’s energy spent on propagating A’ messages.
The fraction of A’s messages worthy to be propagated by B.
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Twitter DataData Volume:
◦2M users (1.9M senders).◦230K tweets per day.
Data format:◦(sender, receiver, time).
Ground Truth Label of Trust: retweeting◦Directed
◦Broadcast
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ExperimentCompute Conversation &
Propagation Graphs.Overlaps between Conversation &
Propagation Graphs.Validate Conversation &
Propagation Graphs using retweets.
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Computing Conversation & Propagation Graphs
Data:◦15M Directed tweets for conversation
graph.◦34M broadcast tweets for propagation
graph.
Settings:
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Computing Conversation & Propagation Graphs (continued)
To achieve comparison between conversation and propagation graphs: treat the undirected edge as two directed ones.
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Overlaps between Conversation & Propagation GraphsCluster these two graphs based
on the weighted edges to discover communities:
Overlaps evaluation:
Random set of clusters with same size distribution; repeat 1000 times.
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Graph validation using retweets.Assumption:
◦A retweet is a propagation.◦When a user propagates information
from some other user, there must be some element of trust between them.
◦ indicates directed trust: .◦Directed retweet is more determinative
than broadcast retweet in indicating trust.
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Graph validation using retweets (contd.)Conversational Trust Graph
Validation:◦Nodes: 20% are also presented in
retweets graph.◦Edges: as follows.
: Random graph, which consists of randomly selected nodes. The edges are communications between the nodes.
: Prominence graph, which consists of most active nodes. The edges are communications between the nodes.
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Graph validation using retweets (contd.)Propagation Trust Graph
Validation:◦Nodes: 20% are also presented in
retweets graph.◦Edges: as follows.
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ConclusionMethod advantages:
◦ Propose a measurable behavior trust metric.
◦ Does not need semantic information.◦ Can be applied to dynamic network.◦ The proposed metric reasonably
correlate with retweets.◦ Can be applied to general social
networks other than Twitter.◦ Good scalability due to low
computational cost on statistical communication data.
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Future WorksVerify the potentially casual
relationship between conversation and propagation behavior.
The intersection of conversation and propagation graphs would be a more stringent measure of trust.
Improve the purity of trust measurement by considering semantics of messages.
Trust should be dependent on context (e.g., we trust a doctor in medical science, but not necessarily in finance analysis.
Improve the trust measurement by considering the quality and value of messages.
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Thank you