Measuring Behavioral Trust in Social Networks Sibel Adali, et al. IEEE International Conference on Intelligence and Security Informatics Presented by:

Measuring Behavioral Trust in Social NetworksSibel Adali, et al.

IEEE International Conference on Intelligence and

Security Informatics

Presented by: Liang ZhaoNorthern Virginia Center

OutlineIntroductionBehavior TrustTwitter dataExperiment ResultsConclusion

IntroductionTrust vs. Social NetworkEvaluate Trust in Social NetworkAssumptionsPurpose of this paper

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

Evaluate Trust in Social Network

Our own predisposition to trust.Relationship with others.Our opinions towards others.

Whether we trust others?

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.

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., .

Behavior TrustConversations & PropagationsConversations behavior based

◦Conversations grouping◦Conversation Trust Computation

Propagation behavior based◦Propagation Trust◦Potential Propagations Counting◦Propagation Trust Computation

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

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

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.

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

?

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

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.

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

ExperimentCompute Conversation &

Propagation Graphs.Overlaps between Conversation &

Propagation Graphs.Validate Conversation &

Propagation Graphs using retweets.

Computing Conversation & Propagation Graphs

Data:◦15M Directed tweets for conversation

graph.◦34M broadcast tweets for propagation

graph.

Settings:

Computing Conversation & Propagation Graphs (continued)

To achieve comparison between conversation and propagation graphs: treat the undirected edge as two directed ones.

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.

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.

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.

Graph validation using retweets (contd.)Propagation Trust Graph

Validation:◦Nodes: 20% are also presented in

retweets graph.◦Edges: as follows.

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.

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.

Thank you