Trust and Reputation in Social Networks - PowerPoint PPT Presentation

Loading...

PPT – Trust and Reputation in Social Networks PowerPoint presentation | free to download - id: 73608e-ZGMxZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Trust and Reputation in Social Networks

Description:

Trust and Reputation in Social Networks Laura Zavala 03/2010 Trust A statement or prediction of reliance Examples I believe that my doctor is a good surgeon how much ... – PowerPoint PPT presentation

Number of Views:40
Avg rating:3.0/5.0
Slides: 20
Provided by: Laur4232
Learn more at: http://ebiquity.umbc.edu
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Trust and Reputation in Social Networks


1
Trust and Reputation in Social Networks
  • Laura Zavala
  • 03/2010

2
Trust
  • A statement or prediction of reliance
  • Examples
  • I believe that my doctor is a good surgeon
  • how much credence should I give to what this
    person says about agiven topic?
  • based on what my friends say, how much should I
    trust this newperson
  • CS Department at UMBC has a good reputation

3
Computing with Trust
  • The Security Approach
  • Authentication, Access Control, Digital
    Signatures, Public Keys, etc
  • Insitutional Approach / Central Authority
  • Trust Networks (Social Approach) Direct
    experiences and reputation
  • Collaborative Filtering (similar agents have
    similar beliefs)
  • Graph theory (trust propagation and inference)
  • Referral Networks (find chains of experts on a
    given topic)

4
Computing with Trust Examples
5
Social Trust
6
Trust Models Issues
  • Trust discovery
  • Trust value
  • Trust propagation
  • Trust aggregation
  • Trust update / learning --- regret, forgiveness,

7
Social Networks Graph Models
  • Small world networks
  • The small world concept suggests that any pair of
    entities in a seemingly vast, random network can
    actually connect relatively short paths of mutual
    acquaintances.
  • Properties of graph structures that define a
    small world network
  • clustering coefficient
  • average path length.

8
Graph Models The Beta Model
Watts and Strogatz (1998) Link Rewiring
b 0
b 0.125
b 1
People know others at random. Not clustered, but
small world
People know their neighbors, and a few distant
people. Clustered and small world
People know their neighbors. Clustered,
but not a small world
9
Graph Models The Beta Model
Watts and Strogatz (1998) Link Rewiring
  • First five random links reduce the average path
    length of the network by half, regardless of N!
  • b model reproduces short-path results of random
    graphs, but also allow for clustering.
  • Small-world phenomena occur at threshold between
    order and chaos.

10
Inferring Trust 2
  • The Goal Select two individuals - the source
    (node A) and sink (node C) - and recommend to the
    source how much to trust the sink.

tAC
A
B
C
A
B
C
A
B
tAB
tBC
From 2
11
Inferring Trust 2
  • Binary values 0 (no trust), 1 (trust)

From 2
12
Inferring Trust 3
  • Three operators
  • Aggregation
  • Concatenation
  • Selection

13
Inferring Trust 3
  • ltb,d,ugt
  • Three operators
  • Aggregation
  • deals with the propagation of trust ratings along
    a path
  • Concatenation
  • chooses the most trust-worthy path to each
    witness
  • Selection
  • deals with the combination of trust ratings from
    paths between the same source and target

14
Inferring Trust 3
15
Inferring Trust 3
16
FilmTrust
  • http//trust.mindswap.org/FilmTrust
  • Combines online social network (w/trust) with
    movie ratings and reviews
  • Use trust inferences
  • To customize ratings
  • To sort reviews

17
FilmTrust
  • Combines trust, social networks, and movie
    ratings.
  • Preliminary results show that, in certain cases,
    the trust-based predictions outperform most other
    systems.

18
Other approaches
  • Game theoretic
  • Emergence Interpretation of Trust
  • Our emergence interpretation enables agents to
    both discover and evolve trust knowledge for
    trust based operations.
  • Tim Finin, Anupam Joshi

19
References
  1. Guillaume Muller, Laurent Vercouter. 2008.
    Computational Trust and Reputation Models,
    AAMAS08 Tutorial
  2. Jennifer Golbeck, James Hendler. 2006. FilmTrust
    Movie recommendations using trust in web-based
    social networks. Proceedings of the IEEE Consumer
    Communications and Networking Conference ,
    January 2006.
  3. Hang, C., Wang, Y., and Singh, M. P. 2009.
    Operators for propagating trust and their
    evaluation in social networks. In Proceedings of
    AAMAS09
  4. Li Ding , Pranam Kolari , Shashidhara
    Ganjugunte , Tim Finin , Anupam Joshi. 2004.
    Modeling and evaluating trust network inference.
    In Proceedings of the 7th International Workshop
    on Trust in Agent Societies
About PowerShow.com