1 networking 2012 parallel and distributed systems group, delft university of technology, the...
DESCRIPTION
3 NETWORKING 2012 Reputation Systems: Basic Concepts the goal of reputation in large scale systems: establish trust among users incentives for good behavior Interaction-Based Reputation Systems why not use the complete history? resource requirements: computation + storage capacity dynamic behavior: population turnover + changing behavior reputation algorithm complete history of interactions reputations of nodesTRANSCRIPT
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Parallel and Distributed Systems Group,Delft University of Technology, the Netherlands
May 22, 2012
Reducing the History in Decentralized
Interaction-Based Reputation SystemsDimitra Gkorou, Tamás Vinkó, Nitin Chiluka, Johan Pouwelse, and Dick Epema
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Overview
• Interaction-based Reputation Systems• Limitations of the Complete History • Reducing the History• Evaluation• Conclusion
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Reputation Systems: Basic Concepts
• the goal of reputation in large scale systems:• establish trust among users• incentives for good behavior
• Interaction-Based Reputation Systems
• why not use the complete history?• resource requirements: computation + storage capacity• dynamic behavior: population turnover + changing
behavior
reputation algorithm
complete history of
interactionsreputations
of nodes
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• Complete History (CH): modeled as a growing directed weighted graph
• Reduced History (RH): a dynamically maintained subset of CH with fixed size
• removal of the least important nodes and edges in
Reducing the History: Basic Approach
Complete History (CH)
Reduced History (RH)
node removal: freshnessactivity levelreputation position
edge removal: freshnessweightposition
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• parameters indicating the importance:• freshness (node/edge): capturing the dynamics of the
system• position (node/edge): keeping the graph connected• activity level (node): maintaining informative nodes• reputation (node): maintaining trustworthy information• weight (edge): importance of an edge
• combined to a priority score for each node and edge
Reducing the History: Priority Score
Complete History (CH)
Reduced History (RH)
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Reduced History: Construction
Complete History: • add new node + its edges• add new edge connecting existing nodesReduced History (fixed size): • add the new node + remove the node with the
lowest priority score• add the new edge + remove the edge with the
lowest priority scorewad
wab
wbc wd
Complete History
wad
wab
wbcwd
Reduced History
wedwed
wce wg
b
ad
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a
dc c
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f fg
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wge wgewfe wfe
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Experiment Setup: Synthetic Graphs• CH growing up to 5000 nodes• random graphs:
• new nodes/edges connected to existent nodes with a constant probability
• scale-free graphs:• new nodes/edges connected to existent nodes with a
probability proportional to their degree• multiple edges correspond to weights
random graph scale-free graph
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Experiment Setup: Real-world Graphs• Bartercast Reputation mechanism:
• Tribler: the BitTorrent P2P file-sharing system• provides incentives for contribution• peers locally store the history of their own interactions +
interactions among other peers• information exchange: using an epidemic protocol
• Bartercast graph:• crawled the Bartercast reputation mechanism (4 months)• union of all local graphs• vertices: the peers of Tribler• weighted edges: the amount of the transferred data
between two peers
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Experiment Setup: real-world graphs• Author-to- author citation graph:
• derived from papers published in Physical Review E• vertices: the authors of papers• weighted edges: number of citations between authors
• small-world graphs• Citation graph more densely connected than Bartercast
graph # nodes
# edges aver. path length
c.c.
Bartercast
10,634 31,624 2.64 0.00074
Citation 15,360 365,319 3.29 0.11
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Computation of Reputation
• Max-flow based computation:• reputation computation of Bartercast• the weights of edges graph as flows• starting from the most central node j• reputation of peer a: the difference of flows faj and fja
• Eigenvector centrality:• well-studied metric• interactions with highly reputed nodes contribute
more• Pagerank
e
j
c
b
Wca\ac
wbi
f
wgk
kwbj
g
wjg
wfg
wge
Wba\abwga
awia
i
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Evaluation Metrics
• the ranking of reputations is more important than their actual values
• the identification of the highest ranked nodes is more important
• consider the sequences of ranked nodes in CH and RH according to their reputation
• two metrics• ranking error: the minimum number of swaps needed to get
the same ranking sequence in RH and CH• ranking overlap: the fraction of common nodes in the
sequences of top-raked nodes in RH and CH
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Evaluation: ranking errorMax-Flow Pagerank
rank
ing
erro
r
Size of RH relatively to the size of CH Growth of CH relatively to the size of RH
Max-Flow Pagerank
• scale-free and real-world graphs exhibit smaller ranking error
• pagerank exhibits smaller ranking error
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Evaluation: ranking overlap
Max-FlowRa
nkin
g ov
erla
pRa
nkin
g ov
erla
p
Size of RH relatively to the size of CH
Pagerank
• max-flow achieves much higher ranking overlap
• random graphs exhibit the worst ranking overlap
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Evaluation: ranking overlap
Max-FlowRa
nkin
g ov
erla
pRa
nkin
g ov
erla
p
Pagerank
Growth of CH relatively to the size of RH
• max-flow achieves much higher again ranking overlap
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Conclusions
• the performance of RH depends on the topology• scale-free and real-world graphs exhibit smaller ranking
error and higher ranking overlap
• the performance of RH depends on the reputation algorithm• pagerank achieves lower ranking error• max-flow achieves higher ranking overlap
• RH achieves good accuracy for real-world graphs
