Transcript Intelligent Control

Network Intelligence
and Networked Intelligence
网络智能和网络化智能
Deyi Li ( 李 德 毅 )
[email protected]
Aug. 1, 2006
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Challenge to AI for Knowledge
Representation
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Study on Knowledge Representation
one-dimensional representation:
predicate calculus, natural language
understanding, etc.
 two-dimensional representation:
pattern recognition, neural network
learning, etc.
 attention on evolutional networks with
uncertainty was less paid unfortunately.

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Networks are present everywhere.
All we need is an eye for them.
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We are witnessing a revolution in
the making as scientists from all
different disciplines discover that
complexity has a strict architecture.
We have come to grasp the important
knowledge of networks.
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Networks interact with one another and
are recursive .
 Getting such a diverse group to agree
on a common core of knowledge
representation about networks is a
significant challenge to both Cognitive
Science and Artificial Intelligence.

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Networks Evolution and Growth drive
the fundamental issue that forms our
view of network representation and
network intelligence.
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ER pure random
graph(1960)
Paul.Erdos
WS small world
model (1998)
Duncan Watts
BA scale-free
model(1999)
Albert Barabasi
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Alfred Renyi
Steven Strogatz
Reka Albert
“Small worlds” and “power law
distributions” are generic properties
of networks in general.
 There is a new knowledge
representation out there that is the
network representation.

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It’s the fact that all of these real
world networks can be explained and
understood using the same concepts,
and the same mathematics, that
makes network representation so
important in AI research in the
information age.
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Mining Typical Topologies
from Real Complex Networks
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typical topologies with randomness
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An evolutional and growth
network may be by and large
characterized by an ideal
typical model
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Mining Typical Topology from Real World
Networks at Multi-scale
Expectation of topologies at
different scales:

Small world network

Scale free network

Hub Network

Star Network
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Extend more properties of networks
the mass of a node
 physical distance between two nodes
 the age of a node
 betweenness of a link
 betweenness of a node

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With the extended properties of
networks, we may map relational
data into networked representation
and propose a new direction that is
networked data mining.
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A detailed networked data
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Mining typical topology with a
middle granularity
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Mining typical topology with a
large granularity
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Discover critical links and
important communities from
a real network
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Many networks are inhomogeneous,
consisting lot of an undifferentiated mass of
nodes, but of distinct groups.
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Mining communities

Classification: The typical problem in
networked data mining is that of dividing
all the nodes of a network into some
number of groups, while minimizing the
number of links that run between nodes
in different groups.
 Clustering: Given a network structure, try
to divide into communities in such a way
that every node belongs only to one of
the communities.
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Community model can capture the
hierarchical feature of a Network.
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A link removal method
based on link betweenness
Input：Initial network topology，the number of
community
Output：network communities
Step 1. Calculate the betweenness for all links
in the network.
Step 2. Remove the link with the highest
betweenness.
Step 3.Re-calculate betweennesses for all
links affected by the removal.
Step 4.Repeat from step 2 until generating
specified numbers of communities. 24
Mining Communities
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Mining clusters in a complex network using data field
method and finding virtual kernels

Given a traffic network, find virtual traffic centers
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Node mass may represent its degree
from data field point of view
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Node mass may also represent its
betweenness from data field point of view
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Emergence Computation
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A subtle urge to synchronize is
pervasive in nature indeed
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synchronized clapping
fireflies flashing
menstrual cycles of women
adaptive path minimization by ants
wasp and termite nest building
army ant raiding
fish schooling and bird flocking
pattern formation in animal coats
coordinated cooperation in slime molds
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Nature Vol. 403, 24 Feb.2000
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The emergence of synchronized
clapping is a delightful expression
of self-organization on a human
scale
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emergence mechanism

For everybody in the audience there
are 3 measurements:
1. time difference at the beginning of the
applause (TDB)
2. interval time of a clap to the next one
(IT, represented by △t)
3. the clapping strength (CS)
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
If there is no any interaction among
audience, the distributions of everybody’s
TDB, IT and CS, even the number of clap
times all follow a kind of poisson curve
like.
 If there are interactions among audience,
the influence to each other depends on
the distance (say rij) between them.
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Assume:
all the clap strengths are the same.
 “following the many” is fundamental
mechanism and pervasive applicable.
 Therefore the relationship of persons
in the audience, that is the structure
of the network, encoding how people
influence each other is set up in
formula 1

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N
t  ti   (c1 (t j  ti )  c2 (t j  ti ))  e
'
i
0
０
１
２
３
４
５
６
７

rij2

i  1,2,...,N
(1)
j 1
1
2
3
4
5
6
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somebody’s just-happened
clap moment is ti and the
next IT (say △ti’) is based
on his current IT (say △ti)
and influenced by the
distanced person who’s
just-happened clap is
measured by △tj and
clapped moment tj
 σ represents distance
influence factor
 c1 and c2 are coupling
factors
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
The formula shows the fact that
there is no an invisible control
to all the audience, every body
affects others and affected by
others equally.
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Single Clapping
Single clapping
Single continuous clapping
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general applause in a theatre
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all the palms in the theatre came
together after a long time applause
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The ‘up here down there’
applause in the square
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An experimental platform of emergence computation
Visualization of courtesy applause and synchronized applause
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
It is difficult to distinguish the virtual
general applause from the real one.
 It is also difficult to distinguish the virtual
synchronous applause from the real one.
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Network is the key to representing the
complex world around us. Small
changes in the topology, affecting only
a few of the nodes, can open up hidden
doors, allowing new possibilities to
emerge.
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Sum up
 Challenge to AI for Knowledge Representation
 Mining Typical Topologies from Real Complex
Networks
 Discover Critical Links and Important
Communities from a Real Network
 Emergence Computation
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To be studied in the future:
1. better measurements of network structure in
network representation
2. better understanding of the relationship between
the architecture of a network and its function
3. better modeling of very large networks
4. mining common concepts of a network across
different scales
5. robustness and security of networks
6. networked data mining
7. virtual reality of emergence.
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Thanks
李 德 毅
[email protected]
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