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Network IntelligenceNetwork Intelligence

and Networked Intelligenceand Networked Intelligence

网络智能和网络化智能网络智能和网络化智能Deyi Li ( Deyi Li ( 李 德 毅 李 德 毅 ))

ziqinli@public2.bta.net.cnziqinli@public2.bta.net.cn

Aug. 1, 2006Aug. 1, 2006

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Challenge to AI for Knowledge Challenge to AI for Knowledge RepresentationRepresentation

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Study on Knowledge RepresentationStudy on Knowledge Representation one-dimensional one-dimensional representation:representation: predicate calculus, predicate calculus, natural language unatural language u

nderstanding, etcnderstanding, etc. . two-dimensional two-dimensional representation:representation: pattern recognitionpattern recognition,, nneural networkeural network le le

arning, etc.arning, etc. attention on attention on evolutionalevolutional networks networks with uwith u

ncertainty ncertainty was less paid unfortunately.was less paid unfortunately.

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Networks are present everywhere. Networks are present everywhere. All we need is an eye for them.All we need is an eye for them.

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We are witnessing a revolution in We are witnessing a revolution in the making as scientists from all the making as scientists from all different disciplines discover that different disciplines discover that complexity has a strict architecture. complexity has a strict architecture. We have come to grasp the We have come to grasp the important knowledge of networks.important knowledge of networks.

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Networks interact with one another and Networks interact with one another and are recursive .are recursive .

Getting such a diverse group to agree Getting such a diverse group to agree on a common core of knowledge on a common core of knowledge representation about networks is a representation about networks is a significant challenge to both Cognitive significant challenge to both Cognitive Science and Artificial Intelligence. Science and Artificial Intelligence.

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Networks Evolution and Growth drive Networks Evolution and Growth drive the fundamental issue that forms our the fundamental issue that forms our view of network representation and view of network representation and network intelligence.network intelligence.

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Paul.Erdos Albert Barabasi

Reka Albert Steven Strogatz Alfred Renyi

Duncan Watts

ER pure random graph(1960)

WS small world model (1998)

BA scale-free model(1999)

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““Small worlds” and “power law distriSmall worlds” and “power law distributions” are generic properties of nebutions” are generic properties of networks in general. tworks in general.

There is a new knowledge representaThere is a new knowledge representation out there that is the network reprtion out there that is the network representation. esentation.

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It’s the fact that all of these real It’s the fact that all of these real world networks can be explained and world networks can be explained and understood using the same understood using the same concepts, and the same concepts, and the same mathematics, that makes network mathematics, that makes network representation so important in AI representation so important in AI research in the information age.research in the information age.

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Mining Typical Topologies Mining Typical Topologies from Real Complex Networksfrom Real Complex Networks

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typical topologies with randomness

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An evolutional and growth An evolutional and growth network may be by and large network may be by and large characterized by an ideal characterized by an ideal typical modeltypical model

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Expectation of topologies at Expectation of topologies at different scales:different scales:

Small world networkSmall world networkScale free networkScale free networkHub NetworkHub NetworkStar NetworkStar Network

Mining Typical Topology from Real World Mining Typical Topology from Real World Networks at Multi-scaleNetworks at Multi-scale

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Extend more properties of networks Extend more properties of networks

the mass of a nodethe mass of a nodephysical distance between two nodephysical distance between two node

ssthe age of a nodethe age of a nodebetweenness of a linkbetweenness of a linkbetweenness of a node betweenness of a node

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With the extended properties of With the extended properties of networks, we may map relational networks, we may map relational data into networked representation data into networked representation and propose a new direction that is and propose a new direction that is networked data mining.networked data mining.

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A detailed networked dataA detailed networked data

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Mining typical topology with a Mining typical topology with a middle granularitymiddle granularity

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Mining typical topology with a Mining typical topology with a large granularitylarge granularity

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Discover critical links and Discover critical links and important communities from important communities from a real networka 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 communitiesMining communities

Classification:Classification: The typical problem in The typical problem in networked data mining is that of dividing all networked data mining is that of dividing all the nodes of a network into some number the nodes of a network into some number of groups, while minimizing the number of of groups, while minimizing the number of links that run between nodes in different links that run between nodes in different groups.groups.

Clustering:Clustering: Given a network structure, try Given a network structure, try to divide into communities in such a way to divide into communities in such a way that every node belongs only to one of the that every node belongs only to one of the communities.communities.

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Community model can capture the hierarchicCommunity model can capture the hierarchical feature of a Network.al feature of a Network.

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A link removal method A link removal method based on link betweennessbased on link betweenness

Input ： Initial network topology ， the number of communityOutput ： 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.

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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 centersGiven a traffic network, find virtual traffic centers

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Node mass may represent its degree Node mass may represent its degree from data field point of viewfrom data field point of view

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Node mass may also represent its betweennesNode mass may also represent its betweenness from data field point of views from data field point of view

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Emergence ComputationEmergence Computation

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A subtle urge to synchronize is A subtle urge to synchronize is pervasive in nature indeed pervasive in nature indeed synchronized clapping synchronized clapping fireflies flashingfireflies flashing menstrual cycles of women menstrual cycles of women adaptive path minimization by antsadaptive path minimization by ants wasp and termite nest buildingwasp and termite nest building army ant raidingarmy ant raiding fish schooling and bird flockingfish schooling and bird flocking pattern formation in animal coatspattern formation in animal coats coordinated cooperation in slime moldscoordinated cooperation in slime molds

31Nature Vol. 403, 24 Feb.2000Nature Vol. 403, 24 Feb.2000

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The emergence of synchronized The emergence of synchronized clapping is a delightful expression clapping is a delightful expression of self-organization on a human of self-organization on a human scalescale

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emergence mechanism emergence mechanism For everybody in the audience there For everybody in the audience there

are 3 measurements: are 3 measurements: 1.1. time difference at the beginning of the time difference at the beginning of the

applause (TDB)applause (TDB)2.2. interval time of a clap to the next one interval time of a clap to the next one

(IT, represented by t)△(IT, represented by t)△3.3. the clapping strength (CS) the clapping strength (CS)

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If there is no any interaction among audieIf there is no any interaction among audience, the distributions of everybody’s TDB, nce, the distributions of everybody’s TDB, IT and CS, even the number of clap times IT and CS, even the number of clap times all follow a kind of poisson curve like. all follow a kind of poisson curve like.

If there are interactions among audience, tIf there are interactions among audience, the influence to each other depends on the he influence to each other depends on the distance (say rdistance (say rijij) between them. ) between them.

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Assume:Assume:all the clap strengths are the same. all the clap strengths are the same. ““following the many” is fundamental following the many” is fundamental

mechanism and pervasive applicable. mechanism and pervasive applicable. Therefore the relationship of persons Therefore the relationship of persons

in the audience, that is the structure in the audience, that is the structure of the network, encoding how people of the network, encoding how people influence each other is set up in influence each other is set up in formula 1formula 1

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somebody’s just-happened somebody’s just-happened clap moment is tclap moment is tii and the n and the next IT (say t△ext IT (say t△ ii’) is based on ’) is based on his current IT (say t△his current IT (say t△ ii) and ) and influenced by the distanced influenced by the distanced person who’s just-happeneperson who’s just-happened clap is measured by t△d clap is measured by t△ jj a and clapped moment tnd clapped moment t jj

σ represents distance influeσ represents distance influence factornce factor

cc1 1 and cand c22 are coupling facto are coupling factorsrs

)1(,...,2,1))()((1

21'

2

N

j

r

ijijii Niettcttcttij

0 1 2 3 4 5 6 7

０　 １ 　２ 　３ 　４ 　５ 　６　 ７

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The formula shows the fact The formula shows the fact that there is no an invisible that there is no an invisible control to all the audience, control to all the audience, every body affects others and every body affects others and affected by others equally.affected by others equally.

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Single ClappingSingle Clapping

Single clapping Single continuous clapping

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general applause in a theatregeneral applause in a theatre

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all the palms in the theatre came all the palms in the theatre came together after a long time applausetogether after a long time applause

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The ‘up here down there’ The ‘up here down there’ applause in the squareapplause 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 It is difficult to distinguish the virtual general applause from the real one. general applause from the real one.

It is also difficult to distinguish the virtual It is also difficult to distinguish the virtual synchronous applause from the real one. synchronous applause from the real one.

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Network is the key to representing the Network is the key to representing the complex world around us. Small complex world around us. Small changes in the topology, affecting only changes in the topology, affecting only a few of the nodes, can open up hidden a few of the nodes, can open up hidden doors, allowing new possibilities to doors, allowing new possibilities to emerge.emerge.

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Sum upSum up

Challenge to AI for Knowledge Representation Challenge to AI for Knowledge Representation Mining Typical Topologies from Real Complex Mining Typical Topologies from Real Complex

Networks Networks Discover Critical Links and Important Discover Critical Links and Important

Communities from a Real Network Communities from a Real Network Emergence ComputationEmergence Computation

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To be studied in the future: To be studied in the future:

1.1. better measurements of network structure in better measurements of network structure in network representation network representation

2.2. better understanding of the relationship between the better understanding of the relationship between the architecture of a network and its function architecture of a network and its function

3.3. better modeling of very large networksbetter modeling of very large networks4.4. mining common concepts of a network across mining common concepts of a network across

different scales different scales 5.5. robustness and security of networksrobustness and security of networks6.6. networked data miningnetworked data mining7.7. virtual reality of emergence.virtual reality of emergence.

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Thanks Thanks

ziqinli@public2.bta.net.cnziqinli@public2.bta.net.cn

李 德 毅李 德 毅