alexander mikov - program tools for dynamic investigation of social networks
TRANSCRIPT
Authors:Alexander Mikov, Elena Zamyatina,Daria Germanova
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Cuban State University, Cuban State University, Higher School Of Economics (Perm Branch)Higher School Of Economics (Perm Branch),,Perm State UniverityPerm State Univerity
Program Tools for Dynamic Investigation of Social Networks
Higher School Of EconomicsNational Research University
Perm Branch
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Outline
• BackGround• Related Works• Specific Properties of Simulation Software • Simulation Model Representation• The Algorithm of Investigation• Graphs
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Background
• Social Network Analyses:– Telecommunication.– Marketing.– Sociology.– Etc.
• Information
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Related Works
• NetLogo• Repast (Recursive Porous Agent
Simulation Toolkit)• Mason
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Winter Simulation Conference • Jonathan K. Alt, Stephen Lieberman. Representing
Dynamic Social Networks In Discrete Event Social Simulation (Modeling, Virtual Environments and Simulation (MOVES) Institute, Naval Postgraduate School, Monterey, California 93943, USA
• Gatti et al. A Simulation-based Approach To Analyze The Information Diffusion In Microblogging Online Social Network. IBM Research-Brazil Av. Tut´oia 1157, San Paulo (SP), BRAZIL
• O¨ zgu¨r O¨ zmen et al. An Agent-based Simulation Study Of A Complex Adaptive Collaboration Network. Oak Ridge National Laboratory. Oak Ridge, TN 37830, USA
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Requirements:
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Simulation system Triad and it’s purposes in past
• Triad – 80-90 years of 20-th century• Linguistic and software tools for computer
aided design of computer systems (aviation industry)
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Triad.Net – the Distributed Simulation System with a remote access
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TriadClientTriadBuilder,TriaDebuggerTriadSecurity
Web-browser
TriadEditor,TriadBuilder,
TriadDebuggerTriadSecurity
Data Base
Server
Server
Simulation Model
TriadBalance
Triad.Net components
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TriadEditor-the subsystem for Triad-model design
TriadDebugger – Triad verification and validation
TriadMining – the subsystem of an output data intellectual processing
TriadSecurity – the safety subsystem
TriadCompile-Triad-compiler
TriadBuilder-the subsystem of completeness of partly defined model
TriadCore-the Kernel of Triad simulation system
TriadBalance – the subsystem with load balancing function
Simulation model description
• Structure layer (STR)
• Routine layerRoutine layer ( (ROUTROUT))• Message layer (MES)Message layer (MES)
• The layer of structure is dedicated to the description of the objects of the simulation model and their interconnections
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Simulation model description
• Structure layerStructure layer ((STRSTR))..
• Routine layer (ROUT).
• Message layerMessage layer ((MESMES))..
• The layer of routines describes the behavior of the simulation model objects
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Simulation model description
• Structure layerStructure layer ((STRSTR))..
• Routine layerRoutine layer ((ROUTROUT))..
• Message layer (MES).
• The layer of messages is used for description
of the messages with complicated structures
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Structure layer operations
• To add/to delete the poles, nodes and arcs.• The operations with graphs (union, intersection and so on)• Graph constants using.
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P[2]
P[1]
P[2]
P[1]
V[1]V[2]
G
G = G + V[2](A)
V[2]
A
+ (V[2].A -> V[1].P[1])open V[1] := Str1 [ P[1]=Outp, P[2]=Inp ]
Outp
Inp
Graph Constants
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Simple path
Path(m)
dPath(m)
Simple cycle
Cycle(m)
dCycle(m)
Complete
Compl(m)
dCompl(m)
Grid
Rectan(m1,m2…)
dRectan(m1,m2…)
Tree
Tree(m,n)
dTree(m,n)
Bipart
Bipart(m,n)
dBipart(m,n)
Star
Star(m)
dStar(m)
Disconnected
nc(m)
dnc(m)
m=4m=6m=4m1=2; m2=3m=3; n=2m=2; n=3m=5m=5
Random graph (model of Erdösh-Renyi) with 30 nodes and probability p=0,25.
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Random graph (model of Erdösh-Renyi) with a number of nodes=30 and a probability p=1.
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Barabashi-Albert graph
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An example: the model of computer network and
it’s description using Triad
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Router
Router
Router
Router
Router
Type Router,Host; integer i;M:=dStar(Rout[5]<Pol[4]>);M:=M+node Hst[8]<Pol>;M.Rout[0]=>Router;for i:=1 by 1 to 4 do
M.Rout[i]=>Router;M:=M+edge(Rout[i].Pol[1]—
Hst[2*i-2]);M:=M+edge(Rout[i].Pol[2]—
Hst[2*i-1]);endf;for i:=0 by 1 to 7 do
M.Hst[i]=>Host;endf;
Graphical Editor
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The Algorithm of Investigation
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Min(var)
Count(event)
Check(Pol)
Information procedureSimulation Model
Conditions of simulation
A List of Standard Information Procedures in Graphical Editor
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An example of information procedure –intellectual analyze
information procedure EVENT_SEQUENCE (in ref event E1,E2,E3;out Boolean ARRIVED)
initial interlock (E2,E3); ARRIVED := false; case of E1:available(E2); E2:available( E3); E3:ARRIVED:=true; endc endinf
24An investigator may detect the arrival of the sequence of events E1→E2→E3
Structural Characteritics
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Conditions of simulation
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Conditions of simulation<name>(<a list of generic parameters>)
(<input and output formal parameters>) initial <a sequence of statements> endi
<a list of information procedures> <a sequence of statements>
processing <a sequence of statements>…endprocendcond
Simulation run
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simulatesimulate
<<a list of an elements of models, being inspected> on conditions of simulation <name>
(a list of actual generic parameters>)[<a list of input and output actual parameters>]
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Distributed simulation model
• Distributed simulation models – a set of logical processes being fulfilled on different compute nodes and communicating with one another by passing messages;
• Each Logical process has local time calendar
• Time paradox – arriving input message with time stamp less than local time
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N1
N4
N3
N2
N6N5
N1
N4
N3
N2
N6N5
LP1
LP2
Distributed Models
30LP1 LP2 LPN
Time Time paradox
Two classes of algorithms
• Conservative
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Optimizations - lookahead
Optimistic Algorithm
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Problems: •Rollbacks•Memory for states
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To find out the hidden dependences of events
Simulation model structure
The sequence of accumulated events
The results of the former runs
The expert knowledge
Expirements
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ILLIAC
050
100150200250300350400
0 20 40 60 80
количество вычислительных узлов
врем
я вы
полн
ения
Оптимистический АлгоритмКонсервативный алгоритмАлгоритм TriadRule
Experiments
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Откаты
020406080
100120140160
0 20 40 60 80
количество узлов
коли
чест
во о
ткат
ов
Оптимистический алгоритм
Алгоритм TriadRule
Thank you for your kindly Attention
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Load balancing
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The reasonses of disbalance
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Communication lines geterogenity
Computing nodes geterogenity
Distributed application geterogenity
Simulation Model Operations
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Node1(Head Node)
Node4(Compute Node)
Node2(Compute Node)
Node5(Compute Node)
Node3(Compute Node)
Node6(Compute Node)
Object 1 Object 2
Object 3 Object 4
Object 8 Object 9
Object 6
Object 10
Object 7
Object 5
Node1(Head Node)
Node4(Compute Node)
Node2(Compute Node)
Node5(Compute Node)
Node3(Compute Node)
Node6(Compute Node)
Object 1 Object 2
Object 3 Object 4
Object 8Object 9
Object 6
Object 10
Object 7
Object 5
Decentralized algorithm multiagent load balancing subsystem
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Compute node
Cluster
Data baseRemote Access
Computing Node and a Simulation Model Fragment
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Monitoring Agent
(SM)
Monitoring Agent
(CS)
Agent of Analyses
Agent of distribution
Agent of migration
Communication of the agents
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Agent of migrationAgent of distribution
Black Board Black Board
Monitoring Agent Monitoring Agent
Agent of AnalysesAgent of
distribution
Rules and metarules for the agent of distribution
MetaRule
MetaRule1 MetaRule2 MetaRuleN
1Rule 2Rule 3Rule RuleK 1+RuleK
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The procedure for the frequency of specific event monitoring
infprocedure EventAverage(event V; in real T ) : realinitial
integer eventCount;real time;eventCount := 0;time := 0;
endihandling
time := T;eventCount:= eventCount + 1;
endhprocessing
EventAverage := eventCount / time;endp
endinf
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Subsystem of visualization of the results of monitoring
Conclusion
• Multimodel investigation of computer networks
• Adaptability of software to incorporate into a simulation model new devices and new algorithms that govern their work
• Simulation Model Completeness Analyses and Simulation model Redefining
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Conclusions
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Thank you for your kindly Attention
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