Download - Control theory in social networks
Control theory in social networks
By Luke Grymek and Sidun Li 03/06/2013
Introduction
http://www.vitbergllc.com http://www.connect-utb.com
What is a social network?
• Collection of individuals and their interactions in a society, which can be in real life or online
• Examples - Facebook - Citation networks - This class
www.connectedaction.net
Example of a Facebook Network Map
Citation networks
http://jasss.soc.surrey.ac.uk/12/4/12.html
EE194 Adv. Control Class
humorgags.blogspot.com
Why study social network?
• $$$ in consumer behavior studies • $$$ in marketing • Help understand/resolve social conflicts
How?
Modeling a social network
• Micro-level: relationships between 2 or a small group of individuals
Modeling a social network
• Meso-level: randomly distributed networks - Exponential random graph model
(Erdős–Rényi : G(n, M), G(n, p) models)
- Scale-free network model
(Barabási–Albert model) http://en.wikipedia.org/wiki/Barab%C3%A1si%E2%80%93Albert_model
http://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model
(p = 0.01)
The network is made of 50 vertices with initial degrees m=1
Modeling a social network
• Macro-level: a combination of meso-level networks
http://en.wikipedia.org/wiki/Social_network
Why controls?
• Too much information = uninformative
Why controls?
• Goal: control “flow of information” in the network, and accurately predict the responses of “inputs” into the system
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t=0 t=1 t=2 t=3
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Why controls?
• Goal: control “flow of information” in the network, and accurately predict the responses of “inputs” into the system
0 0.5
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t=0 t=1 t=2 t=3
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Why controls?
• We can also model networks in a structured fashion (nodes and links can have different meanings, but they can have the same topology)
Dispatcher in a network distributing work to the computers below
Professor Khan distributing projects to his students
Why linear dynamics?
• Conclusions drawn from linear dynamics can be extended to nonlinear systems
• If the controllability matrix of the linearized system has full rank at all points, then it is sufficient for most systems to say that the actual nonlinear system is controllable (i.e. small signal model)
http://web.mit.edu
Structural Controllability: Motivation
Structural Controllability: Motivation
Structural Controllability: Motivation
• C = (B, AB, A2B, … ,AN-1B) • If rank(C)=N we have controllability
Ultimate Goal: Minimize (ND ) whose control is sufficient to control the system’s dynamics
Structural Controllability: Motivation
• Minimum ND can be determined by the ‘maximum matching’ of the network
• Structural controllability problem maps into an equivalent geometrical problem on the network
Goal: Minimize (ND ) whose control is sufficient to control the system’s dynamics
Link to System Controllability
• Structural controllable System controllable for almost all parameter values except for a few combinations of values (which have Lebesgue measure zero)
• Strong structural controllable System controllable Goal: Minimize (ND ) whose control is sufficient
to control the system’s dynamics
Structural Controllability
• Undirected network vs. directed network
www.differencebetween.com
0 a12 a13
a21 0 a23
a31 a32 0
0 0 0
a21 0 0
a31 a32 0 a12 = a21 a13 = a31 a23 = a32
Structural Controllability - Example
Structural Controllability Theorem
Matching • Matching: For an undirected graph, a matching M is
an independent edge set without common vertices. – A vertex is matched if it is incident to an edge in the
matching
We gain control over the network if and only if we directly control each unmatched node
• For a directed graph, an edge subset M is a matching if no two edges in M share a common starting vertex or a common ending vertex. – A vertex is matched if it is incident to an edge in the
matching, otherwise it is unmatched
Maximum matching
• Maximum matching: a matching that covers the most amount of vertices in the graph - it’s called perfect if all vertices are matched (i.e. an elementary cycle) - decompose into bipartite graph (a graph whose vertices can be divided into 2 disjoint sets) - solve using Hopcroft-Karp algorithm, which runs in O(V1/2E) time
Minimum Input Theorem
• Theorem: The minimum number of (input) driver nodes (ND) need to fully control a network G(A) is 1 if there is a perfect matching in G(A). Otherwise, it equals the number of unmatched nodes with respective to any maximum matching set. ND = max{N - |M*|, 1}; (N: total # of nodes, M*: set of matched nodes)
• Intuition: Each node must have its own ‘superior’.
We gain control over the network if and only if we directly control each unmatched node
Matching
• Finding the minimum ND numerically takes O(N1/2L) steps where L denotes number of links (Hopcroft-Karp algorithm)
We gain control over the network if and only if we directly control each unmatched node
Matching Example 1
Directed Path
Max Matching
Matching Example 2
Directed Star
Only 1 link can be part of max matching
Matching Example 3
Only 2 links can be part of max matching
Characterizing and Predicting ND
• ND is determined mainly by the number of incoming and outgoing links each node has and independent of where those links point – Driver nodes tend to avoid hubs
• Denser networks require fewer driver nodes • Larger differences between node degrees
results in more needed drivers • Sparse, heterogeneous networks are the most
difficult to control
ND for different real world networks
Name Nodes Edges ND/N
College Student 32 96 .188
Prison Inmate 67 182 .134
Slashdot 82,169 948,464 .045
WikiVote 7,115 103,689 .666
Epinions 75,888 508,837 .549
Stanford.edu 281,903 2,312,497 .317
Political Blogs 1,224 19,025 .356
Problem solving – System approach
• Model the network using a graph, which by definition is a set of nodes and links
• Apply system control to study the graph: - Given system matrix A and B, is the system controllable? - Study system matrix A to determine the minimum # of non-zero elements in matrix B for controllability
Problem solving – Structural approach
• Model the network • Apply structural control to study the graph :
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations Goal: identify the minimum # of individuals we need to control to control the whole network
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Test in Matlab
• Confirm that the system is controllable A = system topology; B(1,1) = 1;
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Simulations
- Find a maximum matching set and the # of unmatched nodes - Find the driver nodes (the unmatched nodes) - The system is controllable with inputs to the driver nodes
Test in Matlab
• Confirm that the system is controllable A = network topology; B(1 , 1) = 1; or B(13 , 1) = 1; or B(1 4, 1) = 1;
Questions?
References
• “Controllability of complex networks”, Liu, Slotine, & Barabási, 2011 Nature
• “Observability of complex networks”, Liu, Slotine, & Barabási, 2013 PNAS
• “Network Medicine: A Network-based Approach to Human Disease”, Barabási, gulbahce, Loscalzo, 2011 Nat Rev Genet.