Topological Data Analysis (Spring 2018) TDA on Networks · Topological Data Analysis (Spring 2018)...
Transcript of Topological Data Analysis (Spring 2018) TDA on Networks · Topological Data Analysis (Spring 2018)...
Topological Data Analysis (Spring 2018)TDA on Networks
Instructor: Mehmet Aktas
March 27, 2018
1 / 20 Instructor: Mehmet Aktas TDA on Networks
Outline
1 Introduction
2 Filtrations on NetworksThe Rips Complex of a NetworkThe Dowker Filtration of a Network
3 Application
2 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Outline
1 Introduction
2 Filtrations on NetworksThe Rips Complex of a NetworkThe Dowker Filtration of a Network
3 Application
3 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Graphs
Structured data representing relationship btw objects
Important in modeling sophisticated structures and their interaction
Formed by
A set of verticesA set of edges
Examples
Computer networks
Social networks
Protein interactionnetworks
4 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Attributed Graphs
Vertices have a set of attributes describing theproperties of them
Two source of data: Structure & Attribute
Everywhere
Social networks
Co-authorship networks
5 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Attributed Graphs
Vertices have a set of attributes describing theproperties of them
Two source of data: Structure & Attribute
Everywhere
Social networks
Co-authorship networks
5 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Attributed Graphs
Vertices have a set of attributes describing theproperties of them
Two source of data: Structure & Attribute
Everywhere
Social networks
Co-authorship networks
5 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Attributed Graphs
Vertices have a set of attributes describing theproperties of them
Two source of data: Structure & Attribute
Everywhere
Social networks
Co-authorship networks
5 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Attributed Graphs
Vertices have a set of attributes describing theproperties of them
Two source of data: Structure & Attribute
Everywhere
Social networks
Co-authorship networks
5 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
Let G = (X ,E) be a weighted network where X is the set of vertices,E ⊂ X ×X is the set of edges, and ω ∶ X ×X → R is an edge weightfunction.
If there are two different edge weight functions ω,ω′ defined on G , wecan use the l∞ distance as a measure of network similarity between(G , ω) and (G , ω′):
∣∣ω − ω′∣∣l∞ ∶= maxe∈E
∣ω(e) − ω′(e)∣.
Given two vertex sets X and Y , we need to decide how to match uppoints of X with points of Y
Any such matching will yield a subset R ⊂ X ×Y , which is called acorrespondence.
6 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
Let G = (X ,E) be a weighted network where X is the set of vertices,E ⊂ X ×X is the set of edges, and ω ∶ X ×X → R is an edge weightfunction.
If there are two different edge weight functions ω,ω′ defined on G , wecan use the l∞ distance as a measure of network similarity between(G , ω) and (G , ω′):
∣∣ω − ω′∣∣l∞ ∶= maxe∈E
∣ω(e) − ω′(e)∣.
Given two vertex sets X and Y , we need to decide how to match uppoints of X with points of Y
Any such matching will yield a subset R ⊂ X ×Y , which is called acorrespondence.
6 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
Let G = (X ,E) be a weighted network where X is the set of vertices,E ⊂ X ×X is the set of edges, and ω ∶ X ×X → R is an edge weightfunction.
If there are two different edge weight functions ω,ω′ defined on G , wecan use the l∞ distance as a measure of network similarity between(G , ω) and (G , ω′):
∣∣ω − ω′∣∣l∞ ∶= maxe∈E
∣ω(e) − ω′(e)∣.
Given two vertex sets X and Y , we need to decide how to match uppoints of X with points of Y
Any such matching will yield a subset R ⊂ X ×Y , which is called acorrespondence.
6 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
Let G = (X ,E) be a weighted network where X is the set of vertices,E ⊂ X ×X is the set of edges, and ω ∶ X ×X → R is an edge weightfunction.
If there are two different edge weight functions ω,ω′ defined on G , wecan use the l∞ distance as a measure of network similarity between(G , ω) and (G , ω′):
∣∣ω − ω′∣∣l∞ ∶= maxe∈E
∣ω(e) − ω′(e)∣.
Given two vertex sets X and Y , we need to decide how to match uppoints of X with points of Y
Any such matching will yield a subset R ⊂ X ×Y , which is called acorrespondence.
6 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
The distortion of a correspondence between X and Y is
dis(R) ∶= max(x ,y),(x ′,y ′)∈R
∣ωX (x , x ′) − ωY (y , y ′)∣.
We denote the set of all correspondences between X and Y byR(X ,Y ).
The network distance is defined as follows:
dN ((X , ωX ), (Y , ωY )) ∶= 1
2min
R∈R(X ,Y )dis(R).
7 / 20 Instructor: Mehmet Aktas TDA on Networks
Introduction
Network Distances
The distortion of a correspondence between X and Y is
dis(R) ∶= max(x ,y),(x ′,y ′)∈R
∣ωX (x , x ′) − ωY (y , y ′)∣.
We denote the set of all correspondences between X and Y byR(X ,Y ).
The network distance is defined as follows:
dN ((X , ωX ), (Y , ωY )) ∶= 1
2min
R∈R(X ,Y )dis(R).
7 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Outline
1 Introduction
2 Filtrations on NetworksThe Rips Complex of a NetworkThe Dowker Filtration of a Network
3 Application
8 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
For a metric space (X ,dX ), the diameter of a subset σ ⊂ X is definedas diam(σ) ∶= maxx ,x ′∈σ dX (x , x ′).
The Rips complex of a metric space (X ,dX ) is defined for each r ∈ Ras
RδX ∶= {σ ∈ Pow(X ) ∶ diam(σ) ≤ δ}.
For any weigthed network (X , ωX ), define the weight of a subset as amap wgtX (σ) ∶ Pow(X )→ R given by:
wgtX (σ) ∶= maxx ,x ′∈σ
ωX (x , x ′)
9 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
For a metric space (X ,dX ), the diameter of a subset σ ⊂ X is definedas diam(σ) ∶= maxx ,x ′∈σ dX (x , x ′).
The Rips complex of a metric space (X ,dX ) is defined for each r ∈ Ras
RδX ∶= {σ ∈ Pow(X ) ∶ diam(σ) ≤ δ}.
For any weigthed network (X , ωX ), define the weight of a subset as amap wgtX (σ) ∶ Pow(X )→ R given by:
wgtX (σ) ∶= maxx ,x ′∈σ
ωX (x , x ′)
9 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
For a metric space (X ,dX ), the diameter of a subset σ ⊂ X is definedas diam(σ) ∶= maxx ,x ′∈σ dX (x , x ′).
The Rips complex of a metric space (X ,dX ) is defined for each r ∈ Ras
RδX ∶= {σ ∈ Pow(X ) ∶ diam(σ) ≤ δ}.
For any weigthed network (X , ωX ), define the weight of a subset as amap wgtX (σ) ∶ Pow(X )→ R given by:
wgtX (σ) ∶= maxx ,x ′∈σ
ωX (x , x ′)
9 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
The Rips complex of a network (X , ωX ) ∈ N is defined as
RδX ∶= {σ ∈ Pow(X ) ∶ wgtX (σ) ≤ δ}.
The Rips complex as defined above yields a valid simplicial complexon a network for each parameter δ ∈ R. Thus to any network(X , ωX ), we may associate the Rips filtration {Rδ
X ↪Rδ′
X}δ≤δ′ .For each k ∈ Z≥0, we denote the k-dimensional persistence diagram byDgmR
k (X ).
Proposition
Let (X , ωX ), (Y , ωY ) ∈ N . Then we have:
dB(DgmRk (X ),DgmR
k (Y )) ≤ 2dN (X ,Y ).
10 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
The Rips complex of a network (X , ωX ) ∈ N is defined as
RδX ∶= {σ ∈ Pow(X ) ∶ wgtX (σ) ≤ δ}.
The Rips complex as defined above yields a valid simplicial complexon a network for each parameter δ ∈ R. Thus to any network(X , ωX ), we may associate the Rips filtration {Rδ
X ↪Rδ′
X}δ≤δ′ .For each k ∈ Z≥0, we denote the k-dimensional persistence diagram byDgmR
k (X ).
Proposition
Let (X , ωX ), (Y , ωY ) ∈ N . Then we have:
dB(DgmRk (X ),DgmR
k (Y )) ≤ 2dN (X ,Y ).
10 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
The Rips complex of a network (X , ωX ) ∈ N is defined as
RδX ∶= {σ ∈ Pow(X ) ∶ wgtX (σ) ≤ δ}.
The Rips complex as defined above yields a valid simplicial complexon a network for each parameter δ ∈ R. Thus to any network(X , ωX ), we may associate the Rips filtration {Rδ
X ↪Rδ′
X}δ≤δ′ .For each k ∈ Z≥0, we denote the k-dimensional persistence diagram byDgmR
k (X ).
Proposition
Let (X , ωX ), (Y , ωY ) ∈ N . Then we have:
dB(DgmRk (X ),DgmR
k (Y )) ≤ 2dN (X ,Y ).
10 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Rips Filtration
The Rips complex of a network (X , ωX ) ∈ N is defined as
RδX ∶= {σ ∈ Pow(X ) ∶ wgtX (σ) ≤ δ}.
The Rips complex as defined above yields a valid simplicial complexon a network for each parameter δ ∈ R. Thus to any network(X , ωX ), we may associate the Rips filtration {Rδ
X ↪Rδ′
X}δ≤δ′ .For each k ∈ Z≥0, we denote the k-dimensional persistence diagram byDgmR
k (X ).
Proposition
Let (X , ωX ), (Y , ωY ) ∈ N . Then we have:
dB(DgmRk (X ),DgmR
k (Y )) ≤ 2dN (X ,Y ).
10 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Example
11 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Example
11 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Drawbacks of Rips Filtration
It is blind to directed edge weights.
The Rips complex does not absorb information in dimensions higherthan one.
Simplices in a Rips complex are not formed with respect to any“central authority.” This could be undesirable in, for example, asmall-world network, where one would desire simplices to be formedwith respect to particular “hub” nodes.
12 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Drawbacks of Rips Filtration
It is blind to directed edge weights.
The Rips complex does not absorb information in dimensions higherthan one.
Simplices in a Rips complex are not formed with respect to any“central authority.” This could be undesirable in, for example, asmall-world network, where one would desire simplices to be formedwith respect to particular “hub” nodes.
12 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Rips Complex of a Network
Drawbacks of Rips Filtration
It is blind to directed edge weights.
The Rips complex does not absorb information in dimensions higherthan one.
Simplices in a Rips complex are not formed with respect to any“central authority.” This could be undesirable in, for example, asmall-world network, where one would desire simplices to be formedwith respect to particular “hub” nodes.
12 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Outline
1 Introduction
2 Filtrations on NetworksThe Rips Complex of a NetworkThe Dowker Filtration of a Network
3 Application
13 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Dowker Filtration
Given a network (X , ωX ) ∈ N and δ ∈ R, Rd ,X ⊂ X ×X is defined as
Rδ,X ∶= {(x , x ′) ∶= {(x , x ′) ∶ ωX (x , x ′) ≤ δ}.
Dsiδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (xi , x ′) ∈
Rδ,X for each xi}.For δ′ ≥ δ, there is a natural inclusion Dsi
δ,X ↪Dsiδ′,X
This is called the Dowker sink filtration.
Dsoδ,X ∶= {σ = [x0, ..., xn] ∶ there exists x ′ ∈ X such that (x ′, xi) ∈
Rδ,X for each xi}.This is called the Dowker source filtration.
14 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Example
15 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Example
15 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Sink vs Source
The sink and source filtrations are not equal in general. However ...
Theorem
For any k ∈ Z≥0 and (X , ωX ) ∈ N , we have
Dgmsik (X ) = Dgmso
k (X ).
Thus we may call either of the two diagrams above the k-dimensionalDowker diagram of X , denoted Dgm●k(X ).
16 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Sink vs Source
The sink and source filtrations are not equal in general. However ...
Theorem
For any k ∈ Z≥0 and (X , ωX ) ∈ N , we have
Dgmsik (X ) = Dgmso
k (X ).
Thus we may call either of the two diagrams above the k-dimensionalDowker diagram of X , denoted Dgm●k(X ).
16 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Sink vs Source
The sink and source filtrations are not equal in general. However ...
Theorem
For any k ∈ Z≥0 and (X , ωX ) ∈ N , we have
Dgmsik (X ) = Dgmso
k (X ).
Thus we may call either of the two diagrams above the k-dimensionalDowker diagram of X , denoted Dgm●k(X ).
16 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Example
17 / 20 Instructor: Mehmet Aktas TDA on Networks
Filtrations on Networks The Dowker Filtration of a Network
Example
17 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Outline
1 Introduction
2 Filtrations on NetworksThe Rips Complex of a NetworkThe Dowker Filtration of a Network
3 Application
18 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Simulated hippocampal networks
An animal explores a given environment or arena, specific “placecells” in the hippocampus show increased activity at specific spatialregions, called “place fields”.
Each place cell shows a spike in activity when the animal enters theplace field linked to this place cell, accompanied by a drop in activityas the animal moves far away from this place field.
Is the time series data of the place cell activity, referred to as “spiketrains”, enough to detect the structure of the arena?
19 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Simulated hippocampal networks
An animal explores a given environment or arena, specific “placecells” in the hippocampus show increased activity at specific spatialregions, called “place fields”.
Each place cell shows a spike in activity when the animal enters theplace field linked to this place cell, accompanied by a drop in activityas the animal moves far away from this place field.
Is the time series data of the place cell activity, referred to as “spiketrains”, enough to detect the structure of the arena?
19 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Simulated hippocampal networks
An animal explores a given environment or arena, specific “placecells” in the hippocampus show increased activity at specific spatialregions, called “place fields”.
Each place cell shows a spike in activity when the animal enters theplace field linked to this place cell, accompanied by a drop in activityas the animal moves far away from this place field.
Is the time series data of the place cell activity, referred to as “spiketrains”, enough to detect the structure of the arena?
19 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Experiment
20 / 20 Instructor: Mehmet Aktas TDA on Networks
Application
Experiment
20 / 20 Instructor: Mehmet Aktas TDA on Networks