FASTEN: Fast Sylvester Equation Solver for Graph Mining
Transcript of FASTEN: Fast Sylvester Equation Solver for Graph Mining
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Presenter: Boxin Du
Joint work by:
Hanghang TongBoxin Du
FASTEN: Fast Sylvester Equation Solver for Graph Mining
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Why Sylvester Equation?
2
βͺ Link users from different social networks
[Zhang et alβ 16]
βͺ Protein function prediction
[Vishwanathan et alβ 10]
βͺ Fraudulent transaction pattern matching
[Du et alβ 17] βͺ Chemical similarity calculation
[Yu-Chen et alβ 15]
Query pattern
?
β
Network alignment Graph kernel
Subgraph matching Node similarity
Transaction network Chemical compound network
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What is Sylvester Equation: an example on plain graph
3
Adjacency matrix
ππ:
G2
Solution matrix X of the
Sylvester equation:
π = πππππ + π(ππ and ππ are normalized)
βͺ Sylvester equation π = πππππ + π gives the cross-network node similarity matrix π;
G1
Adjacency matrix:
ππ:
Prior knowledge of
cross-network link: π
0 1 1 0
1 0 1 1
1 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 1
0 1 1 0
ποΌ0 0 0 0
0 0 0 0
0 0 0 0
1 0 0 0
[1] Zhang, Si, and Hanghang Tong. "Final: Fast attributed network alignment." Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. ACM, 2016.
[2] Singh, Rohit, Jinbo Xu, and Bonnie Berger. "Global alignment of multiple protein interaction networks with application to functional
orthology detection." Proceedings of the National Academy of Sciences (2008).
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What is Sylvester Equation: an example on attributed graph
4
Input graphs with node attributes
(colors and shapes)
Solution X of π β Οπ,π=ππ ππ
πππ(ππ
ππ)π = π
(ππ and ππ are normalized)
G1 G2
βͺ Sylvester equation π β Οπ,π=ππ ππ
πππ(ππ
ππ)π = π gives the cross-network node similarity matrix π;
0 1 0 0
1 0 1 1
0 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 0
0 1 0 0
ππ: ππ:ποΌ0 0 0 0
0 0 0 0
0 0 0 0
0 0 1 0
[1] Zhang, Si, and Hanghang Tong. "Final: Fast attributed network alignment." Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. ACM, 2016.
[2] Singh, Rohit, Jinbo Xu, and Bonnie Berger. "Global alignment of multiple protein interaction networks with application to functional
orthology detection." Proceedings of the National Academy of Sciences (2008).
e.g. ππππ:
0 0 0
0 0 0
0 0 0 0
0 0 0 0
ππππ:
0 0 0 0
0 0
0 0 0 0
0 0 0 0
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βͺ Given:
β’ Two graphs πΊ1 and πΊ2 (the adjacency matrices are ππ and ππ);
β’ The preference matrix π.
βͺ Find: the solution π of Sylvester equation:
or π± of its equivalent linear system:
βͺ Mathematical details:
β’ ππ β Ξ± 1/2ππβπ/π
ππππβπ/π
, ππ β Ξ± 1/2ππβπ/π
ππππβπ/π
;
β’ ππ and ππ are the diagonal degree matrices of ππ and ππ, 0 < πΌ < 1;
β’ π = ππ βππ (both are normalized), π± = vec(π), π = vec(π).
Formal Definition of Sylvester Equation (Plain Graph)
5
π β ππππππ = π
π βπ π± = π
[1] Zhang, Si, and Hanghang Tong. "Final: Fast attributed network alignment." Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. ACM, 2016.
[2] Singh, Rohit, Jinbo Xu, and Bonnie Berger. "Global alignment of multiple protein interaction networks with application to functional orthology
detection." Proceedings of the National Academy of Sciences (2008).
G2G1
Solution matrix X
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βͺ Given:
β’ Two graphs πΊ1 = {π΄1, π1}, πΊ2 = {π΄2, π2};
β’ The preference matrix π.
βͺ Find: the solution π of Sylvester equation:
or π± of its equivalent linear system:
βͺ Mathematical details:
β’ππ(ππ)
β Ξ± 1/2ππβπ/π
πππππππ
πππβπ/π
, ππ(ππ)
β Ξ± 1/2ππβπ/π
πππππππ
πππβπ/π
;
β’ππ(ππ)
is the adjacency matrix βfilteredβ by attribute i and j.
β’ π : the number of node attributes, π± = vec(π), π = vec(π).
Formal Definition of Sylvester Equation (Attributed Graph)
6
π2ππ, π = 1 if node
π has node attribute
π, o/w it is zero.
π βπ,π=π
π
πππππ(ππ
ππ)π = π
π βπ,π=π
π
(ππππβππ
ππ) π± = π
[1] Zhang, Si, and Hanghang Tong. "Final: Fast attributed network alignment." Proceedings of the 22nd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. ACM, 2016.
[2] Singh, Rohit, Jinbo Xu, and Bonnie Berger. "Global alignment of multiple protein interaction networks with application to functional
orthology detection." Proceedings of the National Academy of Sciences (2008).
Solution matrix X
G2G1
0 0 0
0 0 0
0 0 0 0
0 0 0 0
ππππ:
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Challenges of Solving the Sylvester Equation
βͺ Size of ππ βππ:
β π2 Γ π2 (for plain graphs with π nodes and π edges);
β Straightforward solver costs O(π6) (time) and O(π2) (space);
β State-of-the-art methods: time complexity at least O ππ + π2 ;
βͺ With node attributes:
β Add additional O(π) complexity (for π discrete node attributes);
βͺ Size of solution matrix π:
β π Γ π ;
β Usually not sparse;
β Limit the time/space complexity of the equation solver.
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The Ξ©(π2)bottleneck
The Ξ©(π2)bottleneck
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βͺ Obs.: Tradition methods: all attributed and provide exact solution, but high complexity;
Recent methods: at least π(π2), and are often approximated/not attributed.
βͺ Q: Can we have a solution that is attributed, exact, and more efficient?
Comparison of Methods for the Sylvester EquationAlgorithm Attributed
(Y/N)
Exact
Solution (Y/N)
Time
Complexity
Space
Complexity
Fixed Point (FP) [Vishwanathan et alβ 10] π(π3) π(π2)
Conjugate Gradient (CG) [Y Saad et alβ 03] π(π3) π(π2)
Sylv. [Vishwanathan et alβ 10] π(π3) π(π2)
ARK [U Kang et alβ 12] π(π2) π(π2)
Cheetah [L Li et alβ 10] π(ππ2) π(π2)
NI-Sim [C Li et alβ 10] π(π2) π(π2π2)
FINAL-P [S Zhang et alβ16] π(ππ + π2) π(π2)
FINAL-NE [S Zhang et alβ16] π(πππ + ππ2) π(π2)
FINAL-N+ [S Zhang et alβ16] π(π2) π(π2)
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FASTEN-P π(ππ2) π(π2)
FASTEN-P+ π(ππ + π2π) π(π + ππ)
FASTEN-N π(ππ/+ππ2/π) π(π/π + π2)
FASTEN-N+ π(ππ + π2ππ) π(π + πππ)
Th
is P
ap
er
Recen
t
meth
od
sT
rad
ition
al
meth
od
s
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Roadmap
βͺMotivations
βͺBackground
βͺProposed Algorithms for plain graphs
βͺProposed Algorithms for attributed graphs
βͺExperimental Results
βͺConclusions
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Krylov Subspace Method (KSM) for Linear Systemβͺ Minimal residual method for linear system ππ± = π (π± β Rn):
β Extract π± from π-dimensional subspace of Rn π± β π±π + πΎπ
β Minimize residual π« = π β ππ± β₯ πΏπ small scaled system
β Iteratively update π± and π« until π« 2 is small enough
βͺ Example:
β Let π±π = π, π, π π»(i.e. π«π = π), extract π±π β πΎπ.
β Let π±π = π±π + π³π, minimize π« = π«π β ππ³π (π« β₯ πΏπ).
β Update π± in 3-d space.
β ).10 [1] Saad, Yousef. Iterative methods for sparse linear systems. Vol. 82. siam, 2003.
πΏπ = ππΎπ
πΎπ
π«π
πΆ
ππ³π
π±π
π«π β ππ³π: new residual
πΏπ: Subspace of constraints
3 1 11 2 11 1 3
π₯1π₯2π₯3
=111
π π± π
6 611 6
π₯1β²
π₯2β²=
45
πβ² π² π
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KSM for Linear System (contβd)βͺ Krylov subspace:
βπΎπ π, π«π = π πππ{π«π, ππ«π, π2π«π, β¦ , ππβ1π«π};
β Arnoldi process outputs π orthonormal basis: ππ = π―1, π―2, β¦ , π―π , π β {π, π + 1}
βπππ = ππ+1ΰ·©ππ
βͺ Krylov subspace-based Minimal Residual method:
β Extract solution from π-dimensional Krylov subspace (let πΎπ = πΎπ π, π«π );
β Minimize the residual π« and update solution at every iteration.
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π ππ = ππ+1ΰ·©ππ
[1] Saad, Yousef. Iterative methods for sparse linear systems. Vol. 82. siam, 2003.
Upper-Hessenberg
matrix
π«π
ππ«π
π―1
π―2
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βͺ Arnoldi: π(π) for sparse system;
βͺ Solve small scaled system every iteration;
βͺ Exact solution, no approximation needed;
βͺ Upper-Hessenberg makes solving system faster.
Minimize residual
π½ π² = π β ππ±2
= π β π π±0 + πππ² 2
= β―= ||π½π1 β ΰ·©πππ²||2
Advantages of KSM with Minimal Residual
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equivalent to solve:
[1] Saad, Yousef. Iterative methods for sparse linear systems. Vol. 82. siam, 2003.
β’ Details:
A x Aβ yb b= =Minimize residual
Update solution in
original dimension
High dimensional system
Low dimensional systemΰ·©πππ² = π½π1
=
on Krylov subspace
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Challenges of Applying KSM on Sylvester Equation
βͺ Size of π βπ π± = ποΌ
β Generate πΎπ2(π β ππ βππ, π«π) π π4 or π π2 in time/space cost;
βͺ Size of π β Οπ,π=ππ (ππ
ππβππ
ππ) π± = ποΌ
β Generate πΎπ2(π β Οπ,π=ππ (ππ
ππβππ
ππ) , π«π) π(ππ4) or π ππ2 cost.
βͺ Example:
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G2G1Krylov subspace of
πΎπ2(π β ππ βππ, π«π):16 dimension
0 1 1 0
1 0 1 1
1 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 1
0 1 1 0
0 0 0 0
0 0 0 0
0 0 0 0
1 0 0 0
G2G1
πππππ
*
* ,β¦
π multiplication
π2 Γ π2
π2 Γ π2
*π2 Γ π2
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Roadmap
βͺMotivations
βͺBackground
βͺProposed Algorithms for plain graphs
βͺProposed Algorithms for attributed graphs
βͺExperimental Results
βͺConclusions
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Key Ideas
βͺ#1: Kronecker Krylov Subspace (KKS)
β Implicit construction of the original large Krylov subspace
βLargely reduce the time/space complexity π π4 π π2
βͺ#2: MRES* on KKS with Implicit Solution Representation
βSolve small scaled system and update solution till converge
βFurther reduce the time/space complexity π ππ2 π π2π + ππ
*: MRES: Minimal Residual method
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Theorem: ππ βπk forms
the orthonormal basis of the
Kronecker Krylov subspace;
donβt need to be computed
directly
Kronecker Krylov Subspace (Details)
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βͺ Step 1: Choose Arnoldi vectors π , π; π π2
βͺ Step 2: Generate πΎπ ππ, π ; π ππ
βͺ Step 3: Generate πΎπ ππ, π ; π(ππ)
βͺ Details:
β Choosing π , π s.t. π«π β πΎπ ππ, π β πΎπ(ππ, π):
β If ππ 1β€ ππ β
,
π: ππβs column of largest norm, π = ππππ/ π π
π
β If ππ 1> ππ β
,
π : ππβs row of largest norm, π = ππππ / π π
π
πΎπ ππ, π β πΎπ(ππ, π)
ππππ = ππ+1ΰ·©π1 ππππ = ππ+1ΰ·©π2
ππ = π―1, π―2, β¦ , π―π πk = π°1, π°2, β¦ ,π°π
π βπ π± = π
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Example
βͺ Step 1: ππ = π (π±π = π), π = 0,0,0,1 π, π = 1,0,0,0 π;
βͺ Step 2: πΎπ ππ, π = π πππ{ 0,0.7071,0.7071,0 ,π 1,0,0,0 π};
βͺ Step 3: πΎπ ππ, π = π πππ{ 0,0.7071,0.7071,0 π, 0,0,0,1 π};
βͺ πΎπ ππ, π β πΎπ ππ, π = π πππ{π―π βπ°π, π―π βπ°π, π―π βπ°π, π―π βπ°π}.
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G2G10 1 1 0
1 0 1 1
1 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 1
0 1 1 0
0 0 0 0
0 0 0 0
0 0 0 0
1 0 0 0
G2G1
π A2A1
π βπ π± = π
π―π π―π
π°π π°π
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Minimal Residual (Details):
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βͺ Step 1: Initial residual: π«π = π β π β πΌπ π±π
βͺ Step 2: Let new solution:
π± = π±π + π³π, π³π β πΎπ ππ, π β πΎπ(ππ, π)
βͺ Step 3: Minimize new residual:
ππ= ππ+π
π ππππ+π β ΰ·©πππΰ·©πππ + ππ+π,ππππ+1,π
π»
π
βͺ Both π and π are π by π: small scaled system.
βͺ Step 4: Update solution π and residual π.
π β π + ππ€πππ€π, π β π β ππ€+πΰ·©πππΰ·©ππ
πππ€+ππ + ππ€πππ€
π
βπ L(π)
Small system L(π) = π
Easy to solve!, π βͺ π
Effectiveness: this method
gives the exact solution of the
Sylvester equation on plain
graphs w.r.t. a tolerance π.
π βπ π± = π
Complexity: Time: π(ππ2), Space: π(π2)=
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FASTEN-Pβͺ Major steps:
βͺ Details:
β π is often initialized as π, and π = π;
β Overall Complexity: time: πΆ(πππ); space: πΆ(ππ);
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πΆ(ππ)Choosing
Arnoldi
vectors π and
π by π
πΆ(ππ)Arnoldi
Process on ππ
and ππ to get
the orthogonal
basis πk, πk
πΆ(ππππ β ππ)Solve the new
linear system in
low dimensional
space
πΏ π = π
πΆ(πππ)Update π and
π by π and
check
stopping
condition
Till converge ( ππΉ< π, e.g. 10β8).
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Can we further scale up?
βͺ Goal: complexity: from π(π2) to linear
βͺ Difficulties:
βπ: π Γ π, π(π2) seems to be the lower bound;
βπ: in general not sparse.
βͺ Observation:
βπ is often sparse and low-rank (sparse anchor links across network);
β If prior anchor links are unknown: π is uniform (rank 1);
βπ is low-rank π must have low-rank property (see proof in paper).
βͺ Solution:
β Implicit representation of residual π, intermediate solution π.
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π β ππππππ = π
0 0 0 0
0 0 0 0
0 0 0 0
1 0 0 0
G2G1
π
π Γ π
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Kronecker Krylov Subspace with Low-rank Residualβͺ Step 1: Represent ππ by low-rank matrices ππ, ππ: π(π).
βͺ Step 2: Choose Arnoldi vectors π , π: π(ππ) (π: rank of ππ, ππ)
βͺ Step 3: Generate πΎπ ππ, π , πΎπ ππ, π : π(ππ), and obtain
βͺ Details:
β Choosing π , π (let π«π = ππππππ, π«π = πππππ):
β If max π«π β₯ max π«π ,
π = ππππ(: , π1), π = πππππ
ππ/ π ππ (π1is the index of π«πβs largest entry)
β If max π«π < max(π«π),
π = πππ»ππ(π2, : ), π = πππππ / π π
π (π2is the index of π«πβs largest entryοΌ
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ππππ = ππ+1ΰ·©π1
ππππ = ππ+1ΰ·©π2
ππ = π―1, π―2, β¦ , π―π
πk = π°1, π°2, β¦ ,π°π
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Exampleβͺ Step 1:
β’π: Assume each node in G1 has at most one 1-to-1 anchor link to G2.
β’ππ = π = 0,0,0,1 π β 1,0,0,0 = ππππ; π(π).
βͺ Step 2: Choose Arnoldi vectors, π = 0,0,0,1 π, π = 1,0,0,0 π; π(ππ)
βͺ Step 3:
β’πΎπ ππ, π = π πππ{ 0,0.7071,0.7071,0 ,π 1,0,0,0 π}; π(ππ)
β’πΎπ ππ, π = π πππ{ 0,0.7071,0.7071,0 π , 0,0,0,1 π}; π(ππ)
2222
G2G10 1 1 0
1 0 1 1
1 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 1
0 1 1 0
0 0 0 0
0 0 0 0
0 0 0 0
1 0 0 0
G2G1
π A2A1
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Minimal Residual Method with Low-rank Representation
βͺ Step 1: Obtain and solve small scaled system L(π) = π.
βͺ Step 2: Implicit solution representation π = π, ππ€π , π = [π,ππ€π];
(Original updating: π β π + ππ€πππ€π)
βͺ Step 3: Let π³π = π½π+πΰ·©π1πΰ·©π2π, ππ = ππ€+π
π , ππ = ππ€π, ππ = ππ€π
Construct new residual ππ = ππ, ππ, ππ , ππ = πππ, ππ
π, πππ π
(Original updating: π β π β ππ€+πΰ·©πππΰ·©πππππ€+π
π + ππ€πππ€π)
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Complexity:
Time: π(π π + 2 π),Space: π(π + ππ)
Low-rank property: If π is
rank π, the rank of π is
upper-bounded by ππ‘ππ β π(ππ‘ππ: the iteration number)
π PQ
Represented as
π ππ
ππRepresented as
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FASTEN-P+βͺ Major steps:
βͺ Details:
β : ππ
can be computed as π‘ππππ(πππ ππ
πππ ππ);
β Overall Complexity: time: πΆ(ππ+ πππ); space: πΆ(π+ ππ);
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πΆ(ππ)Arnoldi
Process on ππ
and ππ to get
the orthogonal
basis πk, πk
πΆ(ππππ β ππ)Solve the new
linear system in
low dimensional
space πΏ π = π
πΆ(ππ)Choosing
Arnoldi
vectors π and π by ππ,
ππ
πΆ(πππ)Update π,
π and ππ, ππ
by π and check
stopping
condition
1 2 3 4
Till converge ( ππΉ< π)
4
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Roadmap
βͺMotivations
βͺBackground
βͺProposed Algorithms for plain graphs
βͺProposed Algorithms for attributed graphs
βͺExperimental Results
βͺConclusions
25
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Key Ideas
βͺ#1: Decomposition of Sylvester equation
βDecompose the equation to a inter-correlated Sylvester equation set
βEach decomposed equation is small-scaled & fast to solve
βͺ#2: Apply FASTEN-P(+) on decomposed equation
βApply Block Coordinate Descent (BCD) on the whole equation set
βEfficiently solve every single equation by FASTEN-P(+)
26
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βͺObservation:
β The solution matrix π has block-diagonal structure
β The equation can be decomposed to:
β π1ππ
is a block of π1 of rows from attribute π to columns of attribute π.
β Off-diagonal block: need not to be solved
β Diagonal block: apply Block Coordinate Descent (BCD)
πππ β
πͺ=π
π₯
πππππππ ππ
πππ= πππ
πππ = πππ (1 β€ π, π β€ π, π β π)
Decomposition of Sylvester Equation
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Diagonal block
variables
Off-diagonal
block variables
Solution matrix X
π βπ,π=π
π
πππππ(ππ
ππ)π = π
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Apply FASTEN-P(+) on Decomposed Equation
βͺObservation:
β When applying BCD: solve a non-attributed Sylvester equation each time
β e.g.: when solving π11, the equation becomes:
β Apply FASTEN-P(+) to solve the above equation.
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Diagonal block
variables
π11 β π211π11(π1
11)π = π11 +
πβ 1
π
π21ππππ(π1
1π)π = ΰ·ͺπππ
πππ β
πͺ=π
π₯
πππππππ ππ
πππ= πππ
π111 π2
11
ΰ·ͺπππ
G1β G2β
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π11 β π211π11 π1
11 π + π212π22 π1
12 π = π11
π22 β π221π11 π1
21 π + π222π22 π1
22 π = π22
π12 = π12
π21 = π21
βͺ In this example, the attributed Sylvester equation is decomposed to:
Example
29
π11 and
π22 are two
2 by 2
diagonal
blocks
G2G10 1 0 0
1 0 1 1
0 1 0 0
0 1 0 0
0 1 0 0
1 0 1 1
0 1 0 0
0 1 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 1 0
Solution matrix Xπ
e.g. Solve non-attributed Sylvester
equation on ππππ, ππ
ππ for π11:π β
π,π=π
π
πππππ(ππ
ππ)π = π
A2A1
Diagonal
Off-diagonal
1β
2β
3β 4β
ΰ·ͺπππ1
2
43
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FASTEN-N
βͺ Major steps:
βͺ Details:
β : ππ, ππ are the node attribute matrices of ππ and ππ.
β Overall Complexity: time: πΆ(ππ/π + πππ/π); space: πΆ(π/π + ππ);
30
Initialize
each
diagonal πππ
and the
residual π
πΆ(π)Construct
block
matrices π1ππ
,
π2ππ
, πππ by
ππ, ππ
πΆ(ππ/π)Iterate π times
to solve π block
variables by
BCD &
FASTEN-P
πΆ(πππ/π)Update π
and π; check
stopping
condition
1 2 3 4
2
π βπ,π=π
π
πππππ(ππ
ππ)π = π
Till converge ( ππΉ< π)
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From FASTEN-N to FASTEN-N+βͺ Major steps:
βͺ Details:
β Key idea: apply FASTEN-P+ instead of FASTEN-P in step ;
β Overall Complexity: time: πΆ(ππ+ ππππ); space: πΆ(π+ πππ);
31
πΆ(π)Construct
block
matrices π1ππ
,
π2ππ
, πππ by
ππ, ππ
πΆ(π)Initialize each
implicit
solution ππ,ππ
and the
residual ππ,ππ
πΆ(ππ)Iterate π times
to solve πblock variables
by BCD &
FASTEN-P+
πΆ(ππππ)Update
ππ,ππ and
ππ,ππ; check
stopping
condition
1 2 3 4
π βπ,π=π
π
πππππ(ππ
ππ)π = π
Till converge ( ππΉ< π)
3
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Roadmap
βͺMotivations
βͺBackground
βͺProposed Algorithms for plain graphs
βͺProposed Algorithms for attributed graphs
βͺExperimental Results
βͺConclusions
32
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Experimental Setup
βͺ Datasets Summary:
βͺ Baseline methods
β Conjugate Gradient method (CG) [Saad Y. SIAM 03]
β Fixed Point (FP) [Saad Y. SIAM 03]
β FINAL-P+ & FINAL-N+ [Zhang et al. KDDβ16]
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Dataset Name Category # of Nodes # of Edges
DBLP Co-authorship 9,143 16,338
Flickr User relationship 12,974 16,149
LastFm User relationship 15,436 32,638
Aminer Academic network 1,274,360 4,756,194
LinkedIn Social network 6,726,290 19,360,690
Exact methods
Approximated methods
Arizona State University
Experimental Result - Efficiency
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β’ Obs.: maximum speedup: > 10,000 times with 25K-node network.
β’ Better than approximated methods!
1. DBLP (9,143 nodes)
2. Flickr (12.974 nodes)
3. LastFm (15,436 nodes)
4. Aminer with 25K nodes
5. Aminer with 100K nodes
6. Aminer with 1.2M nodes
7. LinkedIn (6.7M nodes)
Our method
Arizona State University
Experimental Result - Efficiency
35
Our method
β’ Obs.: maximum speedup: > 10,700 times with 25K-node network.
β’ Better than approximated methods!
1. DBLP (9,143 nodes)
2. Flickr (12.974 nodes)
3. LastFm (15,436 nodes)
4. Aminer with 25K nodes
5. Aminer with 100K nodes
6. Aminer with 1.2M nodes
7. LinkedIn (6.7M nodes)
Arizona State University
Experimental Result - Scalability
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Our method
On plain graphs On attributed graphs
Our method
β’ Obs.: FASTEN-P/N scales almost in accord with FINAL-P+/N+
β’ FASTEN-P+/N+ scale linearly with regard to # of nodes (to over 1M)
Arizona State University
Experimental Result - Effectiveness
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On plain graphs On attributed graphs
0
Our method
β’ Obs.: FASTEN gives exact solution while having low running time.
Arizona State University
Parameter Sensitivity
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0
β’ Obs.: the running time of FASTEN-P stays stable in a range of [14,60].
β’ e.g.: FASTEN-P:
Arizona State University
Roadmap
βͺMotivations
βͺBackground
βͺProposed Algorithms for plain graphs
βͺProposed Algorithms for attributed graphs
βͺExperimental Results
βͺConclusions
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Arizona State University
Conclusions
βͺ Goal: Fast & exact solver for (attributed) Sylvester equation.
βͺ Solution: βFASTENβ family
β Key idea #1: Generate Kronecker Krylov subspace
β Key idea #2: Indirect solution representation
β Key idea #3: Decomposition of Sylvester equation
β Key idea #4: BCD & FASTEN-P(+) on decomposed equation
βͺ Results:
β Exact solution and linear scalability w.r.t the size of input graphs;
β Significant speedup against traditional methods.
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Arizona State University
Thank You!
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