liam roditty reachability in directed graphs. connectivity in undirected graphs given two vertices...
Post on 21-Dec-2015
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Connectivity in undirected graphsConnectivity in undirected graphs• Given two vertices decide whether they
are in the same component.
Reachability• Is there a directed
path from u to v
Strong Connectivity• Is there a directed
path from u to v
and from v to u
G = (V,E)
u
v
ṽũG = (V,E)
u
v
ṽũ
G = (V,E)
u
v
ṽũ
TotalTotalupdate update
timetime
Query Query timetime
1
mn I’ 88
m
t
m2 PvL87, FMNZ01
DI’ 00
BHS’ 00
HK’ 95
n3
mn4/3
n/log n
RZ’ 02 Just for DAGs
Decremental ReachabilityDecremental Reachability
Small Query Time:
Maintain explicitly the transitive closure matrix.
Query is done in O(1), each update may take (n2)
time.
Fully dynamic reachability
Small Query Time:
Maintain explicitly the transitive closure matrix.
Query is done in O(1), each update may take (n2)
time.
Fully dynamic reachability
Small Update Time: A data structure which may have a non-constant query time but with smaller update time.
Open problem:
Is it possible to break the (n2) update barrier ?
Yes: Updates in O(m+n log n) and queries in O(n)
Update Update timetime
Query Query timetime
n2
n
1
DI’ 00R’ 03S’04
n
n n
n2 n RZ’ 03
m n
n1.575
n0.575 n
m+n logn
Today
General
graphs
mn t
DI’ 00
DAG
m
t
Fully Dynamic ReachabilityFully Dynamic Reachability
An An overviewoverview of the of the algorithmalgorithmOur algorithm supports the following operations:
Insert(Ew) – Insert the edges Ew all touching w into the graph
Delete(E’ ) – Delete all edges of E’ from the graph
Query(u,v) – Check whether there is a directed path from u to v
G(V,E0)
Insert(Ev)
G(V,E0Ev)
Insert(Eu)
G(V,E0Ev Eu)
An An overviewoverview of the of the algorithmalgorithm
G(V,E0)
Insert(Ev)
G(V,E0Ev)
Insert(Eu)
G(V,E0Ev Eu)u
vx y
v
x
y
Insert( Insert( EEw w )) :: Create Create twotwo trees to capture new paths trees to capture new pathsQuery(u ,v ):Query(u ,v ): w, u w, u T Tinin(w) (w) v v T Toutout(w)(w)
Delete( E’ ): Delete( E’ ): Delete Delete E’E’ from all trees from all trees
Our algorithm works as follows:
…
Insert(Ev1)
v1
Insert(Ev2) Insert(Ev3
)
v2 v3 vl
Insert(Evl)
An An overviewoverview of the algorithm of the algorithm
The main problem is to delete edges efficiently from this forest.
In(vIn(v11))
Out(vOut(v11))
…v1 v2 v3 vl
A simple solution for DAGsA simple solution for DAGs
The total time required to maintain a decremental single source reachability (DSSR) tree in directed acyclic graph is only O(m) (Itliano ’88) Each edge is scanned only once in each tree
Insert( Insert( EEw w ))Query(u ,v)Query(u ,v)
Delete( E’ )Delete( E’ )
O (n )
O (1 )
O (m+n)
•Every edge is examined only once! (If the graph is DAG)
•Total complexity is O(m).
Decremental reachability treeTin(v)
Tout(v)
v
v
…v1 v2 v3 vl
The problem with general graphsThe problem with general graphs
There is not any obvious way to generalize Itliano’s algorithm to general graphs. The best algorithm for DSSR requires O(mn)
time.
Insert( Insert( EEw w ))Query(u ,v)Query(u ,v)
Delete( E’ )Delete( E’ )
O (n )
O (1 )
O (mn)O (n )
O (m+n log n)
O (m+n log n)
…v1 v2 v3 vl
Our SolutionOur Solution
Maintain the trees with respect to Maintain the trees with respect to Strongly Connected ComponentsStrongly Connected Components
Problem 1Problem 1: We may have n : We may have n trees each with different trees each with different components!components!
Problem 2Problem 2: When a component is : When a component is decomposed we have to update decomposed we have to update the edges such that an edge is the edges such that an edge is never examine twice!never examine twice!
We solve Problem 1 using fully dynamic strong connectivity algorithm with update time of O(m(m,n)).
v1
In(vIn(v11))
Out(vOut(v11))
v2
In(vIn(v22))
Out(vOut(v22))
v4
In(vIn(v44))
Out(vOut(v44))
v5
In(vIn(v55))
Out(vOut(v55))
v3
In(vIn(v33))
Out(vOut(v33))
Fully dynamic strong connectivity with “persistency”
Detect and report on decompositions
Updating edge lists
Updating edge lists
Supported operations:
G0=(V,E0)
Insert(E’)
Fully dynamic strong connectivity
G0=(V,E0) G1=(V,E0UE’)G1=(V,E1)
Insert(E’’)
G1=(V,E1) G2=(V,E1UE’’)G2=(V,E2)
Delete( E’ ) – Delete the set E’ from all versions.
Query(u,v,i) – Are u and v in the same component in Gi
Insert( E’ ) – Create a new version and add E’ to it.
Note that these operations create a graph sequence G0(V,E0), G1(V,E1), … , Gt(V,Et) where E0 E1 … Et .This containment is kept during all the update operations!
O (m(m,n))O (m(m,n))O (1
)
Fully dynamic strong connectivityThe components of all the graphs in the sequence are arranged in a hierarchy and can be represented as a forest of size O (n )
G0 G1 G2 G3
The components: The forest:G0 G1 G2 G3
1
1
1
2
3
u
v
Query(u,v,1) Version( LCA(u,v) ) 1Query(u,v,2) Version( LCA(u,v) ) 2
Version tag
Hi+1Hi
A new partitioning of the edges E1,…, Et
Definition 1: A partitioning of the graph sequence edges
Hi = { (u,v) Ei | Query(u,v,i) (Query(u,v,i-1)(u,v)Ei-1) }
Ht+1 = Et \ U Hii=1
t
Gi-1=(V,Ei-1) Gi=(V,Ei) Gi+1=(V,Ei+1)
Hi Hj = ø,Et = U Hii=1
t+1U
G0 G1 G2 G3
FindScc(H1,1) FindScc(H2,2)Shift(H1,H2) Shift(H2,H3)
FindScc(H3,3)Shift(H3,H4)
Cost Analysis: Note that an edge enters and leave Hi just once Moving edges – Paid by the creation of the version they enter. FreeFixed edges – Paid by the current delete operation O(m)
Processing a deletion
Hi edges
Before…After !
G0 G1 G2 G3
FindScc(H1,1) FindScc(H2,2)Shift(H1,H2) Shift(H2,H3)
FindScc(H3,3)Shift(H3,H4)
Cost Analysis: Note that an edge enters and leave Hi just once Moving edges – Paid by the creation of the version they enter. FreeFixed edges – Paid by the current delete operation O(m)
Processing a deletion
Hi edges
Before…After !
We solve Problem 1 using fully dynamic strong connectivity algorithm with update time of O(m(m,n)).
v1
In(vIn(v11))
Out(vOut(v11))
v2
In(vIn(v22))
Out(vOut(v22))
v4
In(vIn(v44))
Out(vOut(v44))
v5
In(vIn(v55))
Out(vOut(v55))
v3
In(vIn(v33))
Out(vOut(v33))
Fully dynamic strong connectivity with “persistency”
Detect and report on decompositions
Updating edge lists
Updating edge lists
Decremental reachability tree
When a decomposition is reported (by the strong connectivity algorithm), we need to:• Build the data for the new components• Connect the new components to the tree
O(n log n)O(m)
• The list in[v] contains only uninspected incoming edges. • The list out[v] contains all the outgoing edge.• A vertex v is active if in[v] is not empty
Decremental reachability tree Data structures
Every component maintains a list of its active vertices
v
in[v]
component
Tree invariant: The first edge of the first vertex in the component active vertices list is the edge that connects the component to the tree. If the component is not connected then its active vertices list is empty
component
- An active vertex- An uninspected incoming edge- A tree edge
- A none active vertex
Deletions of non tree edges
- A deleted edge
Deletions of a tree edge – we search for an edge (u,v) such that the component that contains u satisfies the tree invariant
Decremental reachability tree Edge deletion
Decremental reachability tree Disconnecting a component
component
- An active vertex- An uninspected incoming edge- A tree edge
- A none active vertex- A deleted edge
Disconnecting a component.
How do we find the components vertices ?
We generalized the decremented reachability tree to general graphs by using the components of the graph.
Each edge in the connection process is still scanned only once.
We have to deal with decomposition.
We like to create active lists for new components in efficient way.
Decremental reachability tree
Decremental reachability tree Decomposition
component
Component2
Size = 5
Component1
Size =2
•The largest components among the new components inherits the list of the decomposed component.
•Using the component forest each vertex from a small component is removed from the list and added to its new component list.
The component decomposed to two new components one of size 5 and one of size 2.
Decremental reachability tree Analysis
Component2
Size = 5
Component1
Size =2
Scanning the component vertices can be done in time proportional to the component size. We only scan ‘small’ components.
Let’s analyze the total cost:
If a vertex is moved from one active list to another, the size of the component containing it must have decreased by a factor of at least 2.
Summing up
• The total connection cost is O(m)
• The total decomposition cost is O(n log n)
For each tree
Each update costs O(m + n log n)