LASCNN Algorithm
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In graph theory, LASCNN is a Localized Algorithm for Segregation of Critical/Non-critical Nodes The algorithm works on the principle of distinguishing between critical and non-critical nodes for network connectivity based on limited topology information. The algorithm finds the critical nodes with partial information within a few hops. This algorithm can distinguish the critical nodes of the network with high precision, indeed, accuracy can reach 100% when identifying non-critical nodes. The performance of LASCNN is scalable and quite competitive compared to other schemes.


Pseudocode

The LASCNN algorithm establishes a -hop neighbor list and a duplicate free pair wise connection list based on {{Var, k-hop information. If the neighbors stay connected then the node is non-critical. 
Function LASCNN(MAHSN)
    For ∀ A ∈ MAHSN
        If (A->ConnList.getSize() 
 1) then
            A->SetNonCritical() = LEAF
        Else
            Continue = TRUE
            While (Continue 
 TRUE)
                Continue = FALSE
                For ∀ ActiveConn ∈ ConnList
                    If (A∉ActiveConn) then
                        If (A->ConnNeighbors.getSize() 
 0)
                            A->ConnNeighbors.add(ActiveConn)
                            Continue = TRUE
                        else
                            If (ActiveConn ∩ ConnNeighbors 
 TRUE)
                                ActiveConn ∪ ConnNeighbors
                                Continue = TRUE
                            Endif
                        Endif
                    Endif
                End For
            End While
        Endif
        If (A->ConnNeighbors.getSize() < A->Neighbors.getSize())
            A->SetCritical() = TRUE
        else
            A->SetNonCritical() = INTERMEDIATE
        Endif
    End For
End Function


Implementation

The Critical Nodes application is a Free Open-Source implementation for the LASCNN algorithm. The application was developed in 2013 using Programming Without Coding Technology software.Fayed, Al-Qurishi, Alamri, Aldariseh (2017) PWCT: visual language for IoT and cloud computing applications and systems, ACM


See also

*
Connectivity (graph theory) In mathematics and computer science, connectivity is one of the basic concepts of graph theory: it asks for the minimum number of elements (nodes or edges) that need to be removed to separate the remaining nodes into two or more isolated subgra ...
*
Dynamic connectivity In computing and graph theory, a dynamic connectivity structure is a data structure that dynamically maintains information about the connected components of a graph. The set ''V'' of vertices of the graph is fixed, but the set ''E'' of edges can ch ...
*
Strength of a graph In the branch of mathematics called graph theory, the strength of an undirected graph corresponds to the minimum ratio ''edges removed''/''components created'' in a decomposition of the graph in question. It is a method to compute partitions of t ...
*
Cheeger constant (graph theory) In mathematics, the Cheeger constant (also Cheeger number or isoperimetric number) of a graph is a numerical measure of whether or not a graph has a "bottleneck". The Cheeger constant as a measure of "bottleneckedness" is of great interest in man ...
*
Critical point (network science) In network science, a critical point is a value of average degree, which separates random networks that have a giant component from those that do not (i.e. it separates a network in a subcritical regime from one in a supercritical regime). Conside ...
*
Depth-first search Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible alon ...
*
Breadth-first search Breadth-first search (BFS) is an algorithm for searching a tree data structure for a node that satisfies a given property. It starts at the tree root and explores all nodes at the present depth prior to moving on to the nodes at the next de ...


References


External links


Critical Nodes application
Networks Network theory Graph algorithms