In this blog we will discuss the computation of dense sub-graph (community) of sparse graph, which is very common in our real life complex network. We will give introduction about different way of computing sub-graph and finally will spread some light on random walk approach.

Complex network has become important in
different domain such as sociology (acquaintance or collaboration networks),
biology (metabolic networks, gene networks) or computer science (Internet
topology, Web graph, P2P networks). The Graphs of these networks are general
globally sparse but locally dense. There exist group of vertices which are
highly connected among itself, called communities, but few links to other. This
type of structure carries much information about network. One real life example
could be our Facebook friends if we draw our friends as node and link between
them if they are friend among themselves then we will find a sparse graph
having different dense sub graph(community) based on college friend , High
school friend , School friend, colleague etc.

Community Computation problem can be
related to graph partition problem [1]in which given a graph

*G*= (*V*,*E*) with*V*vertices and*E*edges, k-way partition of graph is partitioning of graph in k part such that number of edges running over these partitions are minimum. However the algorithm for Graph partition is not well suited for our case, because no of communities and their size is input for Community Computation.
Hierarchical clustering is another
classical approach in which nodes are grouped together based on the similarity
between nodes, Newman proposed [2] a greedy algorithm that starts with n
communities corresponding to the vertices and merges communities in order to
optimize a function called modularity which measures the quality of a
partition.

The approach which is discussed here is
based on the random walks on a graph [3] tend to get “trapped” into densely
connected parts corresponding to communities. The graph G has its adjacency
matrix A: A

_{ij }= 1 if vertex i and j are connected and A_{ij }= 0 otherwise. The degree d(i)_{ }of the vertex i is the number of its neighbors. A discrete random walk is a process in which a walker is on a vertex and moves to vertex chosen randomly and uniformly among its neighbors. The sequence of visited vertices is a*Mrakov chain*, the states of which are the vertices of the graph. At each step, the transition probability from vertex i to vertex j is P_{ij}=A_{ij}/d(i). This defines the transition matrix P of random walk. The probability of going from i to j through a random walk of length t is (P^{t})_{ij. }It satisfies following two properties of random walk.*Property 1:*When length of a path between two vertices i and j tends toward infinity. The probability that a random walker will be at vertex j will totally depend on the degree of vertex j

*Property 2:*The ratio of probabilities of going from i to j and j to i through a random walk of a fixed length t depend on the degrees d(i) and d(j).

For grouping
vertices into communities, we will now introduce a distance r between the
vertices that captures the community structure of the graph. This distance will
be large if two vertices are in different communities, and on contrary if the
distance is small they will be in same community. It will be computed from the
information given by the random walk of the graph.

Let us consider a random walk of length t
from vertex i to j . t is large enough to gather the topology of graph.
However t must not be too long to avoid

*property 1.*The probability P^{t}_{ij}would depend on the degree of the vertices i and j. Each P^{t}_{ij }gives some information about the two vertices i and j, but*property 2*says that P^{t}_{ij }and P^{t}_{ji}encode the same information. Finally the information about vertex i is encode inside n probabilities P^{t}_{ik}1<k<n. Which is nothing but the i^{th}row of matrix P^{t}. For comparing two vertices we must notice following points.
·
If
two vertices i and j are in the same community, the probability P

^{t}_{ij }will be high be high but if P^{t}_{ij}is high it does not necessarily imply that i and j will be in same community.
·
High degree vertex will have always high
probability that a random walker will reach there. So P

^{t}_{ij }is influenced by the degree d(j) of vetex.
·
Two
vertices of a same community observe all the other vertices in same way. Thus
if i and j are in same community
then

References

[1]Graph
Partition Problem

[2]Newman,
M.E.J.: Fast algorithm for detecting community structure in networks. Physical
Review E 69 (2004) 066133

[3]Computing
Communities in Large Networks Using Random Walks - Pascal Pons and
Matthieu Latapy

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