Biased random walk on a graph

In Network science, Biased Random Walks on graph provide an approach for the structural analysis of undirected graphs in order to extract their symmetries when the network is too complex or when it is not large enough to be analyzed by statistical methods. The concept of biased random walks on graph has attracted the attention of many researchers and data companies over the past decade especially in the transportation and social networks.

Definitions and Theory

There have been written many different representations of the Biased Random Walk model on graph based on the particular purpose of the analysis. A common representation of the mechanism for undirected graphs is as follows:

On a graph, a walker takes an step from the current node, $j$, to node $i$. Assuming that each node has an attribute $\alpha _{i}$, The probability of jumping from node$j$ to $i$ is given by:

$T_{ij}^{\alpha }={\tfrac {\alpha _{i}A_{ij}}{\sum _{k}\alpha _{k}A_{kj}}}$

In fact, the steps of the walker is biased by the factor of $\alpha$ which may differ from one node to another.

Where $A_{ij}$ represents the topological weight of the edge going from $j$ to $i$. Depending on the network, the attribute $\alpha$ can be interpreted differently. It might be implied as the attraction of a person in a social network, betweenness centrality or even it might be explained as an intrinsic characteristic of a node. It is obvious that in case of a fair random walk $\alpha$ is one for all the nodes.
In case of shortest paths random walks $\alpha _{i}$ is the total number of the shortest paths between all pairs of nodes that pass through the node $i$. In fact the walker prefers the nodes with higher betweenness centrality as below:

$C(i)={\tfrac {\text{Total number of shortest paths through i}}{\text{Total number of shortest paths}}}$

Based on the above equation, the recurrence time to a node in the biased walk is given by

$r_{i}={\tfrac {1}{C(i)}}$

Applications

Variety of applications by using biased random walks on graphs have been developed; control of diffusion, advertisement of products on social networks, explaining dispersal and population redistribution of animals and micro-organisms, community detections, wireless networks, Search engines and so on.

See also

• Social network analysis
• Community structure
• Random walk closeness centrality
• Betweenness centrality
• Travelling salesman problem
• Kullback–Leibler divergence
• Markov chain

External links

• Graph entropy
• Evaluating the Quality of a Network Topology through Random Walks

References

Category:Network theory Category:Social networks Category:Social systems Category:Social information processing