A New Outlier Detection Model Using Random Walk on Local Information Graph

Outrank: a Graph-Based Outlier Detection Framework Using Random Walk

This paper introduces a stochastic graph-based algorithm, called OutRank, for detecting outliers in data. We consider two approaches for constructing a graph representation of the data, based on the object similarity and number of shared neighbors between objects. The heart of this approach is the Markov chain model that is built upon this graph, which assigns an outlier score to each object. U...

Outlier Detection using Graph Mining

Fast Random Walk Graph Kernel

Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it suffers from the expensive computational cost which is at least O(n) or O(m) for graphs with n nodes and m edges. In this paper, we propose Ark, a set of fast algorithms for random walk graph kernel computation. Ark is...

Link prediction based on local random walk

The problem of missing link prediction in complex networks has attracted much attention recently. Two difficulties in link prediction are the sparsity and huge size of the target networks. Therefore, to design an efficient and effective method is of both theoretical interest and practical significance. In this letter, we proposed a method based on local random walk, which can give competitively...

A New Random Walk for Replica Detection in WSNs

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problema...