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At the initial state, an agent stays on a chosen vertex (seed vertex).
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For a preliminary core C with den(C) < T d, the edge clustering coefficient ECC u, v) of each edge (u, v) connecting the seed vertex v and a rest vertex u is calculated.
Recently, random walk methods have gained great attention on this local graph clustering problem, since a walk started from the seed vertex is more likely to stay in the cluster where the seed vertex belongs.
Algorithm 1 illustrates the graph exploring from a seed vertex set Q. Note that for small graph data, we can set the seed vertex set (Q=V) (i.e. the whole graph).
Let s be the seed vertex.
So the seed vertex set (Q=V).
However, if r is too large, the probability values concentrate to the nearest attractor vertex (or the seed vertex itself) before the graph is sufficiently explored.
Starting from the seed vertex, a normal random walk procedure will eventually explore the whole graph.
Very often, people are only interested in finding the cluster for a given seed vertex.
Thus, the LRW procedure will find attractor vertices that the seed vertex is associated.
In [17 19], a random walk is first applied to find important vertices around the seed vertex.
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