  Given a complex biological or social network, how many clusters should it be
decomposed into? We define the distance $d_{i,j}$ from node $i$ to node $j$ as
the average number of steps a Brownian particle takes to reach $j$ from $i$.
Node $j$ is a global attractor of $i$ if $d_{i,j}\leq d_{i,k}$ for any $k$ of
the graph; it is a local attractor of $i$, if $j\in E_i$ (the set of
nearest-neighbors of $i$) and $d_{i,j}\leq d_{i,l}$ for any $l\in E_i$. Based
on the intuition that each node should have a high probability to be in the
same community as its global (local) attractor on the global (local) scale, we
present a simple method to uncover a network's community structure. This method
is applied to several real networks and some discussion on its possible
extensions is made.
