def pagerank(graph, damping_factor=0.85, max_iterations=100, min_delta=1e-05):
    """
    Compute and return the PageRank in an directed graph.    
    
    @type  graph: digraph
    @param graph: Digraph.
    
    @type  damping_factor: number
    @param damping_factor: PageRank dumping factor.
    
    @type  max_iterations: number 
    @param max_iterations: Maximum number of iterations.
    
    @type  min_delta: number
    @param min_delta: Smallest variation required to have a new iteration.
    
    @rtype:  Dict
    @return: Dict containing all the nodes PageRank.
    """
    nodes = graph.nodes()
    graph_size = len(nodes)
    if graph_size == 0:
        return {}
    min_value = (1.0 - damping_factor) / graph_size
    pagerank = dict.fromkeys(nodes, 1.0 / graph_size)
    for i in range(max_iterations):
        diff = 0
        for node in nodes:
            rank = min_value
            for referring_page in graph.incidents(node):
                rank += damping_factor * pagerank[referring_page] / len(graph
                    .neighbors(referring_page))
            diff += abs(pagerank[node] - rank)
            pagerank[node] = rank
        if diff < min_delta:
            break
    return pagerank
