Add Kleinscheiß.

    return G

def root_hubs(graph, edge_freq, min_neighbors=5, theshold=0.8):

def root_hubs(graph, edge_freq, min_neighbors=4, theshold=0.8):

    G = deepcopy(graph)

    V = sorted(G.nodes, key=lambda key: G.degree[key], reverse=True) # -1 to sort descending (...3 -> 2 -> 1...)

    T = mst

    H = hubs

    C = [c.lower().strip() for c in contexts]

    backup_cluster = len(H)

    result = []

        T.nodes[v]['s'] = np.array([s if s == max(scores) else 0 for s in scores])

    for c in contexts:

    for c in C:

        c = c.lower()

        toks = [t.text for t in nlp(c)]

        vector = np.sum([T.nodes[t]['s'] if t in T.nodes else np.zeros(len(H)) for t in toks], axis=0)

        idx = contexts.index(c) + 1

        idx = C.index(c) + 1

        if len(vector) == 0: #if no senses are found -> all in one

            result.append((0, idx))

    data_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/WSI-Evaluator/datasets/MORESQUE/'

    #corpus_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/test'

    corpus_path = '/proj/absinth/wikipedia.txt.dump.20140615-en.SZTAKI/'

    results_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/results/'

    results_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/clustering/'

    stop = set(stopwords.words('english') + ['utc', 'new', 'other'])

        target = value.strip()

        print("[A] Processing '"+target+"'.\n")

        if target[:4] == 'the_' and target.count('_') >= 2: #hard coded 'the'-protection

            target = target[4:]

        f = open(results_path+target+'.absinth', 'w')

        f.write('subTopicID\tresultID\n')