here is a working example ``` import graph_tool.all as gt import multiprocessing as mp g = gt.collection.data["celegansneural"] pool = mp.Pool(5) def fit_sbm(i): state = gt.minimize_blockmodel_dl(g) b = state.get_blocks() print(i) return(b) blocks = pool.map(fit_sbm, range(5)) pool.close #print(blocks) #b0 = blocks[0] #print(b0) b0 = blocks[0] g.vertex_properties["membership"] = b0 ``` i wasn't aware of the own_property() function and can't find it in the graph-tool documentation (other than seeing it being used in examples involving visualization). whatever it does, it seems to work, and the modified code below seems to work ``` import graph_tool.all as gt import multiprocessing as mp g = gt.collection.data["celegansneural"] pool = mp.Pool(5) def fit_sbm(i): state = gt.minimize_blockmodel_dl(g) b = state.get_blocks() print(i) return(b) blocks = pool.map(fit_sbm, range(5)) pool.close #print(blocks) #b0 = blocks[0] #print(b0) b0 = blocks[0] g.vertex_properties["membership"] = g.own_property(b0) ```