C++ graphchi_vertex::outedge方法代码示例

	void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
		
		if(gcontext.iteration == 0){	

			VertexDataType vertexdata = vertex.get_data();
			if(!vertexdata.confirmed || !vertexdata.reconfirmed)
				return ;	
			assert(vertex.num_inedges() * vertex.num_outedges() <= product);
			for(int i=0; i<vertex.num_outedges(); i++){
				bidirectional_label edgedata = vertex.outedge(i)->get_data();
				if(edgedata.is_equal()){		
					/*
					   if(edgedata.smaller_one != 0)
					   std::cout<<edgedata.smaller_one<<" \t"<<edgedata.larger_one<<"\t root="<<root<<std::endl;
					   */
					if(root == edgedata.my_label(vertex.id(), vertex.outedge(i)->vertexid)){
						lock.lock();
						fprintf(fpout, "%u\t%u\n", vertex.id(), vertex.outedge(i)->vertexid);
						lock.unlock();
						continue;
					}
				}
				/*
				   lock.lock();
				   fprintf(fpout1, "%u\t%u\n", vertex.id(), vertex.outedge(i)->vertexid);
				   lock.unlock();
				   */
			}
		}
	}

 /**
  *  Vertex update function.
  */
 void update(graphchi_vertex<VertexDataType, EdgeDataType > &vertex, graphchi_context &gcontext) {
     if (gcontext.iteration == 0) {
         for(int i=0; i < vertex.num_outedges(); i++) {
             chivector<vid_t> * evector = vertex.outedge(i)->get_vector();
             evector->clear();
             assert(evector->size() == 0);
             
             evector->add(vertex.id());
             assert(evector->size() == 1);
             assert(evector->get(0) == vertex.id());
         }
         
     } else {
         for(int i=0; i < vertex.num_inedges(); i++) {
             graphchi_edge<EdgeDataType> * edge = vertex.inedge(i);
             chivector<vid_t> * evector = edge->get_vector();
             assert(evector->size() >= gcontext.iteration);
             for(int j=0; j < evector->size(); j++) {
                 vid_t expected = edge->vertex_id() + j;
                 vid_t has = evector->get(j);
                 if (has != expected) {
                     std::cout << "Mismatch: " << has << " != " << expected << std::endl;
                 }
                 assert(has == expected);
             }
         }
         for(int i=0; i < vertex.num_outedges(); i++) {
             vertex.outedge(i)->get_vector()->add(vertex.id() + gcontext.iteration);
         }
     }
     vertex.set_data(gcontext.iteration + 1);
 }

    /**
     *  Vertex update function.
     */
    void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {        
        if (first_iteration) {
            vertex.set_data(SCCinfo(vertex.id()));
        }
        
        if (vertex.get_data().confirmed) {
            return;
        }   
        
        /* Vertices with only in or out edges cannot be part of a SCC (Trimming) */
        if (vertex.num_inedges() == 0 || vertex.num_outedges() == 0) {
            if (vertex.num_edges() > 0) {
                // TODO: check this logic!
                vertex.set_data(SCCinfo(vertex.id()));
            }
            vertex.remove_alledges();
            return;
        }
        remainingvertices = true;

        VertexDataType vertexdata = vertex.get_data();
        bool propagate = false;
        if (gcontext.iteration == 0) {
            vertexdata = vertex.id();
            propagate = true;
            /* Clean up in-edges. This would be nicer in the messaging abstraction... */
            for(int i=0; i < vertex.num_inedges(); i++) {
                bidirectional_label edgedata = vertex.inedge(i)->get_data();
                edgedata.my_label(vertex.id(), vertex.inedge(i)->vertexid) = vertex.id();
                vertex.inedge(i)->set_data(edgedata);
            }
        } else {
            
            /* Loop over in-edges and choose minimum color */
            vid_t minid = vertexdata.color;
            for(int i=0; i < vertex.num_inedges(); i++) {
                minid = std::min(minid, vertex.inedge(i)->get_data().neighbor_label(vertex.id(), vertex.inedge(i)->vertexid));
            }
            
            if (minid != vertexdata.color) {
                vertexdata.color = minid;
                propagate = true;
            }            
        }
        vertex.set_data(vertexdata);
        
        if (propagate) {
            for(int i=0; i < vertex.num_outedges(); i++) {
                bidirectional_label edgedata = vertex.outedge(i)->get_data();
                edgedata.my_label(vertex.id(), vertex.outedge(i)->vertexid) = vertexdata.color;
                vertex.outedge(i)->set_data(edgedata);
                gcontext.scheduler->add_task(vertex.outedge(i)->vertexid, true);
            }
        }
    }

 /**
  *  Vertex update function.
  */
 void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
     
     if (vertex.get_data().confirmed) {
         return;
     }
     
     VertexDataType vertexdata = vertex.get_data();
     bool propagate = false;
     if (gcontext.iteration == 0) {
         /* "Leader" of the SCC */
         if (vertexdata.color == vertex.id()) {
             propagate = true;
             vertex.remove_alloutedges();
         }
         
     } else {
         
         /* Loop over in-edges and see if there is a match */
         bool match = false;
         for(int i=0; i < vertex.num_outedges(); i++) {
             if (!vertex.outedge(i)->get_data().deleted()) {
                 if (vertex.outedge(i)->get_data().neighbor_label(vertex.id(), vertex.outedge(i)->vertexid) == vertexdata.color) {
                     match = true;
                     
                     break;
                 }
             }
         }
         if (match) {
             propagate = true;
             vertex.remove_alloutedges();
             vertex.set_data(SCCinfo(vertexdata.color, true));
         } else {
             vertex.set_data(SCCinfo(vertex.id(), false));
         }
     }
     
     
     if (propagate) {
         for(int i=0; i < vertex.num_inedges(); i++) {
             bidirectional_label edgedata = vertex.inedge(i)->get_data();
             if (!edgedata.deleted()) {
                 edgedata.my_label(vertex.id(), vertex.inedge(i)->vertexid) = vertexdata.color;
                 vertex.inedge(i)->set_data(edgedata);
                 gcontext.scheduler->add_task(vertex.inedge(i)->vertexid, true);
             }
         }
     }
 }

	void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
		
		assert(vertex.num_inedges() * vertex.num_outedges() <= product);

		for(int i=0; i<vertex.num_outedges(); i++){
			bidirectional_label edgedata = vertex.outedge(i)->get_data();
			if(edgedata.is_equal()){		
				if(root == edgedata.my_label(vertex.id(), vertex.outedge(i)->vertexid)){
					lock.lock();
						fprintf(fpout, "%u\t%u\n", vertex.id(), vertex.outedge(i)->vertexid);
					lock.unlock();
					continue;
				}
			}
			lock.lock();
			fprintf(fpout1, "%u\t%u\n", vertex.id(), vertex.outedge(i)->vertexid);
			lock.unlock();
		}
	}

	void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
		if (gcontext.iteration == 0){
				//Vertexinfo vdata = vertex.get_data();  
				//id_th = vertex.id();	
				vid_t row = vertex.id() / nshards; 
				//vid_t new_id = row * nshards + prefix_sum[vertex.id() % nshards];	
				vid_t new_id = row + prefix_sum[vertex.id() % nshards];	
				vertex.set_data(new_id);
				//source vertex in each CC
				//vdata.level = 0;
				//vertex.set_data(vdata);	
				//vid_t new_id = getNewId(vdata.ccid, vdata.level);
				for(int i=0; i<vertex.num_edges(); i++){
					bidirectional_label edata = vertex.edge(i)->get_data();
					edata.my_label(vertex.id(), vertex.edge(i)->vertex_id()) = new_id;
					vertex.edge(i)->set_data(edata);
				}
				lock.lock();
					fprintf(vfout, "%u\t%u\n", new_id, vertex.id());
				lock.unlock();
		}else{
			for(int i=0; i<vertex.num_outedges(); i++){
				bidirectional_label edata = vertex.outedge(i)->get_data();	
				vid_t my_id = edata.my_label(vertex.id(), vertex.outedge(i)->vertex_id());		
				vid_t nb_id = edata.neighbor_label(vertex.id(), vertex.outedge(i)->vertex_id());
				if(my_id == nb_id){
					std::cout<<"my_id="<<vertex.id()<<"\tmy_label="<<my_id <<"\tnb_label="<<nb_id
							<<"\tnb_vid="<<vertex.outedge(i)->vertex_id()<<std::endl;
					assert(my_id != nb_id);
				}
				if(!flag_weight){
					lock.lock();
					fprintf(efout, "%u\t%u\n", my_id, nb_id);
					lock.unlock();
				}else{
					lock.lock();
					fprintf(efout, "%u\t%u\t%.3f\n", my_id, nb_id, edata.weight);
					lock.unlock();
				}
			}			
		}
	}    

 /**
   * Pagerank update function.
   */
 void update(graphchi_vertex<VertexDataType, EdgeDataType> &v, graphchi_context &ginfo) {
     float sum=0;
     if (ginfo.iteration == 0) {
         /* On first iteration, initialize vertex and out-edges. 
            The initialization is important,
            because on every run, GraphChi will modify the data in the edges on disk. 
          */
         for(int i=0; i < v.num_outedges(); i++) {
             graphchi_edge<float> * edge = v.outedge(i);
             edge->set_data(1.0 / v.num_outedges());
         }
         v.set_data(RANDOMRESETPROB); 
     } else {
         /* Compute the sum of neighbors' weighted pageranks by
            reading from the in-edges. */
         for(int i=0; i < v.num_inedges(); i++) {
             float val = v.inedge(i)->get_data();
             sum += val;                    
         }
         
         /* Compute my pagerank */
         float pagerank = RANDOMRESETPROB + (1 - RANDOMRESETPROB) * sum;
         
         /* Write my pagerank divided by the number of out-edges to
            each of my out-edges. */
         if (v.num_outedges() > 0) {
             float pagerankcont = pagerank / v.num_outedges();
             for(int i=0; i < v.num_outedges(); i++) {
                 graphchi_edge<float> * edge = v.outedge(i);
                 edge->set_data(pagerankcont);
             }
         }
             
         /* Keep track of the progression of the computation.
            GraphChi engine writes a file filename.deltalog. */
         ginfo.log_change(std::abs(pagerank - v.get_data()));
         
         /* Set my new pagerank as the vertex value */
         v.set_data(pagerank); 
     }
 }

    /**
      * Update the weigthed edge chivector
      * We first obtain the edge weight from the first element, sum them, then update the 
      * second item by eacg edge's weight
      */
    void update_edge_data(graphchi_vertex<VertexDataType, EdgeDataType> &v, float quota, bool first){
	float sum = 0.0;
	//if(first)
            for(int i=0; i < v.num_outedges(); i++) {
                graphchi_edge<EdgeDataType> * edge = v.outedge(i);
                if (edge != NULL) {
                    chivector<float> * evector = edge->get_vector();
		    //std::cout << evector->size() << std::endl;
		    /*if (first)
                        assert(evector->size() == 1);
		    else
                        assert(evector->size() == 2);
                    assert(evector->size() == 2);*/
    	            std::cout <<  v.id() << " with data: " << evector->get(0) << std::endl;
	            sum += evector->get(0);
		    /*if (first){
                        evector->add(sum);
                	assert(evector->size() == 2);
		    }*/
	        }
            }
	
        for(int i=0; i < v.num_outedges(); i++) {
            graphchi_edge<EdgeDataType> * edge = v.outedge(i);
            if (edge != NULL) {
                chivector<float> * evector = edge->get_vector();
//                assert(evector->size() == 2);
		float val = quota * evector->get(0) / sum;
		//evector->set(1, val);
		if(first && (evector->size() == 1))
                        evector->add(val);	
		evector->set(1, val);
    		//std::cout <<  v.id() << " with data: " << evector->get(0) << std::endl;
	    }
         }
    }

    /**
      * Update the weigthed edge chivector
      * We first obtain the edge weight from the first element, sum them, then update the 
      * second item by eacg edge's weight
      */
    void update_edge_data(graphchi_vertex<VertexDataType, EdgeDataType> &v, float quota){
	float sum = 0.0;
        for(int i=0; i < v.num_outedges(); i++) {
            graphchi_edge<EdgeDataType> * edge = v.outedge(i);
	    //We store the weight value to the edge->weight field and then sum them
	    /*if(first)
	        edge->set_weight(edge->get_data());*/
	    struct weightE eData = edge->get_data();
	    //sum += eData.weight;
	    sum ++;
	    //if(!first)
    	    //   std::cout <<  v.id() << " with data: " << edge->get_data() << " with weight " << edge->get_weight() << std::endl;
        }
	
        for(int i=0; i < v.num_outedges(); i++) {
            graphchi_edge<EdgeDataType> * edge = v.outedge(i);
	    struct weightE eData = edge->get_data();
	    //eData.pagerank = quota * eData.weight / sum;
	    eData.pagerank = quota * 1.0 / sum;
	    edge->set_data(eData);
	    if (v.id() == 3845)
    	        std::cout <<  v.id() << " -> " << edge->vertex_id() << " with data: " << eData.pagerank << " with weight " << eData.weight << std::endl;
         }
    }

  /** The actual LambdaRank implementation. */
  virtual void compute_gradients(
      graphchi_vertex<TypeVertex, FeatureEdge> &query, Gradient* umodel) {
    std::vector<double> lambdas(query.num_outedges());
    std::vector<double> s_is(query.num_outedges());

    /* First, we compute all the outputs... */
    for (int i = 0; i < query.num_outedges(); i++) {
      s_is[i] = get_score(query.outedge(i));
//      std::cout << "s[" << i << "] == " << s_is[i] << std::endl;
    }
    /* ...and the retrieval measure scores. */
    opt.compute(query);


    /* Now, we compute the errors (lambdas). */
    for (int i = 0; i < query.num_outedges() - 1; i++) {
      int rel_i = get_relevance(query.outedge(i));
      for (int j = i + 1; j < query.num_outedges(); j++) {
        int rel_j = get_relevance(query.outedge(j));
        if (rel_i != rel_j) {
          double S_ij = rel_i > rel_j ? 1 : -1;
          double lambda_ij = dC_per_ds_i(S_ij, s_is[i], s_is[j]) *
                             fabs(opt.delta(query, i, j));
          /* lambda_ij = -lambda_ji */
          lambdas[i] += lambda_ij;
          lambdas[j] -= lambda_ij;
        }
      }
    }

    /* Finally, the model update. */
    for (int i = 0; i < query.num_outedges(); i++) {
      // -lambdas[i], as C is a utility function in this case
      umodel->update(query.outedge(i)->get_vector()->get_data(), s_is[i], lambdas[i]);
    }
  }

  /** Scores all documents for the query. The first step in update(). */
  void score_documents(graphchi_vertex<TypeVertex, FeatureEdge> &query,
                       graphchi_context &ginfo) {
    // XXX
//    std::map<double, FeatureEdge> scores;
    for (int doc = 0; doc < query.num_outedges(); doc++) {
      FeatureEdge* fe = query.outedge(doc)->get_vector();
      fe->header().score = model->score(fe->get_data());
//      query.outedge(doc)->set_vector(fe);

//      scores[fe.score] = fe;
    }
//    for (auto rit = scores.crbegin(); rit != scores.crend(); ++rit) {
//      std::cout << "Score " << query.id()
//                << ": " << rit->second.str() << std::endl;
//    }
  }

 /**
  *  Vertex update function.
  */
 void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
     int ninedges = 0;
     if (gcontext.iteration == 0) {
         for(int i=0; i < vertex.num_inedges(); i++) {
             vertex.inedge(i)->set_data(vertex.id());        
             ninedges++;
         }
     } else {
         // Keep track of the number of edegs to ensure that
         // deletion works fine.
         if (vertex.get_data() != vertex.num_inedges())  {
             logstream(LOG_ERROR) << "Discrepancy in edge counts: " << vertex.get_data() << " != " << vertex.num_inedges() << std::endl;
         }
         assert(vertex.get_data() == vertex.num_inedges());
         
         for(int i=0; i < vertex.num_outedges(); i++) {
             graphchi_edge<vid_t> * edge = vertex.outedge(i);
             vid_t outedgedata = edge->get_data();
             vid_t expected = edge->vertex_id() + gcontext.iteration - (edge->vertex_id() > vertex.id());
             if (!is_deleted_edge_value(edge->get_data())) {
                 if (outedgedata != expected) {
                     logstream(LOG_ERROR) << outedgedata << " != " << expected << std::endl;
                     assert(false);
                 }
             }
         }
         for(int i=0; i < vertex.num_inedges(); i++) {
             vertex.inedge(i)->set_data(vertex.id() + gcontext.iteration);
             
             if (std::rand()  % 4 == 1) {
                 vertex.remove_inedge(i);
                 __sync_add_and_fetch(&ndeleted, 1);
             } else {
                 ninedges++;
             }
         }
     }
     
     if (gcontext.iteration == gcontext.num_iterations - 1) {
         vertex.set_data(gcontext.iteration + 1);
     } else {
         vertex.set_data(ninedges);
     }
 }

    /**
      * Pagerank update function.
	  */
	void update(graphchi_vertex<VType, EType> &v, graphchi_context &ginfo) {
		//array[v.id()]++;		
		if(v.num_edges() == 0)	return;
		if (ginfo.iteration == 0) {
			//int partid = getPId(v.id());	
			vid_t newid = getNewId(v.id()); 	
			v.set_data(newid);
			for(int i=0; i<v.num_edges(); i++){
				graphchi_edge<EType> * edge = v.edge(i);
				EType edata = edge->get_data();
				edata.my_label(v.id(), edge->vertex_id()) = newid;
				edge->set_data(edata);
			}	
		} else if(ginfo.iteration == 1){
			/*
			if(v.id() == 0){
				fprintf(fp_list, "%u %u\n", num_vertices, num_edges);	
			}
			*/
			if(v.num_outedges() > 0){	
				vid_t mylabel = v.get_data();
				for(int i=0; i<v.num_outedges(); i++){
					graphchi_edge<EType> * edge = v.outedge(i);
					EType edata = edge->get_data();
					vid_t nblabel = edata.nb_label(v.id(), edge->vertex_id());
					//vid_t nb_id = edge->vertex_id();
					assert(mylabel != nblabel);
					if(!flag_weight){
						lock.lock();
						fprintf(fp_list, "%u\t%u\n", mylabel, nblabel);		
						lock.unlock();
					}else{
						lock.lock();
						fprintf(fp_list, "%u\t%u\t%.3f\n", mylabel, nblabel, edata.weight);		
						lock.unlock();
					}
					//edge->set_data(edata);	
				}
			}/*else{
				fprintf(fp_list, "\n");
			}*/
		}
	}

    void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
            
        /*
         * Concurrent accessor to access the rvec value corresponding to the current vertex.
         */
         
        tbb::concurrent_hash_map<unsigned int, nlohmann::json>::accessor ac;
        rvec_map.insert(ac,vertex.id());
        nlohmann::json rvec = ac->second;
        
        int dependencies; //The number of active dependencies of the current vertex. 
        
        /*
         * vertex_false to keep track of all the query vertices marked false for the vertex in the current iteration
         */

        std::vector<vid_t> vertex_false;

        

        /*
         * If the vertex has a null rvec, it is being computed for the first time. 
         * Compare the vertex with each of the vertices in the query graph.
         * If the current node matches the query node, add the dependencies of the query node to the rvec.
         * If the query node does not have any dependencies, set rvec[i] as true. (This implies a direct match)
         * If the query node and the current node don't match, set rvec[i] to false and add i to vertex_false. 
         */
        
        if(rvec.is_null()){
           
            dependencies = 0; //Vertex is being computed for the first time and hence has zero dependencies. 
            
            for(unsigned int i=0; i < query_json["node"].size(); i++) {
                if(check_equal(vertex_json[vertex.id()],query_json["node"][i])) {    
                    unsigned int out_d = query_json["node"][i]["out_degree"];
                    if(out_d == 0){
                        rvec[i] = true;
                    }
                    else if(vertex.num_outedges() == 0)
                    {
                        rvec[i] = false;
                        vertex_false.push_back(i);
                    }
                    else
                    {   for(unsigned int j=0; j <query_json["edge"].size(); j++){
                            unsigned int source = query_json["edge"][j]["source"], target = query_json["edge"][j]["target"];
                            if(i == source )
                                rvec[i][target] = vertex.num_outedges();
                        }
                        dependencies++;
                    }

                }
                else
                {
                    rvec[i] = false;
                    vertex_false.push_back(i);
                }
            }
            /*
             * If the vertex has dependencies, schedule the children of the current vertex (outedges).
             */
            if(dependencies != 0){
                for(int i = 0; i <vertex.num_outedges();i++)
                    gcontext.scheduler->add_task(vertex.outedge(i)->vertex_id());
            }
            
            /*
             * Vertex data is set to the number of dependencies.
             * If the vertex data is greater than 0, then it is processed whenever it is scheduled in the subsequent iterations.
             * If the vertex data is 0, it is not processed in the subsequent iterations.  
             */
            vertex.set_data(dependencies);
        } 
            
        dependencies = vertex.get_data();
        
        /*
         * If the current vertex has dependencies, it has to be processed.
         * Collect the edge data of all it's outgoing edges and for each outgoing edge which is updated, update the corresponding dependency.
         * Else, clear all the outedges. 
         */
        
        if(dependencies != 0 ) {
            
            nlohmann::json updates;
            
            for(int i = 0; i < vertex.num_outedges(); i++){
                chivector<vid_t> * e_vector = vertex.outedge(i)->get_vector();
                int evector_size = e_vector->size();
                for( int j =0; j < evector_size; j++){
                    vid_t t = e_vector->get(j);
                    if(updates[t].is_null())
                        updates[t] = 1;
                    else {
                        int n = updates[t];
                        updates[t] = n +1;
                    }
                }
                e_vector->clear();
//.........这里部分代码省略.........

    void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {


        //For iteration 0
        if (gcontext.iteration == 0) {
            
           
            chivector<vid_t> * v_vector = vertex.get_vector();
            v_vector->clear();
            
            /* Initialize a json object, rvec to maintain the result vector of the current node.
             * Maintain the list of outedges of the current vector. 
             * These outedges are the children and must match the children of the query node.
             * Maintain a vector for all the nodes set to false in the current iteraion. 
             */
            nlohmann::json rvec;
            std::vector<vid_t> vertex_outedges;
            std::vector<vid_t> vertex_false;
            
            for (int i = 0; i < vertex.num_outedges(); i++) {
                vertex_outedges.push_back(vertex.outedge(i)->vertex_id());
            }
            
            /*
             * Iterate through all the query nodes, and check the equality with the current node. 
             * If the current node matches the query node, add the dependencies of the query node to the rvec.
             * If the query node does not have any dependencies, set rvec[i] as true. (This implies a direct match)
             * If the query node and the current node don't match, set rvec[i] to false and add i to vertex_false.
             */
            for(unsigned int i=0; i < query_json["node"].size(); i++) {
                if(check_equal(vertex_json[vertex.id()],query_json["node"][i])) {    
                    unsigned int out_d = query_json["node"][i]["out_degree"];
                    if(out_d == 0){
                        rvec[i] = true;
                        v_vector->add(i);
                    }
                    else if(vertex_outedges.size() == 0)
                    {
                        rvec[i] = false;
                        vertex_false.push_back(i);
                    }
                    else
                    {    for(unsigned int j=0; j <query_json["edge"].size(); j++){
                            unsigned int source = query_json["edge"][j]["source"], target = query_json["edge"][j]["target"];
                            if(i == source )
                                rvec[i][target] = vertex_outedges;
                        }
                        v_vector->add(i);
                    }
                    
                }
                else
                {
                    rvec[i] = false;
                    vertex_false.push_back(i);
                }
            }
            
            // Access the element of rvec_map with key equal to current vertex id. 
            // Assign rvec as the value for current vertex id.
            tbb::concurrent_hash_map<unsigned int, nlohmann::json>::accessor ac;
            rvec_map.insert(ac,vertex.id());
            ac->second = rvec;
            ac.release();
            
            /*
             * If the size of vertex_false is not zero, changes have been made in the current iteration.
             * For all the inedges, add the all the elements of vertex_false to the edge vector.
             * Schedule all the inedges for the next iteration.
             * 
             */
            
            if(vertex_false.size() != 0) {
                for(int i = 0; i < vertex.num_inedges(); i++) {
                    chivector<vid_t> * e_vector = vertex.inedge(i)->get_vector();
                    e_vector->clear();
                    for(unsigned int j = 0; j< vertex_false.size(); j++){
                        e_vector->add(vertex_false[j]);
                    }
                    gcontext.scheduler->add_task(vertex.inedge(i)->vertex_id());
                }
            }    

        } 
        
        //For iteration 1..n
        else{
            
            /*
             * Retrieve the rvec for the current node from the rvec_map. 
             * Intialize vertex_false to maintain the vertices set to false.  
             */
            tbb::concurrent_hash_map<unsigned int, nlohmann::json>::accessor ac;
            rvec_map.find(ac,vertex.id());
            nlohmann::json rvec = ac->second;
            std::vector<vid_t> vertex_false;
            
            chivector<vid_t> * v_vector = vertex.get_vector();
            
                    
//.........这里部分代码省略.........