C++ TPt类代码示例

void TestGraphDAG(TPt<TNodeEDatNet<TFlt, TFlt>>& pGraph, std::vector<int> vSeedIDs, int numIterations)
{
	cout << "Starting BP from nodeID {";
	for(int sourceNode : vSeedIDs)
		cout << sourceNode << " ";
	cout << "}\nThe input graph has " << pGraph->GetNodes() << " nodes and " << pGraph->GetEdges() << " edges.\n";

	// Start traversing the graph
	tbb::tick_count tic = tbb::tick_count::now();
	for(int i = 0; i<numIterations; ++i)
		ParallelBPFromNodeDAG(pGraph, vSeedIDs);
	double dElapsedTime = (tbb::tick_count::now() - tic).seconds();
	cout << "Time elapsed for parallel BP: " << dElapsedTime/numIterations << " seconds\n";

	tic = tbb::tick_count::now();
	for(int i = 0; i<numIterations; ++i)
		ParallelBPFromNodeDAG_LevelSynchronous(pGraph, vSeedIDs);
	dElapsedTime = (tbb::tick_count::now() - tic).seconds();
	cout << "Time elapsed for parallel level synchronous BP: " << dElapsedTime/numIterations << " seconds\n";

	tic = tbb::tick_count::now();
	for(int i = 0; i<numIterations; ++i)
		PropagateFromNodeDAG(pGraph, vSeedIDs);
	dElapsedTime = (tbb::tick_count::now() - tic).seconds();
	cout << "Time elapsed for serial BP: " << dElapsedTime/numIterations << " seconds\n";

	std::vector<int> vResult;
	CalculateRankFromSource_BellmanFord(pGraph, vSeedIDs, vResult);
	tic = tbb::tick_count::now();
	for(int i = 0; i<numIterations; ++i)
		ParallelBPFromNode_SingleNodeUpdate(pGraph, vResult, vSeedIDs);
	dElapsedTime = (tbb::tick_count::now() - tic).seconds();
	cout << "Time elapsed for ParallelBPFromNode_SingleNodeUpdate: " << dElapsedTime/numIterations << " seconds\n";
}

bool edgeExists(TPt<TNodeEDatNet<TInt, TFlt> >  & G, int id1, int id2, TFlt weight)
{
    for (SnapEdge EI = G->BegEI(); EI < G->EndEI(); EI++)
    {
        if(EI.GetDstNDat() == id2 && EI.GetSrcNDat() == id1 && EI.GetDat() == weight)
            return true;
    }
    return false;
}

bool vertexExists(TPt<TNodeEDatNet<TInt, TFlt> >  & G, int id)
{
    for (SnapNode NI = G->BegNI(); NI < G->EndNI(); NI++)
    {
        if(NI.GetDat() == id)
        {
            return true;
        }
    }
    return false;
}

void MakeSignEpinions() {
  TSsParser Ss("/u/ana/data/EpinionRatings/user_rating.txt", ssfTabSep);
  //PSignNet Net = TSignNet::New();
  TPt<TNodeEDatNet<TInt, TInt> >  Net = TNodeEDatNet<TInt, TInt>::New();
  TStrHash<TInt> StrSet(Mega(1), true);
      
  while (Ss.Next()) {
    if ( ((TStr)Ss[0]).IsPrefix("#")  )
      continue;
    const int SrcNId = StrSet.AddKey(Ss[0]);
    const int DstNId = StrSet.AddKey(Ss[1]);
    if (! Net->IsNode(SrcNId)) { Net->AddNode(SrcNId); }
    if (! Net->IsNode(DstNId)) { Net->AddNode(DstNId); }
    const int Sign = ((TStr)Ss[2]).GetInt();
    Net->AddEdge(SrcNId, DstNId, Sign);
  }
    
  //    PrintGraphStatTable(Graph, OutFNm, Desc);
  TStr OutFNm = "soc-sign-epinions-user-ratings";
  TStr Desc = "Epinions signed social network";

  // copied from gio.h - line 111
  FILE *F = fopen(OutFNm.CStr(), "wt");
  fprintf(F, "# Directed graph: %s\n", OutFNm.CStr());
  if (! Desc.Empty()) 
    fprintf(F, "# %s\n", (Desc).CStr());
  fprintf(F, "# Nodes: %d Edges: %d\n", Net->GetNodes(), Net->GetEdges());
  fprintf(F, "# FromNodeId\tToNodeId\tSign\n"); 
  for (TNodeEDatNet<TInt,TInt>::TEdgeI ei = Net->BegEI(); ei < Net->EndEI(); ei++) {
      fprintf(F, "%d\t%d\t%d\n", ei.GetSrcNId(), ei.GetDstNId(), ei()());
  }
  fclose(F);
  
  PrintGraphStatTable(Net, OutFNm, Desc);
}

// Generate TNodeEdgeNet
TPt <TNodeEdgeNet<TInt, TInt> > GetTestTNodeEdgeNet() {
  TPt <TNodeEdgeNet<TInt, TInt> > Net;
  TPt <TNodeEdgeNet<TInt, TInt> > Net1;
  TPt <TNodeEdgeNet<TInt, TInt> > Net2;
  int n;

  Net = TNodeEdgeNet<TInt, TInt>::New();

  for (int i = 0; i < 20; i++) {
    Net->AddNode(i);
  }

  for (int i = 0; i < 20; i++) {
    Net->AddEdge(i,(i+1) % 20);
    Net->AddEdge(i,(i+2) % 20);
    Net->AddEdge(i,(i+3) % 20);
    Net->AddEdge(i,(i+1) % 20);
    Net->AddEdge(i,(i+2) % 20);
    Net->AddEdge(i,(i+3) % 20);
  }

  n = 0;
  for (TNodeEdgeNet<TInt, TInt>::TEdgeI EI = Net->BegEI(); EI < Net->EndEI(); EI++) {
    Net->SetEDat(EI.GetId(),n);
    n = (n+1) % 4;
  }

  return Net;
}

//! Given pGraph with data about edge weights, computes the distance of the shortest paths from sourceNode
//! and returns the result in the nodes of pDAGGraph.
//! Updates the edges if bUpdateEdges is set to true. Default is false. In that case only the node data is updated with the shortest distance to sourceNode.
//! @note Requires initial values for the nodes of pDAGGraph (edges are not needed)
void Dijkstra(const TPt<TNodeEDatNet<TFlt, TFlt>>& pGraph, int sourceNode, double dThreshold, TPt<TNodeEDatNet<TFlt, TFlt>>& pDAGGraph, bool bUpdateEdges = false)
{
	double logThreshold = log(dThreshold);
	if(dThreshold==0)
		logThreshold=-DBL_MAX;

	// List of visited nodes
	std::map<int, bool> visitedNodes;
	// Stores the edge vertices to build the final DAG
	std::map<int, int> mapPrevious;
	std::priority_queue<std::pair<int,double>, std::vector<std::pair<int,double>>, Order> nodesToVisit;

	// Distance from source node to itself is 0
	pDAGGraph->SetNDat(sourceNode, 0);
	nodesToVisit.push(std::make_pair(sourceNode,0));

	// Beginning of the loop of Dijkstra algorithm

	while(!nodesToVisit.empty())
	{
		// Find the vertex in queue with the smallest distance and remove it
		int iParentID = -1;
		while (!nodesToVisit.empty() && visitedNodes[iParentID = nodesToVisit.top().first])
			nodesToVisit.pop();
		if (iParentID == -1) break;

		// mark the vertex with the shortest distance
		visitedNodes[iParentID]=true;

		auto parent = pGraph->GetNI(iParentID);
		int numChildren = parent.GetOutDeg();
		for(int i = 0; i < numChildren; ++i)
		{
			int iChildID = parent.GetOutNId(i);
			// Accumulate the shortest distance from source
			double alt = pDAGGraph->GetNDat(iParentID) - log(parent.GetOutEDat(i).Val);
			if(alt >= logThreshold)
			{
				auto it = visitedNodes.find(iChildID);
				if (alt < pDAGGraph->GetNDat(iChildID) && it->second == false)
				{
					//1. update distance
					//2. update the predecessor
					//3. push new shortest rank of chidren nodes
					pDAGGraph->SetNDat(iChildID, alt);
					mapPrevious[iChildID]= iParentID;
					nodesToVisit.push(std::make_pair(iChildID,alt));
				}
			}
		}

	}

	if(bUpdateEdges)
		for(auto it=mapPrevious.begin(); it!= mapPrevious.end(); ++it)
		{
			pDAGGraph->AddEdge(it->second, it->first);
			pDAGGraph->SetEDat(it->second,it->first, pGraph->GetEDat(it->second,it->first));
		}
}

//???????
TPt<TNodeEDatNet<TFlt, TFlt>> GenerateDAG2(const TPt<TNodeEDatNet<TFlt, TFlt>>& pGraph, const std::vector<int> &vSeedIDs, double dThreshold)
{
	// Vector of MIOA graphs per seed node
	std::vector<TPt<TNodeEDatNet<TFlt, TFlt>>> vMIOAGraphs;

	// Compute the union of MIOA for each node of vSeedIDs
	for(auto it=vSeedIDs.begin(); it!=vSeedIDs.end(); ++it)
		vMIOAGraphs.push_back(MIOA(pGraph, *it, dThreshold));
	auto pOut = GraphUnion(vMIOAGraphs);

	// Set node data
	for (auto NI = pOut->BegNI(); NI < pOut->EndNI(); NI++)
		pOut->SetNDat(NI.GetId(), FLT_MAX);

	// Copy the edge weights from pGraph
	for (auto EI = pOut->BegEI(); EI < pOut->EndEI(); EI++)
		pOut->SetEDat(EI.GetSrcNId(), EI.GetDstNId(), pGraph->GetEDat(EI.GetSrcNId(), EI.GetDstNId()));

	// Create a super root in order to update in one pass all the shortest paths from vSeedIDs nodes
	int superRootID = pGraph->GetMxNId()+1;
	pOut->AddNode(superRootID);
	for(auto it=vSeedIDs.begin(); it!=vSeedIDs.end(); ++it)
	{
		pOut->AddEdge(superRootID, *it);
		pOut->SetEDat(superRootID, *it, 1.0);
	}
	Dijkstra(pOut, superRootID, dThreshold, pOut);
	// Remove the artificial super root node
	pOut->DelNode(superRootID);

	// Traverse the edges and prune the graph
	for (auto EI = pOut->BegEI(); EI < pOut->EndEI(); EI++)
	{
		if(EI.GetDstNDat().Val < EI.GetSrcNDat().Val)
			pOut->DelEdge(EI.GetSrcNId(), EI.GetDstNId());
	}

	//Reset Node data from the original graph
	for (auto NI = pGraph->BegNI(); NI < pGraph->EndNI(); NI++)
		pOut->SetNDat(NI.GetId(),NI.GetDat().Val);

	return pOut;
}

TPt<TNodeEDatNet<TFlt, TFlt>> MIOA(const TPt<TNodeEDatNet<TFlt, TFlt>>& pGraph, int sourceNode, double dThreshold)
{
	//////////////////////////////////////////////////////////////
	// Compte the Maximum Influence Out-Arborescence with Dijkstra

	// Copy the nodes of pGraph
	auto pDAGGraph = TNodeEDatNet<TFlt, TFlt>::New();
	for (auto NI = pGraph->BegNI(); NI < pGraph->EndNI(); NI++)
	{
		int NodeID = NI.GetId();
		pDAGGraph->AddNode(NodeID);
		pDAGGraph->SetNDat(NodeID, FLT_MAX);
	}
	Dijkstra(pGraph, sourceNode, dThreshold, pDAGGraph, true);

	// pDAGGraph is the MIOA starting from sourceNode

	return pDAGGraph;
}

TPt<TNodeEDatNet<TFlt, TFlt>> GenerateDAG1(const TPt<TNodeEDatNet<TFlt, TFlt>> &pGraph, const std::vector<int>& seedNodes, double threshold)
{
	// Copy pGraph into pGraph_DAG1
	auto pGraph_DAG1 = TNodeEDatNet<TFlt, TFlt>::New();

	for (auto NI = pGraph->BegNI(); NI < pGraph->EndNI(); NI++)
		pGraph_DAG1->AddNode(NI.GetId());
	
	for (auto EI = pGraph->BegEI(); EI < pGraph->EndEI(); EI++)
	{
		pGraph_DAG1->AddEdge(EI.GetSrcNId(),EI.GetDstNId());
		pGraph_DAG1->SetEDat(EI.GetSrcNId(),EI.GetDstNId(), pGraph->GetEDat(EI.GetSrcNId(),EI.GetDstNId()));
	}

	// Create a super root in order to update in one pass all the shortest paths from vSeedIDs nodes
	int superRootID =  pGraph_DAG1->GetMxNId()+1;
	pGraph_DAG1->AddNode(superRootID);

	for(int srcNode: seedNodes)
	{
		pGraph_DAG1->AddEdge(superRootID, srcNode);
		pGraph_DAG1->SetEDat(superRootID, srcNode, 1.0);
	}
	pGraph_DAG1 = MIOA(pGraph_DAG1, superRootID, threshold);
	// Remove the artificial super root node
	pGraph_DAG1->DelNode(superRootID);


	// Add back other edges with the condition r(u)<r(v)
	for (auto EI = pGraph->BegEI(); EI < pGraph->EndEI(); EI++)
	{
		int u = EI.GetSrcNId(), v = EI.GetDstNId();
		if(pGraph_DAG1->GetNDat(u)< pGraph_DAG1->GetNDat(v))
		{
			if (!pGraph_DAG1->IsEdge(u,v))
			{
				pGraph_DAG1->AddEdge(u,v);
				pGraph_DAG1->SetEDat(u,v,EI.GetDat());
			}
		}
	}
	//Reset Node data from the original graph
	for (auto NI = pGraph->BegNI(); NI < pGraph->EndNI(); NI++)
		pGraph_DAG1->SetNDat(NI.GetId(),NI.GetDat().Val);

	return pGraph_DAG1;
}

int main(int argc, char* argv[]) {
  // create a graph and save it
  { PNGraph Graph = TNGraph::New();
  for (int i = 0; i < 10; i++) {
    Graph->AddNode(i); }
  for (int i = 0; i < 10; i++) {
    Graph->AddEdge(i, TInt::Rnd.GetUniDevInt(10)); }
  TSnap::SaveEdgeList(Graph, "graph.txt", "Edge list format"); }
  // load a graph
  PNGraph Graph;
  Graph = TSnap::LoadEdgeList<PNGraph>("graph.txt", 0, 1);
  // traverse nodes
  for (TNGraph::TNodeI NI = Graph->BegNI(); NI < Graph->EndNI(); NI++) {
    printf("NodeId: %d, InDegree: %d, OutDegree: %d\n", NI.GetId(), NI.GetInDeg(), NI.GetOutDeg());
    printf("OutNodes: ");
    for (int e = 0; e < NI.GetOutDeg(); e++) { printf("  %d", NI.GetOutNId(e)); }
    printf("\nInNodes: ");
    for (int e = 0; e < NI.GetInDeg(); e++) { printf("  %d", NI.GetInNId(e)); }
    printf("\n\n");
  }
  // graph statistic
  TSnap::PrintInfo(Graph, "Graph info");
  PNGraph MxWcc = TSnap::GetMxWcc(Graph);
  TSnap::PrintInfo(MxWcc, "Largest Weakly connected component");
  // random graph
  PNGraph RndGraph = TSnap::GenRndGnm<PNGraph>(100, 1000);
  TGStat GraphStat(RndGraph, TSecTm(1), TGStat::AllStat(), "Gnm graph");
  GraphStat.PlotAll("RndGraph", "Random graph on 1000 nodes");
  // Forest Fire graph
  { TFfGGen ForestFire(false, 1, 0.35, 0.30, 1.0, 0.0, 0.0);
  ForestFire.GenGraph(100);
  PNGraph FfGraph = ForestFire.GetGraph(); }
  // network
  TPt<TNodeEDatNet<TStr, TStr> > Net = TNodeEDatNet<TStr, TStr>::New();
  Net->AddNode(0, "zero");
  Net->AddNode(1, "one");
  Net->AddEdge(0, 1, "zero to one");
  return 0;
}

// before generating DAG
void RandomGraphInitialization(TPt<TNodeEDatNet<TFlt, TFlt>> &pGraph)
{
	srand(time(NULL));
	for (auto EI = pGraph->BegEI(); EI < pGraph->EndEI(); EI++)
		pGraph->SetEDat(EI.GetSrcNId(), EI.GetDstNId(), (double) rand() / RAND_MAX);
	for (auto NI = pGraph->BegNI(); NI < pGraph->EndNI(); NI++)
		pGraph->SetNDat(NI.GetId(), 0.0);
}

// Test edge subgraphs
TEST(subgraph, TestEdgeSubNets) {
  TPt <TNodeEdgeNet<TInt, TInt> > Net;
  TPt <TNodeEdgeNet<TInt, TInt> > Net1;
  TPt <TNodeEdgeNet<TInt, TInt> > Net2;
  TPt <TNodeEdgeNet<TInt, TInt> > Net3;
  TPt <TNodeEdgeNet<TInt, TInt> > Net4;
  int i;
  TIntV NIdV;
  TIntV NIdV1;

  Net = GetTestTNodeEdgeNet();
  EXPECT_EQ(20,Net->GetNodes());
  EXPECT_EQ(120,Net->GetEdges());

  for (i = 10; i < 15; i++) {
    NIdV.Add(i);
  }

  Net1 = TSnap::GetSubGraph(Net, NIdV);
  EXPECT_EQ(5,Net1->GetNodes());
  EXPECT_EQ(18,Net1->GetEdges());

  for (i = 0; i < 20; i += 2) {
    NIdV1.Add(i);
  }

  Net2 = TSnap::GetSubGraph(Net, NIdV1);
  EXPECT_EQ(10,Net2->GetNodes());
  EXPECT_EQ(20,Net2->GetEdges());

  Net3 = TSnap::GetEDatSubGraph(Net, 1, 0);
  EXPECT_EQ(20,Net3->GetNodes());
  EXPECT_EQ(30,Net3->GetEdges());

  Net4 = TSnap::GetEDatSubGraph(Net, 2, -1);
  EXPECT_EQ(20,Net4->GetNodes());
  EXPECT_EQ(60,Net4->GetEdges());
}

// Test the default constructor
TEST(TNodeEdgeNet, DefaultConstructor) {
  TPt <TNodeEdgeNet<TInt, TInt> > Net;

  Net = TNodeEdgeNet<TInt, TInt>::New();

  EXPECT_EQ(0,Net->GetNodes());
  EXPECT_EQ(0,Net->GetEdges());

  EXPECT_EQ(1,Net->IsOk());
  EXPECT_EQ(1,Net->Empty());
  EXPECT_EQ(1,Net->HasFlag(gfDirected));
  EXPECT_EQ(1,Net->HasFlag(gfNodeDat));
}

std::vector<int> MaxIncrementalInfluence(TPt<TNodeEDatNet<TFlt, TFlt>>& pGraph, int numRounds){

	std::vector<int> vSeedSet;
	tbb::concurrent_unordered_map<int,double> mSpreadIncrement;
	auto pGraph_temp = TNodeEDatNet<TFlt, TFlt>::New();
	double influence = 0.0; int i,chunk = 50;
	static tbb::spin_mutex sMutex;

	//Failure of using PeerSeeds due to insufficient memory
	//std::map<int,std::vector<int> > mPeerSeeds;
	//std::map<int,TPt<TNodeEDatNet<TFlt, TFlt>> > mMIOAs;

	/* Initialization*/
	int numNodes = pGraph->GetMxNId();
	#pragma omp parallel shared(pGraph,chunk,mSpreadIncrement) private(pGraph_temp,i)
	{
		#pragma omp for schedule(dynamic,chunk) nowait
			for (i =0;i<numNodes;++i)
			{
				if(pGraph->IsNode(i))
				{
					pGraph_temp = MIOA(pGraph, i, 0);
					InitializationBeforePropagation(pGraph_temp);
					ParallelBPFromNode_1DPartitioning(pGraph_temp, i);
					mSpreadIncrement[i]=InfluenceSpreadFromSeedNodes(pGraph_temp);
					//mMIOAs.insert(std::make_pair(i,pGraph_v));
				}

			}
	}

/*
	//build PeerSeeds
	//Failure due to the insufficient memory
	for (int v =0; v<pGraph->GetNodes();++v)
		if(pGraph->IsNode(v))
			mPeerSeeds[v]=GetPeerSeeds(mMIOAs,v);
*/

	cout<<"--------------------------Finished Initialization---------------------"<<endl;

	for (int i=0;i<numRounds;++i)
	{
		/* select the i'th seed by finding u = argmax(mSpreadIncrement)*/
		auto it = std::max_element(mSpreadIncrement.begin(),mSpreadIncrement.end(),
							[&](std::pair<int,double> const& a, std::pair<int,double> const& b) {
								 return a.second < b.second;
								 }
							);
		int SeedID = it->first;
		cout << SeedID <<endl;

		/* calculate the current influence spread */
		vSeedSet.push_back(SeedID);
		pGraph = GenerateDAG1(pGraph, vSeedSet, 0.0);
		ParallelBPFromNode_1DPartitioning(pGraph, vSeedSet);
		influence = InfluenceSpreadFromSeedNodes(pGraph);

		/*remove the newly selected node*/
		mSpreadIncrement.unsafe_erase(SeedID);

		/* update incremental influence spread for each round */
		double Delta_MAX = 0.0;
		std::vector<int> vSeedSet_temp = vSeedSet;
		#pragma omp parallel shared(pGraph,chunk,vSeedSet,mSpreadIncrement,Delta_MAX) private(pGraph_temp,vSeedSet_temp,i)
		{
			#pragma omp for schedule(dynamic,chunk) nowait
				for (i =0;i<numNodes;++i)
				{
					/* exclude the nodes in seed set */
					auto result = std::find(vSeedSet.begin(),vSeedSet.end(), i);
					if (result != vSeedSet.end()) continue;

					if(pGraph->IsNode(i) && mSpreadIncrement[i] > Delta_MAX)
					{
						/*different processors use different copied vSeedSet*/
						vSeedSet_temp.push_back(i);

						pGraph_temp = GenerateDAG1(pGraph, vSeedSet_temp, 0);
						ParallelBPFromNode_1DPartitioning(pGraph_temp, vSeedSet_temp);
						mSpreadIncrement[i]=InfluenceSpreadFromSeedNodes(pGraph_temp)-influence;
						if (mSpreadIncrement[i]> Delta_MAX)
						{
							tbb::spin_mutex::scoped_lock lock(sMutex);
							Delta_MAX = mSpreadIncrement[i];
						}
						vSeedSet_temp.pop_back();
					}
				}
		}
	}
	return vSeedSet;
}

// Test update node data
void UpdateNodeData() {
  int NNodes = 10000;
  int NEdges = 100000;

  TPt <TNodeEDatNet<TInt, TInt> > Net;
  TPt <TNodeEDatNet<TInt, TInt> > Net1;
  TPt <TNodeEDatNet<TInt, TInt> > Net2;
  int i;
  int n;
  int NCount;
  int x,y;
  bool t;
  int NodeDat;
  int Value;
  bool ok;

  Net = TNodeEDatNet<TInt, TInt>::New();
  t = Net->Empty();

  // create the nodes
  for (i = 0; i < NNodes; i++) {
    Net->AddNode(i,i+5);
  }
  t = Net->Empty();
  n = Net->GetNodes();

  // create random edges
  NCount = NEdges;
  while (NCount > 0) {
    x = (long) (drand48() * NNodes);
    y = (long) (drand48() * NNodes);
    // Net->GetEdges() is not correct for the loops (x == y),
    // skip the loops in this test
    if (x != y  &&  !Net->IsEdge(x,y)) {
      n = Net->AddEdge(x, y);
      NCount--;
    }
  }
  PrintNStats("UpdateNodeData:Net", Net);

  // read and test node data
  ok = true;
  for (TNodeEDatNet<TInt, TInt>::TNodeI NI = Net->BegNI(); NI < Net->EndNI(); NI++) {
    NodeDat = Net->GetNDat(NI.GetId());
    Value = NI.GetId()+5;
    if (NodeDat != Value) {
      ok = false;
    }
  }
  printf("network UpdateNodeData:Net, status1 %s\n", (ok == true) ? "ok" : "ERROR");

  // update node data, node ID + 10
  for (TNodeEDatNet<TInt, TInt>::TNodeI NI = Net->BegNI(); NI < Net->EndNI(); NI++) {
    Net->SetNDat(NI.GetId(), NI.GetId()+10);
  }

  // read and test node data
  ok = true;
  for (TNodeEDatNet<TInt, TInt>::TNodeI NI = Net->BegNI(); NI < Net->EndNI(); NI++) {
    NodeDat = Net->GetNDat(NI.GetId());
    Value = NI.GetId()+10;
    if (NodeDat != Value) {
      ok = false;
    }
  }
  printf("network UpdateNodeData:Net, status2 %s\n", (ok == true) ? "ok" : "ERROR");
}