List Algorithm
List<Table> tables = client.listAlgo();
Degree Calculation
Out Degree
OutDegreeRequest request = new OutDegreeRequest();
request.setNodeId(12L);
List<Table> tables = client.outDegree(request);
Out Degree - all nodes
OutDegreeAllRequest request = new OutDegreeAllRequest();
List<Table> tables = client.outDegreeAll(request);
In Degree
InDegreeRequest request = new InDegreeRequest();
request.setNodeId(12L);
List<Table> tables = client.inDegree(request);
In Degree - all nodes
InDegreeAllRequest request = new InDegreeAllRequest();
List<Table> tables = client.inDegreeAll(request);
Degree Centrality
DegreeCentralityRequest request = new DegreeCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.degreeCentrality(request);
Degree Centrality - all nodes
DegreeCentralityAllRequest request = new DegreeCentralityAllRequest();
List<Table> tables = client.degreeCentralityAll(request);
Centrality Calculation
Closeness Centrality
ClosenessCentralityRequest request = new ClosenessCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.closenessCentrality(request);
Out Closeness Centrality
OutClosenessCentralityRequest request = new OutClosenessCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.outClosenessCentrality(request);
In Closeness Centrality
InClosenessCentralityRequest request = new InClosenessCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.inClosenessCentrality(request);
Graph Centrality
GraphCentralityRequest request = new GraphCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.graphCentrality(request);
Betweenness Centrality
BetweennessCentralityRequest request = new BetweennessCentralityRequest();
request.setNodeId(12L);
List<Table> tables = client.betweennessCentrality(request);
General Graph Algorithm
Graph-Wide K-Hop
KhopAlgoRequest request = new KhopAlgoRequest();
request.setDepth(1);
List<Table> tables = client.khopAlgo(request);
Connected Component
List<Table> tables = client.connectedComponent();
Triangle Counting
List<Table> tables = client.triangleCounting();
Common Neighbours
CommonNeighboursRequest request = new CommonNeighboursRequest();
request.setNodeId1(12);
request.setNodeId2(24);
List<Table> tables = client.commonNeighbours(request);
Subgraph
SubgraphRequest request = new SubgraphRequest();
request.setNodeIds(new int[] {1,2,3,4});
List<Table> tables = client.subGraph(request);
dvanced Graph Algorithm
hyperANF
HyperANFRequest request = new HyperANFRequest();
request.setLoopNum(2);
request.setRegisterNum(4);
List<Table> tables = client.hyperANF(request);
kNN
KNearestNeighborRequest request = new KNearestNeighborRequest();
request.setNodeId(2);
request.setNodePropertyNames(Arrays.asList("age"));
request.setTop(10);
request.setTargetPropertyName("name");
List<Table> tables = client.kNearestNeighbor(request);
k-Core
KCoreRequest request = new KCoreRequest();
request.setK(5);
List<Table> tables = client.kCore(request);
MST
MinimumSpanningTreeRequest request = new MinimumSpanningTreeRequest();
request.setStartNodeId(12);
request.setEdgePropertyName("rank");
request.setWriteBack(true);
List<Table> tables = client.minimumSpanningTree(request);
k-Means
KMeansRequest request = new KMeansRequest();
request.setK(3);
request.setStartIds(Arrays.asList(1,2,3,4));
request.setLoopNum(2);
request.setNodePropertyNames(Arrays.asList("age"));
request.setDistanceType(DistanceType.CosineSimilarityDistance);
List<Table> tables = client.kMeans(request);
Clustering Coefficient
ClusteringCoefficientRequest request =
new ClusteringCoefficientRequest();
request.setNodeId(12);
List<Table> tables = client.clusteringCoefficient(request);
Community Detection & Propagation Algorithm
Page Rank
PageRankRequest request = new PageRankRequest();
request.setLoopNum(2);
request.setDamping(0.8f);
List<Table> tables = client.pageRank(request);
Sybil Rank
SybilRankRequest request = new SybilRankRequest();
request.setLoopNum(2);
request.setSybilNum(10);
request.setTrustSeeds(Arrays.asList(63342,12324,52356,18974,9634,34));
request.setTotalTrust(100);
List<Table> tables = client.sybilRank(request);
LPA
LabelPropagationRequest request = new LabelPropagationRequest();
request.setLoopNum(2);
request.setNodePropertyName("name");
List<Table> tables = client.labelPropagation(request);
HANP （Hop Attenuation & Node Preference）
HANPRequest request = new HANPRequest();
request.setLoopNum(2);
request.setDelta(1);
request.setEdgePropertyName("rank");
request.setNodePropertyName("age");
request.setM(2);
request.setWriteBack(true);
List<Table> tables = client.hanp(request);
Louvain
LouvainRequest request = new LouvainRequest();
request.setPhase1Loop(5);
request.setMinModularityIncrease(0.01f);
request.setEdgePropertyName("rank");
List<Table> tables = client.louvain(request);
Louvain Visualization
LouvaindvRequest request = new LouvaindvRequest();
request.setTaskId(1);
request.setTop(1);
request.setTotal(500);
List<Table> tables = client.louvainDv(request);
Similarity
Jaccard Similarity
JaccardSimilarityRequest request = new JaccardSimilarityRequest();
request.setNodeId1(1);
request.setNodeId2(2);
request.setTop(3);
List<Table> tables = client.jaccardSimilarity(request);
Cosine Similarity
CosineSimilarityRequest request = new CosineSimilarityRequest();
request.setNodeId1(1);
request.setNodeId2(2);
request.setNodePropertyNames(Arrays.asList("name", "age"));
List<Table> tables = client.cosineSimilarity(request);
Graph Embedding
Random Walk
RandomWalkRequest request = new RandomWalkRequest();
request.setWalkNum(2);
request.setWalkLength(2);
request.setEdgePropertyName("age");
List<Table> tables = client.randomWalk(request);
Node2Vec Random Walk
RandomWalkNode2VecRequest request = new RandomWalkNode2VecRequest();
request.setWalkNum(1);
request.setWalkLength(1);
request.setP(1);
request.setQ(1);
request.setEdgePropertyName("age");
List<Table> tables = client.randomWalkNode2Vec(request);
Struc2Vec Random Walk
RandomWalkStruc2VecRequest request = new RandomWalkStruc2VecRequest();
request.setWalkNum(1);
request.setWalkLength(1);
request.setK(1);
request.setStayProbability(0.5f);
List<Table> tables = client.randomWalkStruc2Vec(request);
Node2Vec Embedding
Node2VecRequest request = new Node2VecRequest();
request.setLoopNum(1);
request.setContextSize(1);
request.setDimension(1);
request.setEdgePropertyName("rank");
request.setIterNum(1);
request.setLearningRate(.5);
request.setMinFrequency(1);
request.setMinLearningRate(.5);
request.setNegNum(1);
request.setP(.5f);
request.setQ(.5f);
request.setResolution(1);
request.setSubSampleAlpha(.5);
request.setWalkLength(1);
request.setWalkNum(1);
List<Table> tables = client.node2Vec(request);
LINE（Large Information Network Embedding）
LINERequest request = new LINERequest();
request.setDimension(1);
request.setEdgePropertyName("rank");
request.setNegNum(1);
request.setResolution(1);
request.setStartAlpha(.5);
request.setTotalSample(1);
request.setTrainOrder(1);
List<Table> tables = client.line(request);
Struc2Vec Embedding
Struc2VecRequest request = new Struc2VecRequest();
request.setContextSize(1);
request.setDimension(1);
request.setK(1);
request.setLearningRate(.5);
request.setLoopNum(1);
request.setMinFrequency(1);
request.setMinLearningRate(.5);
request.setNegNum(1);
request.setResolution(1);
request.setStayProbability(.5f);
request.setSubSampleAlpha(.5);
request.setWalkLength(1);
request.setWalkNum(1);
request.setWindowSize(1);
List<Table> tables = client.struc2vec(request);
