@antv/algorithm
Version:
graph algorithm
179 lines • 7.17 kB
JavaScript
import createWorker from './createWorker';
import { ALGORITHM } from './constant';
/**
* @param graphData 图数据
* @param directed 是否为有向图
*/
var getAdjMatrixAsync = function getAdjMatrixAsync(graphData, directed) {
return createWorker(ALGORITHM.getAdjMatrix).apply(void 0, [graphData, directed]);
};
/**
* 图的连通分量
* @param graphData 图数据
* @param directed 是否为有向图
*/
var connectedComponentAsync = function connectedComponentAsync(graphData, directed) {
return createWorker(ALGORITHM.connectedComponent).apply(void 0, [graphData, directed]);
};
/**
* 获取节点的度
* @param graphData 图数据
*/
var getDegreeAsync = function getDegreeAsync(graphData) {
return createWorker(ALGORITHM.getDegree)(graphData);
};
/**
* 获取节点的入度
* @param graphData 图数据
* @param nodeId 节点ID
*/
var getInDegreeAsync = function getInDegreeAsync(graphData, nodeId) {
return createWorker(ALGORITHM.getInDegree)(graphData, nodeId);
};
/**
* 获取节点的出度
* @param graphData 图数据
* @param nodeId 节点ID
*/
var getOutDegreeAsync = function getOutDegreeAsync(graphData, nodeId) {
return createWorker(ALGORITHM.getOutDegree)(graphData, nodeId);
};
/**
* 检测图中的(有向) Cycle
* @param graphData 图数据
*/
var detectCycleAsync = function detectCycleAsync(graphData) {
return createWorker(ALGORITHM.detectCycle)(graphData);
};
/**
* 检测图中的(无向) Cycle
* @param graphData 图数据
*/
var detectAllCyclesAsync = function detectAllCyclesAsync(graphData) {
return createWorker(ALGORITHM.detectAllCycles)(graphData);
};
/**
* 检测图中的所有(有向) Cycle
* @param graphData 图数据
*/
var detectAllDirectedCycleAsync = function detectAllDirectedCycleAsync(graphData) {
return createWorker(ALGORITHM.detectAllDirectedCycle)(graphData);
};
/**
* 检测图中的所有(无向) Cycle
* @param graphData 图数据
*/
var detectAllUndirectedCycleAsync = function detectAllUndirectedCycleAsync(graphData) {
return createWorker(ALGORITHM.detectAllUndirectedCycle)(graphData);
};
/**
* Dijkstra's algorithm, See {@link https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm}
* @param graphData 图数据
*/
var dijkstraAsync = function dijkstraAsync(graphData, source, directed, weightPropertyName) {
return createWorker(ALGORITHM.dijkstra).apply(void 0, [graphData, source, directed, weightPropertyName]);
};
/**
* 查找两点之间的所有路径
* @param graphData 图数据
* @param start 路径起始点ID
* @param end 路径终点ID
* @param directed 是否为有向图
*/
var findAllPathAsync = function findAllPathAsync(graphData, start, end, directed) {
return createWorker(ALGORITHM.findAllPath).apply(void 0, [graphData, start, end, directed]);
};
/**
* 查找两点之间的所有路径
* @param graphData 图数据
* @param start 路径起始点ID
* @param end 路径终点ID
* @param directed 是否为有向图
* @param weightPropertyName 边权重的属名称，若数据中没有权重，则默认每条边权重为 1
*/
var findShortestPathAsync = function findShortestPathAsync(graphData, start, end, directed, weightPropertyName) {
return createWorker(ALGORITHM.findShortestPath).apply(void 0, [graphData, start, end, directed, weightPropertyName]);
};
/**
* Floyd–Warshall algorithm, See {@link https://en.wikipedia.org/wiki/Floyd%E2%80%93Warshall_algorithm}
* @param graphData 图数据
* @param directed 是否为有向图
*/
var floydWarshallAsync = function floydWarshallAsync(graphData, directed) {
return createWorker(ALGORITHM.floydWarshall).apply(void 0, [graphData, directed]);
};
/**
* 标签传播算法
* @param graphData 图数据
* @param directed 是否有向图，默认为 false
* @param weightPropertyName 权重的属性字段
* @param maxIteration 最大迭代次数
*/
var labelPropagationAsync = function labelPropagationAsync(graphData, directed, weightPropertyName, maxIteration) {
if (maxIteration === void 0) {
maxIteration = 1000;
}
return createWorker(ALGORITHM.labelPropagation)(graphData, directed, weightPropertyName, maxIteration);
};
/**
* 社区发现 louvain 算法
* @param graphData 图数据
* @param directed 是否有向图，默认为 false
* @param weightPropertyName 权重的属性字段
* @param threshold
*/
var louvainAsync = function louvainAsync(graphData, directed, weightPropertyName, threshold) {
return createWorker(ALGORITHM.louvain)(graphData, directed, weightPropertyName, threshold);
};
/**
* 最小生成树，See {@link https://en.wikipedia.org/wiki/Kruskal%27s_algorithm}
* @param graph
* @param weight 指定用于作为边权重的属性，若不指定，则认为所有边权重一致
* @param algo 'prim' | 'kruskal' 算法类型
* @return EdgeConfig[] 返回构成MST的边的数组
*/
var minimumSpanningTreeAsync = function minimumSpanningTreeAsync(graphData, weight, algo) {
return createWorker(ALGORITHM.minimumSpanningTree).apply(void 0, [graphData, weight, algo]);
};
/**
* PageRank https://en.wikipedia.org/wiki/PageRank
* refer: https://github.com/anvaka/ngraph.pagerank
* @param graph
* @param epsilon 判断是否收敛的精度值，默认 0.000001
* @param linkProb 阻尼系数（dumping factor），指任意时刻，用户访问到某节点后继续访问该节点链接的下一个节点的概率，经验值 0.85
*/
var pageRankAsync = function pageRankAsync(graphData, epsilon, linkProb) {
return createWorker(ALGORITHM.pageRank).apply(void 0, [graphData, epsilon, linkProb]);
};
/**
* 获取指定节点的所有邻居
* @param nodeId 节点 ID
* @param edges 图中的所有边数据
* @param type 邻居类型
*/
var getNeighborsAsync = function getNeighborsAsync(nodeId, edges, type) {
return createWorker(ALGORITHM.getNeighbors).apply(void 0, [nodeId, edges, type]);
};
/**
* GADDI 图模式匹配
* @param graphData 原图数据
* @param pattern 搜索图（需要在原图上搜索的模式）数据
* @param directed 是否计算有向图，默认 false
* @param k 参数 k，表示 k-近邻
* @param length 参数 length
* @param nodeLabelProp 节点数据中代表节点标签（分类信息）的属性名。默认为 cluster
* @param edgeLabelProp 边数据中代表边标签（分类信息）的属性名。默认为 cluster
*/
var GADDIAsync = function GADDIAsync(graphData, pattern, directed, k, length, nodeLabelProp, edgeLabelProp) {
if (directed === void 0) {
directed = false;
}
if (nodeLabelProp === void 0) {
nodeLabelProp = 'cluster';
}
if (edgeLabelProp === void 0) {
edgeLabelProp = 'cluster';
}
return createWorker(ALGORITHM.GADDI).apply(void 0, [graphData, pattern, directed, k, length, nodeLabelProp, edgeLabelProp]);
};
export { getAdjMatrixAsync, connectedComponentAsync, getDegreeAsync, getInDegreeAsync, getOutDegreeAsync, detectCycleAsync, detectAllCyclesAsync, detectAllDirectedCycleAsync, detectAllUndirectedCycleAsync, dijkstraAsync, findAllPathAsync, findShortestPathAsync, floydWarshallAsync, labelPropagationAsync, louvainAsync, minimumSpanningTreeAsync, pageRankAsync, getNeighborsAsync, GADDIAsync };