(self, walk_length, start_node)
| 103 | return features_matrix |
| 104 | |
| 105 | def _node2vec_walk(self, walk_length, start_node): |
| 106 | # Simulate a random walk starting from start node. |
| 107 | G = self.G |
| 108 | alias_nodes = self.alias_nodes |
| 109 | alias_edges = self.alias_edges |
| 110 | |
| 111 | walk = [start_node] |
| 112 | |
| 113 | while len(walk) < walk_length: |
| 114 | cur = walk[-1] |
| 115 | cur_nbrs = list(G.neighbors(cur)) |
| 116 | if len(cur_nbrs) > 0: |
| 117 | if len(walk) == 1: |
| 118 | walk.append(cur_nbrs[alias_draw(alias_nodes[cur][0], alias_nodes[cur][1])]) |
| 119 | else: |
| 120 | prev = walk[-2] |
| 121 | next = cur_nbrs[alias_draw(alias_edges[(prev, cur)][0], alias_edges[(prev, cur)][1])] |
| 122 | walk.append(next) |
| 123 | else: |
| 124 | break |
| 125 | |
| 126 | return walk |
| 127 | |
| 128 | def _simulate_walks(self, num_walks, walk_length): |
| 129 | # Repeatedly simulate random walks from each node. |
no test coverage detected