图的邻接表和邻接矩阵表示最为常用，但是有时需要建图时这两种表示效率不是很高，因为需要构造每个结点和每一条边。此时使用一些隐式的表示方法可以提升建图效率。
classSolution: defladderLength(self, beginWord: str, endWord: str, wordList: List[str]) ->int: N, K=len(wordList), len(beginWord) find_end=Falseforiinrange(N): ifwordList[i] ==endWord: find_end=Truebreakifnotfind_end: return0wordList.append(beginWord) N+=1# clustering nodes for efficiency compare to adjacent listcluster=collections.defaultdict(list) foriinrange(N): node=wordList[i] forjinrange(K): cluster[node[:j] +'*'+node[j+1:]].append(node) # bidirectional BFSvisited_start, visited_end=set([beginWord]), set([endWord]) bfs_start, bfs_end=collections.deque([beginWord]), collections.deque([endWord]) step=2whilebfs_startandbfs_end: # startnum_level=len(bfs_start) whilenum_level>0: node=bfs_start.popleft() forjinrange(K): key=node[:j] +'*'+node[j+1:] fornincluster[key]: ifninvisited_end: returnstep*2-2ifnnotinvisited_start: visited_start.add(n) bfs_start.append(n) num_level-=1# endnum_level=len(bfs_end) whilenum_level>0: node=bfs_end.popleft() forjinrange(K): key=node[:j] +'*'+node[j+1:] fornincluster[key]: ifninvisited_start: returnstep*2-1ifnnotinvisited_end: visited_end.add(n) bfs_end.append(n) num_level-=1step+=1return0