用Buv表示节点u对其邻居节点的影响力：Buv=1/Lin；Lin（v）-------点v的入度。
２、LT传播模型算法实现
linear_threshold.py (LT传播模型算法)
Implement linear threshold models
社交网络影响力最大化 传播模型——线性阈值（LT）模型算法实现
def linear_threshold(G, seeds, steps=0): #LT线性阈值算法
"""
Parameters
----------
G : networkx graph #所有节点构成的图
The number of nodes.
seeds: list of nodes #子节点集
The seed nodes of the graph
steps: int #激活节点的层数（深度），当steps<=0时，返回子节点集能激活的所有节点
The number of steps to diffuse
When steps <= 0, the model diffuses until no more nodes
can be activated
Return
------
layer_i_nodes : list of list of activated nodes
layer_i_nodes[0]: the seeds #子节点集
layer_i_nodes[k]: the nodes activated at the kth diffusion step #该子节点集激活的节点集
Notes
-----
1. Each node is supposed to have an attribute "threshold". If not, the
default value is given (0.5). #每个节点有一个阈值，这里默认阈值为：0.5
2. Each edge is supposed to have an attribute "influence". If not, the
default value is given (1/in_degree) #每个边有一个权重值，这里默认为：1/入度
References
----------
[1] GranovetterMark. Threshold models of collective behavior.
The American journal of sociology, 1978.
"""