lpa社区发现

with dense connections within groups and only sparser connections between them" (Newman, 2004). Such groups are called “communities".

一、LPA算法

当前所用的一些社区发现算法，需要先验信息，比如社团的数目和大小，或者计算量很大。LPA算法只需要使用网络结构，不需要一个预定义目标函数的优化或者先验信息。在LPA中，每一个节点用一个唯一的label初始化，每一步中，每个节点采纳它的多数邻居当前拥有的label，在这个迭代过程中，链接紧密的节点拥有一致的label，从而组成一个community。

二、TCLAP算法（Transitive Clustering and Pruning, TCLAP）

1.   Clusters：BFS sampling

随机抽取一个节点v0，定义一个集合C，按照BFS方法增加v0的邻居到集合C。

2.   Pruning：

二、realization

1.Newman kindly provided his modularity spectralmaximization algorithm in C,

2.our T-CLAP algorithm is implemented in Python.

3.modified T-CLAP: simpler because it skips clustering

<Peer Influence in the Diffusion of the iPhone3G over a Large Social Network>

 

三、comparison

T-CLAP and the Newman algorithm, we generally onlycompare the running time, because Newman's algorithm is a graph partitioningalgorithm and does not return any subpopulations (beyond the clus-ters in thereturned partition). For the comparison between T-CLAP and the Clausetalgorithm, the comparison is based on the quality of the subpopulationsreturned, which is measured by the I -E ratio and density, plus speed, which ismeasured by running times.

modularity Q用于评估社团发现的效果，

LPA：Near linear time algorithm to detect community structures in large-scale networks