Community 1
Community 2

Introduction
v Community Influence Maximization v 社区影响最大化
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Firstly proposed by Jerry Scripps et al (2007)
The nodes in the same community have much homogeneity, so they have frequent connections. 相同社区中的节点的相似性，联系频繁。 The nodes from different community have much heterogeneity, so they have seldom connections. 不同社区中的节点的相异性，联系很少。 n The influence can spread very abroad in the same community, however it is difficult to arrive a different community. 相同社区中 影响传播很快很广泛，而很难传播到不同的社区。
0.6 Inactive node Active node U 0.5 0.3 0.2 0.5 Threshold[0,1] Influence received
Abstract
v The results show that
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ACS is more suitable to the moderate or large network which has many communities, ACD can achieve a good result by adjusting the parameters values. 结果表明 ACS适合含有多个社区的大中型网络 ACD通过调整系数来获取较好的结果
v Spread models: 影响模型
Linear threshold model, Independent cascade model, Degressive cascade model, …… 线型阈模型，独立级联模型，递减级联模型
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A social network with two communities
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Introduction vSocial network社会网络
Formed with the development of IT and Internet
vInnovative thing新事物
Comes with uncertainties and risks不确定性、风险 性
Domingos and Richardson (2001, 2002) Graph G: a social network 社会网络 Node节点: individual 个体 Edge边: relation between nodes节点间的联系 Goal: finding the best k nodes as the initial sowers of influence in order to maximize the number of nodes that will eventually be influenced 目标：找到最好的k个节点，作为影响传播的种子节点，能 够最终影响最多的节点数。
Influence spread model vActivation激活
The nodes in social network will be activated when the influence it accepted is bigger than its threshold. 在社会网络中，当节点受到的影响大 于其阈值，则该节点将被激活 The node which is activated for the first time has the ability to influence its adjacent nodes.当节 点初次被激活，则该节点具有影响其相邻节点的能力， 即传染性。
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Contents
1. Introduction 2. Influence spread model 3. Influence maximization algorithms 4. Experiments and analysis
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Weighted graph 加权图 The weight of an edge: the number of the connections 边的权值：联系的次数 A
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A simple weighted social network 简单的加权社会网络图
Strength of influence: 影响力 •Frequency of connection联系频率 •Number of neighbors邻居节点数
If the number of influenced nodes is not less than one in a community, then we say this community is covered by the influence. 假定：如果一个社区中有一个或一个以上的节点受影响了，则称这个社区已被影 响覆盖。
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ACS calculates the amount of communities (comA) attached to each node, and iteratively chooses the node which spans the most communities into the target set. ACS计算每个节点连接的社区数，不断寻找连接最多社区的节点 ACD uses the combination of comA and degree to evaluate the importance of each node, and then chooses the several most important nodes into the target set. ACD综合利用ComA和节点度来计算节点的重要性，寻找重要的节点
vIndividual threshold个体阈值
Starting point from which individual begins to accept innovative thing
vInfluence spread process影响传播过程
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Introduction vInfluence Maximization影响最大化
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的社区数。
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Goal: finding the best k nodes as initial sowers of influence in order to maximize the number of communities that will eventually be covered by influence. 目标：找到最好的k个节点，作为影响传播的种子节点，能够最终影响最多
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Introduction
v Involved fields:相关领域
Collective behavior, Social network, Innovative spread,…… 集体行为，社会网络，创新传播…
v Algorithms: 算法
Random method, Degree method, Greedy method, …… 随机方法，度方法，贪心算法
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ACS is based on community structure基于社区结构 ACD is based on the combination of community amount and degree基于社区数与节点度
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Abstract
v Firstly, finding the community structures hidden in the networks by employing the community mining algorithm (ICS/Radicchi). 采用经典社区挖掘算法找到隐含于社会网络中的社 区。 v Then
v Furthermore, our work also takes a new insight into the research of the problem of influence maximization. v 此文对影响最大化研究提出新的见解。 v Key words:
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Community structure, Influence maximization, Community coverage, Data mining 社区结构，影响最大化，社区覆盖，数据挖掘
v Finally, utilizing the reconstructed linear threshold model we do comparison experiments on three datasets. v 采用重构线型阈模型，并在三个数据集上进行对比。
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Contents
1. Introduction引言 2. Influence spread model影响传播模型 3. Influence maximization algorithms 影响最大化算法 4. Experiments and analysis实验分析
wij =
ij j∈neighbor node set of i
∑f
f ij
(1)
wij ≠ w ji