Python图分析与社交网络探索实战

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"《Python for Graph and Network Analysis》是一本深入研究社交网络分析的专业书籍，由 Mohammed Zuhair Al-Taie 和 Seifedine Kadry 联合编写，作为Advanced Information and Knowledge Processing系列的一部分。该系列由 Lakhmi C. Jain 编辑，他分别来自英国伯恩茅斯大学和澳大利亚南澳大利亚大学，旨在关注在航空、商业、安全、教育、工程、健康、管理和科学等领域的先进信息处理与知识处理的新设计与应用。这本书特别针对Python语言，这是当前数据科学和网络分析领域中最受欢迎的编程语言之一。Python的强大之处在于其易读性、丰富的库支持以及强大的数据处理能力，使得它成为图论和社交网络分析的理想工具。在社交网络分析中，作者可能会探讨如何构建和解析复杂网络结构，如节点、边、社区检测、中心性测量、网络可视化以及网络演化模型等核心概念。书中可能涵盖了以下几个关键知识点： 1. Python图形库：如NetworkX、 igraph或Gephi等，这些库提供了创建、操作和分析图的数据结构和算法。 2. 网络数据结构：介绍如何使用邻接矩阵、邻接列表和其他数据结构来表示网络，并解释它们的优缺点。 3. 网络分析算法：包括中心性分析（如度中心性、接近中心性、介数中心性等）、社区检测方法（如 Louvain算法、 Girvan-Newman分割等）和聚类算法。 4. 网络可视化：如何将复杂的网络结构转化为易于理解的图形表示，如力导向布局、层次结构等。 5. 社交网络动态：探讨网络随时间变化的趋势，如网络的增长、稳定性和社区结构的变化。 6. 机器学习与社交网络：结合机器学习技术，如推荐系统、情感分析等，挖掘社交网络中的模式和洞察。 7. 案例研究：通过实际案例展示如何在不同领域（如电子商务、社交媒体分析、科学研究合作网络）应用Python进行社交网络分析。阅读这本书，读者不仅可以提升Python编程技能，还能深入了解社交网络分析的实际应用，这对于那些希望在这个快速发展的领域保持竞争力的IT专业人士来说是宝贵的资源。"

• In the blogosphere, a relation can be established between actor a and actor b if

the latter commented on a post published by the former.

A social network can contain millions of users. Some of these users are more

active or more powerful than other users. In order to distinguish between the differ-

ent users in a social network, a number of network measurements such as degree,

closeness, betweenness, and eigenvector centralities were developed. These mea-

surements can extract, based on a dened criterion, the users who are more impor-

tant than others. Degree centrality, for example, considers the number of direct links

to other users as the measure of importance. Closeness centrality measures how

close a user is to the other users in the network. Betweenness centrality describes

how important a user is as a link between different network segments. Eigenvector

centrality denes the important user as the one who is connected to important users

in the network.

1.2 Network Measurements

A number of measurable network characteristics were developed to gain a greater

insight into networks, with many of them having their roots in social studies on the

relationships among social actors. In this section, we will discuss three categories of

measurements that have been dened in the social network analysis stream:

1. Network connection, which includes transitivity, multiplexity, homophily, dyads

and mutuality, balance and triads, and reciprocity

2. Network distribution, which includes the distance between nodes, degree cen-

trality, closeness centrality, betweenness centrality, eigenvector centrality,

PageRank, geodesic distance and shortest path, eccentricity, and density

3. Network segmentation, which includes cohesive subgroups, cliques, clustering

coefcient, k-cores, core/periphery, blockmodels, and hierarchical clustering

1.2.1 Network Connection

Network connection (or connectivity) refers to the ability to move from one node to

another in a network. It is the ratio between route distance and geodesic distance.

Connectivity can be calculated locally (for a part of the network) and globally (for

the entire network). Let’s take a look at some of the important metrics of network

connection.

1.2 Network Measurements

curving back to their nodes. In this case, the value of the main diagonal for any of

these nodes is no more zero.

In addition to the simple representation of relationships between actors using

zeros and ones to indicate the presence or absence of a tie between two nodes as

done in the above table, we can also show the strength of the ties. For example, if

the ties are multiplex, the values in the cells could be the number of different types

of relationships between nodes. Simple metrics like “weak,” “medium,” or “strong”

can also be used to indicate the strength of a relationship between pairs of nodes.

They are translated as one, two, three, respectively. Nodes which have no tie between

them are still coded zero (Table1.2).

1.2.4 Homophily

An important requirement for the study of social networks is the identication of

groups and the study of the relations between them. It is an important driver for

maintaining stability in human and animal social communities over time, and it is a

major criterion that governs the making of ties in social networks.

Homophily is the tendency of individuals to connect with others who share the

same attitudes and beliefs. The tendency of individuals to associate with similar

others based on gender, education, race, or other socioeconomic characteristics is

very common in social communities. Coordination and cooperation are typically

more successful between people who show some similarity to each other such that

individuals in homophilic relationships are likely to hear about new ideas or ask for

help from each other.

Homophily in the context of online social networking can be understood from

the similarity of users who are using the network in terms of age, educational back-

ground, region, or profession. In the sense of corporate networks, homophily is

translated as the similarity of professional or academic qualications.

Table 1.2 Matrix representation of the sociogram showing strengths of ties

A B C D E

A 0 3 2 0 0

B 0 0 0 0 0

C 0 0 0 1 1

D 0 0 1 0 2

E 0 0 1 2 0

1 Theoretical Concepts ofNetwork Analysis

1.2.5 Dyads andMutuality

A dyad is a pair of actors in a network potentially connected by a social relation. It

is the simplest possible social group in which the transfer of information between

the two nodes can be fast and easy. Dyads are among the concepts that characterize

social networks. In the realm of corporations, dyadic links between organizations

may allow the exchange of information and knowledge, joint venture, tangible and

intangible resources, board interlocks, and many others. Each dyad can be in one of

the three cases: a positive relationship, a negative relationship, and no relationship

between the nodes of the dyad (empty dyad) (Fig.1.3).

In Fig.1.3a, b, nodes indicate individuals and each edge indicates either a posi-

tive or negative relationship. Figure c shows that node A and node B are not con-

nected by any edge.

1.2.6 Balance andTriads

A triad is a network structure consisting of three actors and three dyads. Given a

complete graph of three actors (triad), we can identify four different types of rela-

tionships, depending on the number of negative relationships between nodes: (a) a

friend of my friend is my friend, (b) an enemy of my enemy is my friend, (c) a friend

of my friend is my enemy, and (d) an enemy of my enemy is my enemy.

Once a sign is given to a link, the next question would be: how does that sign is

going to interact with other signs in its local vicinity or with other signs at the net-

work level?

Structural balance theory is the key concept in many applications of the signed

graph theory. It tries to explain how a specic pattern of negative and positive signs

can result in a different kind of relationships. The theory considers the possible

ways in which triads can be signed: triads with an odd number of “+” edges are bal-

anced, while triads with an even number of “-” edges are unbalanced. Imbalanced

graph congurations usually create stress for individuals located on them.

Both the structural balance theory and signed graphs were developed to solve the

subgrouping problem in social psychology. However, they found applications in

other elds as well. They were found very helpful in predicting and explaining

friendships and animosity changes in human communities, which is useful in the

Fig. 1.3 Types of the relationships between two nodes

1.2 Network Measurements

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