# 网络科学简介¶

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# FROM SADDAM HUSSEIN TO NETWORK THEORY¶

### A SIMPLE STORY (1) The fate of Saddam and network science¶

• SADDAM HUSSEIN: the fifth President of Iraq, serving in this capacity from 16 July 1979 until 9 April 2003
• Invasion that started in March 19, 2003. Many of the regime's high ranking officials, including Saddam Hussein, avoided capture.
• Hussein was last spotted kissing a baby in Baghdad in April 2003, and then his trace went cold.

• Designed a deck of cards, each card engraved with the images of the 55 most wanted.

• It worked: by May 1, 2003, 15 men on the cards were captured, and by the end of the month another 12 were under custody.
• Yet, the ace of spades, i.e. Hussein himself, remained at large.

# The capture of Saddam Hussein¶

• shows the strong predictive power of networks.

• underlies the need to obtain accurate maps of the networks we aim to study;

• and the often heroic difficulties of the mapping process.

• demonstrates the remarkable stability of these networks

• The capture of Hussein was not based on fresh intelligence
• but rather on his pre-invasion social links, unearthed from old photos stacked in his family album.
• shows that the choice of network we focus on makes a huge difference:

• the hierarchical tree captured the official organization of the Iraqi government,
• was of no use when it came to Saddam Hussein's whereabouts.

• the founder of al-Qaeda, the organization that claimed responsibility for the September 11 attacks on the United States.

2005年9月1日，中情局内部关于猎杀本·拉登任务的布告栏上贴出了如下信息：由于关押囚犯的强化刑讯已经没有任何意义，“我们只能继续跟踪科威特”。中情局自此开始了对科威特长达数年的跟踪，最终成功窃听到了他本·拉登之间的移动电话，从确定了他的位置并顺藤摸瓜找到了本·拉登在巴基斯坦的豪宅，再经过9个月的证实、部署，于2011年5月1日由海豹突击队发动突袭、击毙本·拉登。

# VULNERABILITY¶

DUE TO INTERCONNECTIVITY

• The 2003 blackout is a typical example of a cascading failure.
• 1997, when the International Monetary Fund pressured the central banks of several Pacific nations to limit their credit.
• 2009-2011 financial melt-down

An important theme of this class:

• we must understand how network structure affects the robustness of a complex system.

• develop quantitative tools to assess the interplay between network structure and the dynamical processes on the networks, and their impact on failures.

• We will learn that failures reality failures follow reproducible laws, that can be quantified and even predicted using the tools of network science.

NETWORKS AT THE HEART OF

# Complex¶

[adj., v. kuh m-pleks, kom-pleks; n. kom-pleks] –adjective

• composed of many interconnected parts; compound; composite: a complex highway system.
• characterized by a very complicated or involved arrangement of parts, units, etc.: complex machinery.
• so complicated or intricate as to be hard to understand or deal with: a complex problem.
          Source: Dictionary.com

# Complexity¶

a scientific theory which asserts that some systems display behavioral phenomena that are completely inexplicable by any conventional analysis of the systems’ constituent parts. These phenomena, commonly referred to as emergent behaviour, seem to occur in many complex systems involving living organisms, such as a stock market or the human brain.

Source: John L. Casti, Encyclopædia Britannica

• society
• brain
• market
• cell

# Behind each complex system there is a network, that defines the interactions between the component.¶

• Social graph
• Organization
• Brain
• finantial network
• Internet
• Genes

Behind each system studied in complexity there is an intricate wiring diagram, or a network, that defines the interactions between the component.

# THE HISTORY OF NETWORK ANALYSIS¶

• Graph theory: 1735, Euler

• Social Network Research: 1930s, Moreno

• Communication networks/internet: 1960s

• Ecological Networks: May, 1979.

While the study of networks has a long history from graph theory to sociology, the modern chapter of network science emerged only during the first decade of the 21st century, following the publication of two seminal papers in 1998 and 1999.

The explosive interest in network science is well documented by the citation pattern of two classic network papers, the 1959 paper by Paul Erdos and Alfréd Rényi that marks the beginning of the study of random networks in graph theory [4] and the 1973 paper by Mark Granovetter, the most cited social network paper [5].

Both papers were hardly or only moderately cited before 2000. The explosive growth of citations to these papers in the 21st century documents the emergence of network science, drawing a new, interdisciplinary audience to these classic publications.

# THE EMERGENCE OF NETWORK SCIENCE¶

• Movie Actor Network, 1998;
• World Wide Web, 1999.
• C elegans neural wiring diagram 1990
• Citation Network, 1998
• Metabolic Network, 2000;
• PPI network, 2001

# The universality of network characteristics:¶

The architecture of networks emerging in various domains of science, nature, and technology are more similar to each other than one would have expected.

# THE CHARACTERISTICS OF NETWORK SCIENCE¶

• Interdisciplinary
• Empirical
• Quantitative and Mathematical
• Computational

# Typical Network Science Research¶

• Discovering, Modeling, Verification
• WATTSDJ,STROGATZSH.Collective dynamics of‘small-world’ networks. Nature, 1998, 393(6684): 440–442.
• BARABÁSI A-L, ALBERT R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509-512.

# Typical Math Style¶

Fan Chung & Linyuan Lu, The average distance in random graphs with given expected degree,. PNAS, 19, 15879-15882 (2002).

# Typical Physical Style¶

A.-L.Barabási,R.Albert,H.Jeong Mean-field theory for scale-free random networks. Physica A 272, 173–187 (1999).

# Typical Computer Science Style¶

• Community detection
• Recommendation algorithms

# Typical control sytle¶

Controllability of Complex Networks

Liu Y Y, Slotine J J, Barabási A L. Nature, 2011, 473(7346): 167-173.

# 阅读材料¶

• Barabasi 2016 Network Science. Cambridge
• 汪小帆、李翔、陈关荣 2012 网络科学导论. 高等教育出版社
• 梅拉妮·米歇尔 2011 复杂,湖南科学技术出版社
• 菲利普-鲍尔 2004 预知社会：群体行为的内在法则，当代中国出版社
• 巴拉巴西 2007 链接：网络新科学 湖南科技出版社