- Development of network science: 1980-2000 1st Milestone - regular vs random networks 2nd Milestone - small worlds 3rd Milestone - scale free networks
- Epistemological discussion
- What do we mean by scaling
- The five laws of urban scaling

Not really new

Graph Theory in the 18th century

Leonhard Euler’s work on small graphs

High degree of regularity: similar degree centrality among different nodes

20th Century: advances in mathematics and statistics

Algorithmic network analysis

Erdős, Rényi, et al. (1960)

Large scale networks with no obvious structure

Nodes degree follows a Poisson distribution: similar degree, close to the average degree

, rare exceptions Representative of real world network?

Source: Watts and Strogatz (1998)

Source: Torres et al. (2009)

… in different fields, from social science to biology

Digitization of data in many different fields + large databases ➔ real world systems as networks

Advances in computer science and in computing

Looseness between different disciplinary boundaries

Reductionist approaches lose ground in favor of holistic research approaches, which try to understand the system as a whole

Small world effect

Bacon number

Short average distances, enabling nodes to reach each other within a few steps

Characteristic of numerous real world networks

Structural characteristic rather than an organizing principle

Even RN networks are characterized by short average distances

Watts and Strogatz (1998) Small-world (SW) model

Coexistence of short average distance with high clustering coefficient

SW networks are located between regular and random networks:

Highly clustered like regular lattices

Small distances like random networks

Node degree distribution is quite similar with the RN and decays exponentially

A set of intensively interconnected local clusters, which gain global connectivity via a few links, which span the entire network linking distant clusters

Nodes in SW networks benefit from the high local connectivity and easy distant communication with remote clusters using the intra-cluster links

Probability of finding a highly connected node decreases exponentially as highly connected nodes are practically absent in RN and SW models

Source: Watts and Strogatz (1998)

**Pros and cons**

Social capital (bridging and bonding)

Disease spread

Real world examples?

Barabási and Albert (1999)

Very few super connected nodes and a vast majority of less connected nodes

SF: nodes degree distribution follows a power law distribution regardless the scale of observation

2 main formation mechanisms:

**growth**: expansion of networks over time**preferential attachment**: growth is not equally dispersed across the nodes; highly connected nodes are more likely to receive new links than the lower degree nodes

An initial difference in the connectivity between two nodes will increase further as the network grows

This is a cumulative —

*rich get richer*— processThe probability \(P(k)\) that a node has a degree \(k\) decays following a power function, with usually \(2 < \gamma < 3\)

\(P(k)≈𝑘^{−\gamma}\)

Power laws in networks are related with the existence of

**both of the above two mechanisms**Later versions of SF models included more realistic options for the network growth

Source: Albert, Jeong, and Barabási (2000)

And finally the power law…

**Pros and cons?**

Efficiency

Resilience

Disease spread prevention

Vulnerability towards targeted attacks

Real world examples?

Both RN and SW have short average distances

RN cannot be included in SW because they lack the high clustering coefficient

SF networks share the short average distance and the high cluster coefficient of SW ones, but the SW are not characterized by the scale-free distribution

All scale free networks display small world properties, while all small-world networks are not necessarily scale free

**1. Complexity Science**

Most studies in the network science domain have a starting point in statistical physics

Stochastic approaches

Underlying probability model which usually follows a power law

*Main objective:*identification of the underlying mechanisms using generative modeling and simulation*Potential risk:*the probability model might not follow a power law mechanism, which is a common assumption

**2. Social Network Analysis**

Sociology and graph theory

Focus on social networks

Extensive utilization of network metrics

**3. Geography and Urban Analytics**

Softer’ approaches

Ex-post empirical modeling for identifying characteristics of theoretical network models in real world networks

Global and local network statistics

Use of network measures in mainstream statistical modeling

Empirical verification of the functions that better explain the node degree distribution (power vs. exponential functions)

Spatial networks, but also dynamic networks

Scale in geography

Scale in math:

\(y(x_i)\) a function of \(x_i\) where \(i\) is a spatial unit

If we scale \(x\) by some scalar \(\lambda\),

**the function scales**if its scaled value is proportional to its previous value: \(y(\lambda x_i) \propto y(x_i)\)\(y(x_i) = x_i^a\)

\(y(\lambda x_i) = (\lambda x_i)^a = \lambda^a x_i^a = \lambda^a y(x_i)\)

➔ Power law

Regularities

Quantitative revolution post WW2

Cultural turn

… today …

As cities grow…

… the number of potential connections increases as the square of population.

\(C = p(p-1)/2 \propto p^2\)

As cities grow in size …

land values decline non-linearly from the centre

Source: Coe, Kelly, and Yeung (2019)

As cities grow…

.. interactions between them decline with increasing distance

Newton law of gravitation

Source: I, Dennis Nilsson, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=3455682

As cities get larger…

… there are less of them

Regularity in the distribution of cities within a country

Empirical observation and quantification

Hierarchical urban systems:

one/a few big cities

more medium size cities

large number of very small cities

Source: O’sullivan (2012)

Rank size rule

\(pop_i = pop_d r_i^{-a}\)

if \(a=1\): Zipf law

\(pop_i = pop_d / r_i\)

Rank size rule

\(pop_i = pop_d r_i^{-a}\)

if \(a=1\): Zipf law

\(pop_i = pop_d / r_i\)

Primary cities above what a Zipf law would predict

Newly industrialised countries

No overall consensus why the rank-size rule holds

Statistical regularity or an underpinning micro-economic process?

As cities grow…

… their average real income (and wealth) increases more than proportionately (Bettencourt et al. 2007)

\(Y_i = Y_0P_i^\beta\)

\(Y_i\): material resources (energy or infrastructure) or social activity (wealth, patents, and pollution) in city \(i\)

\(Y_o\): normalization constant

\(P_i\): population of city i

\(\beta\): exponent

Source: Bettencourt et al. (2007)

Not surprisingly another straight line

Another power law

Source: Bettencourt et al. (2007)

An average urban dweller in the capital, Lisbon, has approximately twice as many reciprocated mobile phone contacts, *k*, as an average individual in the rural town of Lixa.

Source: Bettencourt (2021)

\(Y=3P^b\)

300 random observation

Plotting the results

Economies of scale

\(b<1\) decreasing returns to scale

\(b=1\) constant returns

\(b>1\) increasing returns

SF networks, economies of scale?

Bettencourt (2021):

Cities, of course, do not really have their own dynamics; they depend on decisions made by people, corporations, governments, and others. The aggregate statistics of all their decisions will therefore emerge as key and provide another con- nection to the uses of information in urban science.

Albert, Réka, Hawoong Jeong, and Albert-László Barabási. 2000. “Error and Attack Tolerance of Complex Networks.” *Nature* 406 (6794): 378–82.

Barabási, Albert-László, and Réka Albert. 1999. “Emergence of Scaling in Random Networks.” *Science* 286 (5439): 509–12.

Batty, Michael. 2013. *The New Science of Cities*. MIT press.

Bettencourt, Luı́s MA. 2021. “Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems.”

Bettencourt, Luı́s MA, José Lobo, Dirk Helbing, Christian Kühnert, and Geoffrey B West. 2007. “Growth, Innovation, Scaling, and the Pace of Life in Cities.” *Proceedings of the National Academy of Sciences* 104 (17): 7301–6.

Coe, Neil M, Philip F Kelly, and Henry WC Yeung. 2019. *Economic Geography: A Contemporary Introduction*. John Wiley & Sons.

Erdős, Paul, Alfréd Rényi, et al. 1960. “On the Evolution of Random Graphs.” *Publ. Math. Inst. Hung. Acad. Sci* 5 (1): 17–60.

O’sullivan, Arthur. 2012. “Urban Economics 8th Ed.”

Torres, Sonia H, Marı́a Montes de Oca, Eduardo Loeb, Priva Zabner-Oziel, Valentina Wallis, and Noelina Hernández. 2009. “Isoenzimas de Lactatodeshidrogenasa En El músculo Esquelético de Pacientes Con EPOC.” *Archivos de Bronconeumologı́a* 45 (2): 75–80.

Watts, Duncan J, and Steven H Strogatz. 1998. “Collective Dynamics of ‘Small-World’networks.” *Nature* 393 (6684): 440–42.