# Networks and graphs, theory thereof

November 24, 2014 — October 20, 2021

Abstract networks as in the bridges of Kaliningrad, graph theory and so on. the prevalence of online social networks in modern life leads us to naturally think of those, but networks provide a natural substrate for a bunch of processes, and passing backwards and forwards between linear-algebraic formalisms and graph formalisms can illuminate both.

## 1 To learn: basic

- What graph theory gets us in understanding factor graphs and other graphical models.
- Relationship between graph theory and clustering, similarity metrics, dimensionality reduction.
- signal processing on graphs. (For that see also matrix factorisation.)
- “Pagerank” et al. See Iterative reputation methods
- graphons and graphexes (Cosma’s intro)
- “Persistent Homology” — what is that?

## 2 To learn: advanced

- specific graph counting based on generating functions (which is a “spectral” method but not normally what one means by spectral methods.)
- use of approximating Fourier inversion/Cauchy path integrals. (the “asymptotic enumeration method”, (McKay and Wormald 1990; Odlyzko 1995) I saw Mikhael Isaev present on work with Brendan McKay and Catherine Greenhill (Greenhill et al. 2016; Isaev and McKay 2016)) on an interesting construction based on concentration and complex martingales, which was surprisingly elementary, using a novel concentration equality based on complex generalisation of McDiarmid and Catoni type concentration inequalities. Lots of fancy keywords in there.

## 3 Spectral methods

## 4 Dynamic graphs

e.g. Volodymyr Miz, Kirell Benzi, Benjamin Ricaud, and Pierre Vandergheynst, Wikipedia graph mining: dynamic structure of collective memory.

## 5 As a topology for other processes

It’s not just nodes and edges and possibly a probability distribution over the occurrence of each. Networks are presumably interesting because they provide a topology upon which other processes occur. And the interaction between this theory and pure driven topology is much more complex and rich. Such models include circuit diagrams, probabilistic graphical models, neural networks, contagion processes reaction networks and others.

Scientists and engineers use diagrams of networks in many different ways. The Azimuth Project is investigating these, using the tools of modern mathematics. You can read articles about our research here:

…You can watch 4 lectures, an overview of network theory, here:

For now, I’m interested in conductance in electrical networks, random walks on graphs and the connection betwixt them. Where can I find out more about that? And how about the connection from those to harmonic functions?

### 5.1 Stochastic Petri Nets

- David A. Tanzer on Petri net programming
- John Baez’s current stochastic Petri network work

## 6 Graph software

## 7 Incoming

Suddenly planar graphs seem interesting

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