# Networks' complexity measures with Wolfram Language?

GROUPS:
 Vitaliy Kaurov 10 Votes I would like to measure how complex various networks are. There are certainly many approaches, but I am interested in what we can do with tools available in the Wolfram Language. There are many discussions in the New Kind of Science book about networks. But since its publication Wolfram Language evolved significantly. Imagine we have something more general, like a mixed multi-edge graph: network[v_, e_] := Graph[RandomInteger[{1, v}, {e, 2}] /. {x_Integer, y_Integer} :> RandomChoice[{DirectedEdge[x, y], UndirectedEdge[x, y]}]] SeedRandom[131]; g = network[20, 60] Symmetries should affect complexity, and some general measure should detect that in, say, pure trees or symmetric and/or planar graphs: TreeGraph[RandomInteger[#] <-> # + 1 & /@ Range[0, 100], GraphLayout -> "RadialDrawing"]  GraphData["IcosahedralGraph"] Does anyone know or have any ideas for some complexity measure that we can quickly compute with Wolfram Language? Perhaps @Hector Zenil, @Todd Rowland or @Marco Thiel could point to the right direction. Maybe there were some Wolfram Science Summer School projects about this.
2 years ago
13 Replies
 Todd Rowland 5 Votes Networks are notoriously difficult to measure in their complexity in any satisfactory way. One cannot rely on visualizations like you can with cellular automata. Keep in mind that even strings have no satisfactory measure, but there are measures for any expression like StringLength[Compress[expr]] The ones you can use for strings (with their drawbacks) you can also use for networks, and Wolfram Language is inherently one dimensional so there is, at least from some perspective, little difference when talking about general complexity.Instead of asking for general measures of complexity, it makes more sense to talk about specific types of networks, and what sorts of algorithms construct them, or how they are being used, and custom tailor the method to that.
2 years ago
 Marco Thiel 4 Votes Dear Vitaliy,I know that this does not really answer your question, but regarding symmetries in graphs I found this paper on Symmetry in Complex Networks quite interesting. I have not tried that out in Mathematica; it would be nice to get figure one in that paper for larger graphs. Mathematica does have the Command GraphAutomorphismGroup, which is also explored in this paper. GraphAutomorphismGroup is computationally very expensive (at least as costly as isomorphism problems). GraphAutomorphismGroup[ExampleData["NetworkGraph"]] PermutationGroup[{Cycles[{{32, 33}}], Cycles[{{31, 32}}], Cycles[{{30, 31}}], Cycles[{{29, 30}}], Cycles[{{16, 18}}], Cycles[{{5, 11}, {6, 7}}]}] works fine, but it would be nice to mark the symmetries in the graph directly. This here works very quickly: GraphAutomorphismGroup[ExampleData[{"NetworkGraph", "CoauthorshipsInNetworkScience"}]] but GraphAutomorphismGroup[ExampleData[{"NetworkGraph", "MetabolicNetworkSaccharomycesCerevisiae"}]] hasn't finished yet on my computer. Also Groupoids have been used to classify symmetry in graphs, for example by Ian Stewart. Note that for the directed multigraph that you have defined GraphAutomorphismGroup does not work. Cheers,Marco
2 years ago
 Szabolcs Horvát 5 Votes Somehow I find it very strange that real-world networks would have any non-trivial symmetries. I'll need to take a look at that paper when I have time. For a real-world network (i.e. something having a lot of noise) I would expect the only symmetries to arise either from "exchangeable" degree-1 nodes connected to the same other node, or maybe sometimes from complete subgraphs.As for ExampleData[{"NetworkGraph", "MetabolicNetworkSaccharomycesCerevisiae"}], the Bliss algorithm available through IGraph/M handles this easily: g = ExampleData[{"NetworkGraph", "MetabolicNetworkSaccharomycesCerevisiae"}]; << IGraphM group = IGBlissAutomorphismGroup[g]; These are the generators of the group: PermutationCycles /@ group {Cycles[{{200, 249}, {201, 250}}], Cycles[{{480, 484}, {481, 485}, {1041, 1042}, {1113, 1449}}], Cycles[{{536, 539}, {537, 540}, {610, 615}, {611, 616}, {1262, 1268}, {1263, 1269}, {1264, 1270}}], Cycles[{{541, 557}, {617, 642}, {949, 1271}, {952, 1272}}], Cycles[{{1315, 1438}}], Cycles[{{1497, 1498}}]} BTW there are some interesting papers on how graph symmetries affect synchronization networks: http://arxiv.org/abs/1211.5390EDIT:Indeed if we look at which nodes of this metabolic network participate in the symmetries, they all turn out to be low-degree periphery nodes, as I would expect. Below you can see the neighbourhood of these nodes. In[155]:= vi = Flatten /@ First /@ PermutationCycles /@ group In[156]:= Table[ vv = VertexList[g][[ind]]; HighlightGraph[ NeighborhoodGraph[g, vv, 2], vv ], {ind, vi} ] 
2 years ago
 Marco Thiel 2 Votes Hi Szabolcs,yes, there are indeed quite interesting papers on synchronisation and symmetries in networks. Here are two more of them:Nonlinear Dynamics on Networks - Groupoid formalismand Symmetry groupoids and patterns of synchronyAs a matter of fact, I also enjoyed this one:http://www.tbiomed.com/content/8/1/21By the way, I really need to try out the IGraph/M in Mathematica. Haven't got around to that. But I use several others of your great suggestions, such as MaTex. Somehow I find it very strange that real-world networks would have any non-trivial symmetries. I'll need to take a look at that paper when I have time. The authors say that it is due to the fact that many growing networks are locally tree like. Many networks  for example the internet and the world wide web  are growing' 4 (that is, new vertices are added to the network over time). Generically, any growth process which allows for new vertices to be added to the network one at a time naturally leads to a network with locally tree-like regions. Such locally tree-like areas are common in real-world networks and their presence is important because, while the majority of large graphs are asymmetric, it is common for large random trees to exhibit a high degree of symmetry [23], deriving from the presence of identical branches about the same fork. Thus we expect a certain degree of tree-like symmetry to be present in many real-world networks. In the following sections we determine the extent to which real-world symmetry is locally tree-like. We begin by considering the structure of network automorphism groups. Please tell me what you think after you read it.Cheers,Marco
2 years ago
 This paper shows the relationship between complexity and the graph automorphism group size:Correlation of Automorphism Group Size and Topological Properties with Program-size Complexity Evaluations of Graphs and Complex Networks H. Zenil, F. Soler-Toscano, K. Dingle and A. Louis Physica A: Statistical Mechanics and its Applications, vol. 404, pp. 341–358, 2014.Available online here, and preprint here.
1 year ago
 Hi Vitaliy,What do you mean by complexity? For instance, the complexity of random walks in the network (is this bounded or unbounded?). Or how complicated are the paths between any pair of nodes (the total number of paths is a finite number). Can complexity be associated with robustness (the effort to break the network into two connected components)? If this is the case, then one may look at combinatorial and spectral measures of robustness to elect one as a complexity measure or even create a new one, under this concept. However, this would measure only a kind of edge complexity. For tree-like networks and for networks with a complicated core and leaf vertices this would probably be misleading.
2 years ago
 I meant something like Kolmogorov complexity or similar. I was hoping @Hector Zenil will comment something - he has been doing research in this domain.
2 years ago
 Charlie Brummitt 2 Votes Statistical physicists have focused on random models of networks. You could construct a "null model" that capture some aspect of the network (such as all graphs that have the same degree distribution) and see how different your network is to that null model on certain dimensions (e.g., clustering, motifs, path length distribution, hierarchical structure, or whatever you think might be important for your network). If your network is not statistically significantly different from your null model, then your network is a "simple" as your null model (which may have few parameters and hence is quite "simple"); otherwise, you have a lower bound on the "complexity" of your network.
2 years ago
 Todd Rowland 1 Vote Following up on Charlie's post, there is a close relation to Kolmogorov randomness and other types of randomness, so any of the measures he mentions will satisfy Vitaliy's original question.
2 years ago
 I wonder how relevant this will turn out to be: Mathematician claims breakthrough in complexity theory László Babai, a mathematician and computer scientist at the University of Chicago in Illinois, has developed a mathematical recipe or "algorithm" that supposedly can take two networks—no matter how big and tangled—and tell whether they are, in fact, the same, in far fewer steps than the previous best algorithm. ... Babai has shown that a problem that was thought to be on the harder NP end of the spectrum is closer to the easier P end, instead.