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- 1. Center for Financial Studies at the Goethe UniversityPhD Mini-courseFrankfurt, 25 January 2013Financial NetworksIII. Centrality and SystemicImportance Dr. Kimmo Soramäki Founder and CEO FNA, www.fna.fi
- 2. Agenda for today• Centrality and Network Core• Developing SinkRank• Analyzing and visualizing cross-border banking exposures 2
- 3. Centrality in Networks
- 4. Common centrality metricsCentrality aims to summarize some notion of importance.Operationalizing the concept is more challenging.Degree: number of linksCloseness: distance from/to othernodes via shortest pathsBetweenness: number of shortestpaths going through the nodeEigenvector: nodes that are linked byother important nodes are more central,probability of a random process
- 5. Centrality depends on network process• Trajectory • Transmission – Geodesic paths (shortest paths) – Parallel duplication – Any path (visit a given node once) – Serial duplication – Trails (visit a given link once) – Transfer – Walks (free movement) Borgatti (2005). Centrality and network flow . Social Networks 27, pp. 55–71.
- 6. Path based centrality measures 6
- 7. Closeness• The Farness of a node is defined as the sum of its distances to all other nodes• The Closeness of a node is defined as the inverse of the farness• Needs a connected graph (or component)• Directed/undirected• Weighed/un-weighted 7
- 8. Betweenness Centrality• Measures the number of shortest paths going through a vertex or an arc• Algorithm – For each pair of vertices (s,t), compute the shortest paths between them – For each pair of vertices (s,t), determine the fraction of shortest paths that pass through the vertex in question – Sum this fraction over all pairs of vertices (s,t).• Directed/undirected; Weighed/unweightedFreeman, Linton (1977). "A set of measures of centralitybased upon betweenness". Sociometry 40: 35–4 8
- 9. Calculating BWC and Closeness# Load sample networkloada -file pathnetwork-a.txt[delimiter=tab] -preserve falseloadvp -file pathnetwork-v.txt[delimiter=tab]# Calculate unweighted and undirected Betweenness centralitybwc -direction undirected# Calculate unweighted and undirected Closeness centralitycloseness -direction undirected# Set values for arcscalcap -e [?random:uniform:1,5:123?] -saveas weight# Calculate weighed and undirected Betweenness centralitybwc -direction undirected -p weight -saveas bwc-weighted# Calculate unweighted and undirected Closeness centralitycloseness -direction undirected -p weight -saveas closeness-weighted# Visualizeviz -arrows false -vsize bwc 9
- 10. Cut Edge/ArcCut edge or bridge is an edgewhose deletion increases thenumber of connected componentsTarjan (74) provides a linear timealgorithm
- 11. Cut Points/VerticesCut points are the end vertices ofa cut arc (if their degree is not 1)# Add network „CutPoint to database.addn -n CutPoint -preserve false# Add vertices and arcs to network.adda -a v1-v2adda -a v1-v5adda -a v2-v3adda -a v3-v4adda -a v4-v5adda -a v3-v6adda -a v6-v8adda -a v8-v7adda -a v6-v7# Identify cut arc and vertexcutarccutvertex# Visualizeviz -vcolor cutvertex -vsizedefault 10 -vlabel vertex_id -awidthdefault 2 -arrows false -fontsize 25 -saveas CutPointViz
- 12. Walk based centrality measures 12
- 13. Sample networkAdjacency matrix A B C A 0 1 2 B 1 0 0 C 0 1 0Transition matrix :Right stochastic Left stochastic A B C A B C adda -a A-C -preserve false adda -a C-B A 0 1/3 2/3 A 0 1 0 adda -a A-B adda -a B-A B 1 0 0 B 1/3 0 0 setap -p value -value 1 C 0 1 0 C 2/3 1 0 setap -a A-C -p value -value 2 13
- 14. Degree• Local measure• Can be calculated for all types of networks• Undirected, outgoing and incoming direction• Weighted degree = Strength A B C degree 3 3 2 out-degree 2 1 1 in-degree 1 2 1 strength 4 3 3 out-strength 3 1 1 in-strength 1 2 2 14
- 15. Eigenvector Centrality (EVC)• Connections are not equal, a connection to a more important node is more important• We make centrality (xi) proportional to the average of the centrality (e.g. degree) of i‟s network neighbors: where λ is a constant and A the adjacency matrix (Aij =1 if link i-j exists, and 0 otherwise)• Defining a vector of centralities x=(x1, x2, ..., xn), we can rewrite x=Ax• We see that x is an eigenvector of the adjacency matrix with eigenvalue λ 15
- 16. EVC - Properties• All entries of x are positive for eigenvector associated with the largest eigenvalue (Perron–Frobenius theorem). The entry xi gives EVC for node i.• Adjacency matrix A can also contain weights instead of 0-1 links -> weighed EVC• The graph can be directed (asymmetric A) -> directed EVC• Can contain loops (self-links, Ai=j)• The graph must be strongly connected! Can be calculated only for GSCC. 16
- 17. Markov chains• Markov chains are memoryless random processes that undergo transitions from one state to another• We describe a Markov chain as follows: (66.6%) (100%) We have a set of states, S = {s1, s2,.. sn} (33.3%)• A process starts in one of these states and moves successively from one state (100%) to another. Each move is called a step.• If the chain is currently in state si, it moves to state sj at the next step with a probability denoted by pij• The probabilities pij are called transition probabilities and a matrix T specifying pijs a transition matrix 17
- 18. State probability vector• Let q(t)=(q1(t),, q2(t) , ... , qn(t)) be the state probability vector whose ith component is the probability that the chain is in state i at time t.• Markov chain is fully defined by q(0) and T q(t)=q(t−1)T=q(0)Tt• q(t) is also called the distribution of the chain at time t• Question: at which probabilities do we find a random process at states si when t is large?• An important node would have the process visit it often 18
- 19. Stationary probability vector• A stationary probability vector π is defined as a vector that does not change under application of the transition matrix π= πT• For any – irreducible (~ strongly connected component) – aperiodic (~ process does not visit nodes at determined intervals) – positive-recurrent (~ process re-enters each node eventually) Markov Chain there exists a unique stationary probability vector (Fundamental Theorem of Markov Chains) 19
- 20. Simple way of Calculating• The distribution vector after 1 step is the matrix product, q(0)T• The distribution one step later, obtained by again multiplying by T, is given by (q(0)T)T = q(0)T2.• Similarly, the distribution after t steps can be obtained by multiplying q(0) on the right by T t times, or multiplying q(0) by Tt.• Distribution After t Steps: q(t)=q(0)Tt• EVC = elements of q(t) for a large t• Power iteration -method 20
- 21. Combining iterative and Markov chaininterpretations• The Perron–Frobenius theorem says that in a stochatic matrix, the largest absolute eigenvalue is always 1• Transition matrix can be right (T) or left (T) stochastic• As a result we have: π=Aπ (Eigenvector) π=πT (Markov Chain) π=Tπ (Largest Eigenvector, i.e. 1, of left stochastic transition matrix) 21
- 22. Most networks are not strongly connected• EVC can be calculated only for “Giant Strongly Connected Component” (GSCC)• Due to need for irreducible, aperiodic, positive-recurrent Markov Chain• Solution: PageRank and the Random Surfer" -model
- 23. PageRank• Solves the problem with a “Damping factor” which is used to modify the transition matrix (S) – Gi,j= i,j C• Effectively allowing the random process out of dead-ends (dangling nodes), but (66.6%) (100%) at the cost of introducing error (33.3%)• Effect of A B – Centrality of each node is 1/N (100%) – Eigenvector Centrality – Commonly is used
- 24. Calculating EVC and PageRank# Create sample networkadda -a A-C -preserve falseadda -a C-B A B Cadda -a A-Badda -a B-A EVC 0.375 0.375 0.250setap -p value -value 1setap -a A-C -p value -value 2 PageRank 0.368 0.374 0.258# Calculate weighted and directed EVCevc -p value -saveas EVC PageRank-0 0.375 0.375 0.250# Calculate PageRank (default alpha=0.15)# Note: This relates to 0.85 in slides PageRank-1 0.333 0.333 0.333pagerank -p value -saveas PageRank CheiRank 0.397 0.388 0.215# Calculate PageRank (alpha=0)pagerank -p value -alpha 0 -saveas PageRank-0 CheiRank-0 0.400 0.400 0.200# Calculate PageRank (alpha=1)pagerank -p value -alpha 1 -saveas PageRank-1 CheiRank-1 0.333 0.333 0.333#Calculate same for CheiRankcheirank -p value -saveas CheiRankcheirank -p value -alpha 0 -saveas CheiRank-0cheirank -p value -alpha 1 -saveas CheiRank-1# save results in a csv filesavev -file walkcentrality.csv 24
- 25. Final notes on PageRank/EVC• Undirected vs. Directed – PageRank generally in-direction – out-direction = CheiRank Important and Important Fragile CheiRank• Unweighted vs. Weighted Unimportant Fragile – 0/1 or real values in A/T PageRank 25
- 26. Identifying the core 26
- 27. Maximum Clique• A graph may contain many complete subgraphs ("cliques"), i.e. sets of nodes where each pair of nodes is connected• The largest of these is called Maximum Clique• One way of finding the core# Create random networkrandom -nv 30 -na 120 -preserve false -seed 123# Identify maximum undirected clique# 0 - no clique, 1 - maximum clique, 2... smaller cliquesmaxclique -direction any# Set color property of nodes in clique as redsetvp -p color -value red -e maxclique=1# Visualizeviz -vcolor color -vsizedefault 8 -arrows false 27
- 28. Newman Modularity• Method for detecting modules (also called groups, clusters or communities)• Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. # Create random tree tree -nv 30 -preserve false -seed 123 # Identify communities with Newmans modularity algorithm newman # Visualize viz -vcolor newman -vsizedefault 8 -arrows false Newman, M. E. J. (2006). "Modularity and community structure in networks". PROCEEDINGS- NATIONAL ACADEMY OF SCIENCES USA 103 (23): 8577–8696. 28
- 29. Craig - von Peter Core• Interbank markets are tiered in a core-periphery structure• Determines the optimal set of core banks that achieves the best structural match between observed structure and perfectly tiered structure # Create network with core-periphery structure complete -nv 3 -preserve false -directed false adda -a 00001-00004 adda -a 00002-00005 adda -a 00003-00006 # Calculate core cvpcore # Set color property of nodes in clique as red setvp -p color -value red -e cvpcore=true Ben Craig and Goetz von Peter (2010). Interbank tiering and money center banks, BIS Working Papers No 322. # Visualize 29 viz -vcolor color -vsizedefault 8 -arrows false
- 30. Developing a centrality metric for Payment Systems SinkRankDiscussion Paper, No. 2012-43 | September 3, 2012http://www.economics-ejournal.org/economics/discussionpapers/2012-43 30
- 31. Interbank Payment Systems• Provide the backbone of all economic transactions• Banks settle claims arising from customers transfers, own securities/FX trades and liquidity management• Target 2 settled 839 trillion in 2010
- 32. Systemic Risk in Payment Systems• Credit risk has been virtually eliminated by system design (real-time gross settlement)• Liquidity risk remains – “Congestion” – “Liquidity Dislocation”• Trigger may be – Operational/IT event – Liquidity event – Solvency event• Time scale is intraday, spillovers possible
- 33. Process in payment systemsTransfer along walks 33
- 34. Distance to Sink• Markov chains are well-suited to model transfers along walks• Absorbing Markov Chains give distances: From B 1 To A From C 2 (66.6%) (100%) From A To B From C 1 (33.3%) From A To C From B (100%)
- 35. SinkRank SinkRanks on unweighted• SinkRank is the average distance networks to a node via (weighted) walks from other nodes• We need an assumption on the distribution of liquidity in the network at time of failure – Assume uniform -> unweighted average – Estimate distribution -> PageRank - weighted average – Use real distribution -> Real distribution are used as weights
- 36. SinkRank – effect of weights Uniform PageRank “Real” (A,B,C: 33.3% ) (A: 37.5% B: 37.5% C:25%) (A: 5% B: 90% C:5%) Note: Node sizes scale with 1/SinkRank
- 37. How good is it?
- 38. Experiments• Design issues – Real vs. artificial networks? – Real vs. simulated failures? – How to measure disruption?• Approach taken 1. Create artificial data with close resemblance to the US Fedwire system (BA-type, Soramäki et al 2007) 2. Simulate failure of a bank: the bank can only receive but not send any payments for the whole day 3. Measure “Liquidity Dislocation” and “Congestion” by non-failing banks 4. Correlate 3. (the “Disruption”) with SinkRank of the failing bank
- 39. Barabási–Albert (BA) model• Based on Barabási–Albert (BA) model• The BA algorithm generates random scale-free networks and is based on two forces: growth an preferential attachment: – The network begins with an initial network of m0 (>2) nodes. – New nodes are added to the network one at a time. – Each new node is connected to existing nodes with a probability that is proportional to the number of links that the existing nodes already have.• Instead of links, we generate payments (multiple links between pairs of nodes)• We use lower preferential attachment accumulation than the BA model 39
- 40. Generated data
- 41. Measures• Congestion: duration of delays in the system aggregated over all banks• Liquidity Dislocation: the average reduction in available funds of the other banks due to the failing bank• Disruption: duration-weighted sum of Congestion and Liquidity Dislocation• -> Carry out counterfactual simulations with generated data - failing banks and measuring impact 41
- 42. Distance from Sink vs. Disruption Relationship between Failure Distance and Disruption when the most central bank fails Highest disruption to banks whose liquidity is absorbed first (low Distance to Sink) Distance to Sink
- 43. SinkRank vs. Disruption Relationship between SinkRank and Disruption Highest disruption by banks who absorb liquidity quickly from the system (low SinkRank)
- 44. Implementing SinkRank
- 45. Implementation example available at www.fna.fi
- 46. Blog, Library and Demos at www.fna.fiDr. Kimmo Soramäkikimmo@soramaki.netTwitter: soramaki

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