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When Affinity Meets Resistance
On the Topological Centrality of Edges in Complex Networks
Gyan RanjanUniversity of Minnesota, MN
[Collaborators: Zhi-Li Zhang and Hesham Mekky.]
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Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph
Connectivity within and across partitions
Random detours Overhead
Real-world networks and applications
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Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph
Connectivity within and across partitions
Random detours Overhead
Real-world networks and applications
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Motivation Complex networks
Study of entities and inter-connections Applicable to several fields
Biology, structural analysis, world-wide-web
Notion of centrality Position of entities and inter-connections
Page-rank of Google
Utility
Roles and functions of entities and inter-connections Structure determines functionality
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Cart before the Horse
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Centrality of nodes: Red to blue to white, decreasing order [1].
Western states power grid Network sciences co-authorship
State of Art Node centrality measures
Degree, Joint-degree Local influence
Shortest paths based Random-walks based
Page Rank
Sub-graph centrality
Edge centrality Shortest paths based [Explicit] Combination of node centralities of end-points [Implicit]
Joint degree across the edge
Our approach A geometric and topological view of network structure
Generic, unifies several approaches into one
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Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph Connectivity within and across partitions
Random detours Overhead
Example and real-world networks
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Definitions Network as a graph G(V, E)
Simple, connected and unweighted [for simplicity] Extends to weighted networks/graphs
wij is the weight of edge eij
Topological dimensions |V(G)| = n [Order of the graph] |E(G)| = m [Number of edges] Vol(G) = 2 m [Volume of the graph] d(i) = Degree of node i
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The Graph and Algebra For a graph G(V, E)
[A]nxn = Adjacency matrix of G(V, E) aij = 1 if in E(G), 0 otherwise [D] nxn = Degree matrix of G(V, E) [L] nxn = D – A = Laplacian matrix of G(V, E)
Structure of L
Symmetric, centered and positive semi-definite L U Lambda
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Geometry of Networks The Moore-Penrose pseudo-inverse of L
Lp
where
In this n-dimensional space [2]:
x
x
x
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Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph
Connectivity within and across partitions
Random detours Overhead
Real-world networks and applications
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Bi-Partitions of a Network Connected bi-partitions of G(V, E)
P(S, S’): a cut with two connected sub-graphs V(S), V(S’) and E(S, S’) : nodes and edges T(G), T(S) and T(S’) : Spanning trees T set of spanning trees in
S and S’ respectively
set of connected bi-partitions
Represents a reduced state First point of disconnectedness Where does a node / edge lie?
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S S’
Bi-Partitions and L+
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Lower the value, bigger the sub-graph in which eij lies.
Lower the value, bigger the sub-graph in which i lies.
A measure of centrality of edge eij in E(G):
Bi-Partitions and L+
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Higher the value, more the spanning trees on which eij lies.
[2, 3]
For an edge eij in E(G):
When the Graph is a Tree
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Lower the value, closer to the tree-center i is.
Lower the value, closer to the tree-center eij is.
When the Graph is a Tree
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Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph
Connectivity within and across partitions
Random detours Overhead
Real-world networks and applications
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Random Detours Random walk from i to j
Hitting time: Hij Commute time: Cij = Hij + Hji = Vol(G) [2, 3]
Random detour i to j but through k
Detour overhead [1]
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Recurrence in Detours
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Expected number of times the walker returns to source
Overview Motivation
Geometry of networks
n-dimensional embedding
Bi-partitions of a graph
Connectivity within and across partitions
Random detours Overhead
Real-world networks and applications
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Wherein lies the Core
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The Net-Sci Network
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2. Selecting edges based on centrality
The Western States Power-Grid
|V(G)| = 4941, |E(G)| = 6954
(a) Edges with Le+ ≤ 1/3 of mean(b) Edges with Le+ ≤ 1/2 of mean(c) Edges with Le+ ≤ mean
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Extract Trees the Greedy Way
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2. The Italian power grid
network
Spanning tree obtained through Kruskal’s algorithm on Le
+
Relaxed Balanced Bi-Partitioning
Balanced connected bi-partitioning NP-Hard problem Relaxed version feasible
|E(S, S’)| minimization not required Node duplication permitted
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Summary of Results Geometric approach to centrality
The eigen space of L+ Length of position vector, angular and Euclidean distances
Notion of centrality Based on position and connectedness
Global measure, topological connection
Applications
Core identification Greedy tree extraction Relaxed bi-partitioning
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Questions?
Thank you!
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Selected References [1] G. Ranjan and Z. –L. Zhang, Geometry of Complex Networks
and Topological Centrality, [arXiv 1107.0989].
[2] F. Fouss et al., Random-walk computation of similarities betweennodes of a graph with application to collaborative recommendation, IEEE Transactions on Knowledge and Data Engineering, 19, 2007.
[3] D. J. Klein and M. Randic. Resistance distance. J. Math. Chemistry, 12:81–95, 1993.
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Acknowledgment The work was supported by DTRA grant HDTRA1-09-1-0050 and
NSF grants CNS-0905037, CNS-1017647 and CNS-1017092.
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