Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Analysis and Actions on Graph Data.

Graphs are commonly used for representing relations between entities and handling data processing in various research fields, especially in social, cyber and physical networks. Many data mining and inference tasks can be interpreted as certain actions on the associated graphs, including graph spectral decompositions, and insertions and removals of nodes or edges. For instance, the task of graph clustering is to group similar nodes on a graph, and it can be solved by graph spectral decompositions. The task of cyber attack is to find effective node or edge removals that lead to maximal disruption in network connectivity. In this dissertation, we focus on the following topics in graph data analytics: (1) Fundamental limits of spectral algorithms for graph clustering in single-layer and multilayer graphs. (2) Efficient algorithms for actions on graphs, including graph spectral decompositions and insertions and removals of nodes or edges. (3) Applications to deep community detection, event propagation in online social networks, and topological network resilience for cyber security. For (1), we established fundamental principles governing the performance of graph clustering for both spectral clustering and spectral modularity methods, which play an important role in unsupervised learning and data science. The framework is then extended to multilayer graphs entailing heterogeneous connectivity information. For (2), we developed efficient algorithms for large-scale graph data analytics with theoretical guarantees, and proposed theory-driven methods for automatic model order selection in graph clustering. For (3), we proposed a disruptive method for discovering deep communities in graphs, developed a novel method for analyzing event propagation on Twitter, and devised effective graph-theoretic approaches against explicit and lateral attacks in cyber systems.PHDElectrical & Computer Eng PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135752/1/pinyu_1.pd

Diffusion, Infection and Social (Information) Network Database

Research to analyze diffusive phenomena over large rich datasets has received considerable attention in recent years. Moreover, with the appearance and proliferation of online social network services, social (information) network analysis and mining techniques have become closely intertwined with the analysis of diffusive and infection phenomena. In this dissertation, we suggest various analysis and mining techniques to solve problems related to diffusive and infection phenomena over social (information) networks built from various datasets in diverse areas. This research makes five contributions. The first contribution is about influence analysis in social networks for which we suggest two new centrality measures, Diffusion Centrality and Covertness Centrality. Diffusion Centrality quantifies the influence of vertices in social networks with respect to a given diffusion model which explains how a diffusive property is spreading. Covertness Centrality quantifies how well a vertex can communicate (diffuse information) with (to) others and hide in networks as a common vertex w.r.t. a set of centrality measures. The second contribution is about network simplification problems to scale up analysis techniques for very large networks. For this topic, two techniques, CoarseNet and Coarsened Back and Forth (CBAF), are suggested in order to find a succinct representation of networks while preserving key characteristics for diffusion processes on that network. The third contribution is about social network databases. We propose a new network model, STUN (Spatio-Temporal Uncertain Networks), whose edges are characterized with uncertainty, space, and time, and develop a graph index structure to retrieve graph patterns over the network efficiently. The fourth contribution develops epidemic models and ensembles to predict the number of malware infections in countries using past detection history. In our fifth contribution, we also develop methods to predict financial crises of countries using financial connectedness among countries

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201