A Novel Multiplex Network-based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

This work was supported by National Natural Science Foundation of China under Grant No. 61473203, and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.Peer reviewedPostprin

Reconstruction of Directed Networks from Consensus Dynamics

This paper addresses the problem of identifying the topology of an unknown, weighted, directed network running a consensus dynamics. We propose a methodology to reconstruct the network topology from the dynamic response when the system is stimulated by a wide-sense stationary noise of unknown power spectral density. The method is based on a node-knockout, or grounding, procedure wherein the grounded node broadcasts zero without being eliminated from the network. In this direction, we measure the empirical cross-power spectral densities of the outputs between every pair of nodes for both grounded and ungrounded consensus to reconstruct the unknown topology of the network. We also establish that in the special cases of undirected or purely unidirectional networks, the reconstruction does not need grounding. Finally, we extend our results to the case of a directed network assuming a general dynamics, and prove that the developed method can detect edges and their direction.Comment: 6 page

Comparison of the language networks from literature and blogs

In this paper we present the comparison of the linguistic networks from literature and blog texts. The linguistic networks are constructed from texts as directed and weighted co-occurrence networks of words. Words are nodes and links are established between two nodes if they are directly co-occurring within the sentence. The comparison of the networks structure is performed at global level (network) in terms of: average node degree, average shortest path length, diameter, clustering coefficient, density and number of components. Furthermore, we perform analysis on the local level (node) by comparing the rank plots of in and out degree, strength and selectivity. The selectivity-based results point out that there are differences between the structure of the networks constructed from literature and blogs

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table

Dual-layer network representation exploiting information characterization

In this paper, a logical dual-layer representation approach is proposed to facilitate the analysis of directed and weighted complex networks. Unlike the single logical layer structure, which was widely used for the directed and weighted flow graph, the proposed approach replaces the single layer with a dual-layer structure, which introduces a provider layer and a requester layer. The new structure provides the characterization of the nodes by the information, which they provide to and they request from the network. Its features are explained and its implementation and visualization are also detailed. We also design two clustering methods with different strategies respectively, which provide the analysis from different points of view. The effectiveness of the proposed approach is demonstrated using a simplified example. By comparing the graph layout with the conventional directed graph, the new dual-layer representation reveals deeper insight into the complex networks and provides more opportunities for versatile clustering analysis.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Persistent Homology Guided Force-Directed Graph Layouts

Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets