Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

Updating and downdating techniques for optimizing network communicability

Abstract: The total communicability of a network (or graph) is defined as the sum of the entries in the exponential of the adjacency matrix of the network, possibly normalized by the number of nodes. This quantity offers a good measure of how easily information spreads across the network, and can be useful in the design of networks having certain desirable properties. The total communicability can be computed quickly even for large networks using techniques based on the Lanczos algorithm. In this work we introduce some heuristics that can be used to add, delete, or rewire a limited number of edges in a given sparse network so that the modified network has a large total communicability. To this end, we introduce new edge centrality measures which can be used to guide in the selection of edges to be added or removed. Moreover, we show experimentally that the total communicability provides an effective and easily computable measure of how "well-connected" a sparse network is.

Pairwise Co-betweenness for Several Types of Network

Abstract: Vertex betweenness centrality is essential in the analysis of social and information networks, and it quantify vertex importance in terms of its quantity of information along geodesic paths in network. Edge betweenness is similar to the vertex betweenness. Co-betweenness centrality is a natural developed notion to extend vertex betweenness centrality to sets of vertices, and pairwise co-betweenness is a special case of co-betweenness. In this paper, we analysis the pairwise co-betweenness of WS network model with the different reconnection probability which including rule, smallworld and random network. The pairwise co-betweenness value is represented by several different ways, and it shows some regularity with changing reconnection probability of each edge in WS network model. Meanwhile, for communitystructure network, we obtain vertex-induced subgraph with the highest betweenness vertices, and the edge-induced subgraph with the highest pairwise co-betweenness edges. We demonstrate that the edge of cross-groups is consistent with the edges with top incidental pairwise co-betweenness. Finally, further illustration to the interaction of pairwise co-betweenness and network structure is provided by a practical social network.

Social Network Analytics: Computational Research Methods and Techniques

Abstract: Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networksDiscusses various methods of visualizing, modeling and tracking network patterns, organization, growth and changeCovers the most recent research on social network analysis and includes applications to a number of domains

Simulation of network-enabled radar systems to assess the value of jamming in a general radar topology

Abstract: This paper explores the concept of a system of diverse and independent radar systems and the corresponding network theory in a network-centric warfare environment. Matlab simulations are developed to demonstrate the concepts and quantify the network metrics discussed for important netted radar configurations. The effect of electronic attack is also addressed. Simulation results to demonstrate the signal-to-noise ratio performance with and without network synchronization are shown, including the degradation due to electronic attack.

Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks

Abstract: Social networks are sub-set of complex networks, where users are defined as nodes, and the connections between users are edges. One of the important issues concerning social network analysis is identifying influential and penetrable nodes. Centrality is an important method among many others practiced for identification of influential nodes. Centrality criteria include degree centrality, betweenness centrality, closeness centrality, and Eigenvector centrality; all of which are used in identifying those influential nodes in weighted and weightless networks. TOPSIS is another basic and multi-criteria method which employs four criteria of centrality simultaneously to identify influential nodes; a fact that makes it more accurate than the above criteria. Another method used for identifying influential or top-k influential nodes in complex social networks is Heat Diffusion Kernel: As one of the Topological Diffusion Models; this model identifies nodes based on heat diffusion. In the present paper, to use the topological diffusion model, the social network graph is drawn up by the interactive and non-interactive activities; then, based on the diffusion, the dynamic equations of the graph are modeled. This was followed by using improved heat diffusion kernels to improve the accuracy of influential nodes identification. After several re-administrations of the topological diffusion models, those users who diffused more heat were chosen as the most influential nodes in the concerned social network. Finally, to evaluate the model, the current method was compared with Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS).