https://kops.uni-konstanz.de/bitstream/handle/123456789/5940/variants.pdf;sequence=1

On variants of shortest-path betweenness centrality and their generic computation ☆

State of the aRt personality research: A tutorial on network analysis of personality data in R

Prior research has emphasized the importance of boundary spanners in facilitating the transfer of knowledge between organizational units. The successful transfer of knowledge between organizational units is critical for a number of organizational processes and performance outcomes. The empirical evidence on the success of boundary spanners is mixed, however. Research findings indicate boundary spanners can either facilitate or inhibit the flow of knowledge between organizational units. We develop and test a theoretical argument emphasizing the importance of the broader network context in which boundary spanning occurs. In particular, we consider how tie strength, network cohesion, and network range affect the level of knowledge acquired in cross-unit knowledge transfer relationships. An analysis of knowledge transfer relationships among several hundred scientists indicates that each network feature had a positive effect on the level of knowledge acquired in cross-unit knowledge transfer relationships. Our findings illustrate how network features contribute to the flow of knowledge between organizational units and, therefore, how network context contributes to heterogeneity in boundary-spanning outcomes.

Bridging the Knowledge Gap: The Influence of Strong Ties, Network Cohesion, and Network Range on the Transfer of Knowledge Between Organizational Units

We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611972801.49

Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization.

Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.

/pdf/centrality-estimation-in-large-networks-5d6jpuly7e.pdf

Centrality Estimation in Large Networks

We consider variations of two well-known centrality measures, betweenness and closeness, with a different model of information spread. Rather than along shortest paths only, it is assumed that information spreads efficiently like an electrical current. We prove that the current-flow variant of closeness centrality is identical with another known measure, information centrality, and give improved algorithms for computing both measures exactly. Since running times and space requirements are prohibitive for large networks, we also present a randomized approximation scheme for current-flow betweenness.

/pdf/centrality-measures-based-on-current-flow-5dvzv08330.pdf

Centrality measures based on current flow

Spectral methods are naturally suited for dynamic graph layout, because moderate changes of a graph yield moderate changes of the layout under weak assumptions. We discuss some general principles for dynamic graph layout and derive a dynamic spectral layout approach for the animation of small-world models.

/pdf/dynamic-spectral-layout-of-small-worlds-3vsav6qsfv.pdf

Dynamic spectral layout of small worlds

We settle an open problem mentioned in Diaz, Petit, and Serna: A survey of graph layout problems (ACM Computing Surveys 34:313{356, 2002). Of eight objectives considered in that survey, only the complexity status of minimum vertex bisection is listed as unknown. We show that both minimum and maximum vertex bisection are NP-hard, but polynomially solvable on special graph classes such as hypercubes and trees.

/pdf/vertex-bisection-is-hard-too-5dwrec5w0v.pdf

Vertex Bisection is Hard, too

We present a method for visual summary of bilateral conflict structures embodied in event data. Such data consists of actors linked by time-stamped events and may be extracted from various sources such as news reports and dossiers. When analyzing political events, it is of particular importance to be able to recognize conflicts and actors involved in them. By projecting actors into a conflict space, we are able to highlight the main opponents in a series of tens of thousands of events and provide a graphic overview of the conflict structure. Moreover, our method allows for smooth animation of the dynamics of a conflict

/pdf/summarizing-dynamic-bipolar-conflict-structures-1nosh8uoms.pdf

Daniel Fleischer

Papers

Centrality measures based on current flow

Dynamic spectral layout of small worlds

Vertex Bisection is Hard, too

Summarizing Dynamic Bipolar Conflict Structures

Dynamic spectral layout of small worlds