Citation-based analysis of literature: a case study of technology acceptance research
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Measuring and predicting the human mobility along the links of a transportation network has always been of a great importance to researchers in the field. Hitherto, producing such data relied heavily on expensive and time con- suming surveying and on-field observational methods. In this work we propose an efficient estimation method for the assessment of the flow through links in trans- portation networks that is based on the Betweenness Centrality measure of the network's nodes. Furthermore, we show that the correlation between those two features can be significantly increased when additional (pre-defined and known) properties of the network are taken into account, generating an augmented Mo- bility Oriented Betweenness Centrality measure. We validate the results using a transportation dataset, constructed using cellular phones data, that contains a high resolution network of the Israeli transportation system. We show that the flow that was measured using this expensive and complicated method can be accurately es- timated using our proposed Augmented Betweenness technique.
Network controllability
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Based on the complex network theory,we present formulae for estimating the maximum of betweenness centrality of edge in a network after the random breakdowns happen.Betweenness centrality of edge is defined as the number of shortest paths traveling through an edge given a communication protocol.The edge possessing the max betweenness tends to be congested in communication process,so providing more precise estimation of edge betweenness can give more exact estimation to the capacity of distributing traffic to a network.Finally,We confirm the formula by simulation analysis.Little attention has been paid to the effect of random breakdown on the edge betweenness,so the proposed formula provides a new solution to estimating edge betweenness and also it can be useful for traffic engineering and network planning.
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Betweenness centrality plays a crucial role in the identification of the critical nodes and links. Last year, we obtain the betweenness identities in connected network and demonstrate that the average shortest distance is determined by the betweenness centrality in connected network. In this paper, we extend the betweenness identities in the undirected networks, whether connected or unconnected networks. The betweenness identities reveal the relationship of the betweenness and the average shortest distances of the connected components in the undirected network.
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Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many today's networks, algorithms that quickly update centrality scores have become a necessity. For betweenness, several dynamic algorithms have been proposed over the years, targeting different update types (incremental- and decremental-only, fully-dynamic). In this paper we introduce a new dynamic algorithm for updating betweenness centrality after an edge insertion or an edge weight decrease. Our method is a combination of two independent contributions: a faster algorithm for updating pairwise distances as well as number of shortest paths, and a faster algorithm for updating dependencies. Whereas the worst-case running time of our algorithm is the same as recomputation, our techniques considerably reduce the number of operations performed by existing dynamic betweenness algorithms.
Dynamic network analysis
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The analysis of real-world systems through the lens of complex networks often requires a node importance function. While many such views on importance exist, a frequently used global node importance measure is betweenness centrality, quantifying the number of times a node occurs on all shortest paths in a network. This centrality of nodes often significantly depends on the presence of nodes in the network; once a node is missing, e.g., due to a failure, other nodes’ centrality values can change dramatically. This observation is, for instance, important when dismantling a network: instead of removing the nodes in decreasing order of their static betweenness, recomputing the betweenness after a removal creates tremendously stronger attacks, as has been shown in recent research. This process is referred to as interactive betweenness centrality. Nevertheless, very few studies compute the interactive betweenness centrality, given its high computational costs, a worst-case runtime complexity of O ( N ∗∗
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Social Network Analysis
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Given the great current interest in European R&D networks, in which organizations from the science and the industry sectors perform joint R&D, we investigate knowledge flows in the European R&D network, as inferred from Framework Programme (FP) data. We make use of the concept of edge betweenness centrality, which assesses the power of a relation based on the load placed on the corresponding network edge. Edges with high betweenness centrality have the greatest load, are strategically positioned, and potentially can act as bottlenecks for the flows. We use this idea to evaluate knowledge flows between organizations in the European R&D network, considering several ways to relate the betweenness centrality at the level of FP project participants to knowledge flows at the NUTS2 regional level. We do so by aggregating betweenness centrality values calculated using bipartite graphs linking organizations to the FP projects in which they participate, condensing inter-organizational centralities to inter-regional betweenness centralities. We determine the most central inter-regional knowledge flows, and consider the implications for knowledge flows in European R&D networks. We model the betweenness centrality by means of spatial interaction models, estimating how geographical, technological, and social factors influence the centralities. The results have meaningful implications to European R&D policy, in particular concerning which region pairs become bottlenecks in the flow of knowledge.
Network theory
Knowledge flow
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