Titlebook: Complex Networks and Their Applications VII; Volume 1 Proceedings Luca Maria Aiello,Chantal Cherifi,Luis M. Rocha Conference proceedings 20

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A New Group Centrality Measure for Maximizing the Connectedness of Network Under Uncertain Connectivther than a single node. From this, it can be expected that duplication of nodes covered by the extracted nodes can be eliminated, so that a practical candidate site of evacuation facility can be estimated. We evaluate the effectiveness and efficiency of the proposed method compared with the method

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Walk Prediction in Directed Networksures derived from network analysis. This problem is related to but different from the well-studied problem of link prediction. We apply our model to data from several years of U.S. patient referrals. We present experiments showing that the adoption of network-based features in the BPR framework impr

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Average-Case Behavior of ,-Shortest Path Algorithmse complexity and observed empirical performance. While we also provide a weaker bound of . for sparser graphs with ., we show empirical evidence that the stronger bound should also hold in the sparser setting. We then prove that Feng’s directed .-shortest path algorithm computes the second shortest

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Scaling of Random Walk Betweenness in Networksrototypical or small graph that is generated using the same model. The exact distribution itself depends on the graph model. A normalized random walk betweenness (NRWBC), that counts each walk passing through a vertex only once, is also defined. This measure is argued to be more useful and robust an

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1860-949X ions, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks..978-3-030-05411-3Series ISSN 1860-949X Series E-ISSN 1860-9503

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Stable Mutations for Evolutionary Algorithmsaveraged over the full range of possible sizes .. We consider as a case study the phonological network of English words, and discover two natural word orders (word frequency and age of acquisition) that do a surprisingly good job of rescalably summarizing the lexicon.