We propose a method for the Distributed Assessment of Network CEntrality (DANCE) in complex networks. DANCE attributes to each node a volume-based centrality computed using only localized information, thus not requiring knowledge of the full network topology. We show DANCE is simple, yet efficient, in assessing node centrality in a distributed way. Our proposal also provides a way for locating ...

Relying on random and purposive moving agents, we simulated human movement in large street networks. We found that aggregate flow, assigned to individual streets, is mainly shaped by the underlying street structure, and that human moving behavior (either random or purposive) has little effect on the aggregate flow. This finding implies that given a street network, the movement patterns generate...

Matrices associated with graphs, such as the Laplacian, lead to numerous interesting graph problems expressed as linear systems. One field where Laplacian linear systems play a role is network analysis, e. g. for certain centrality measures that indicate if a node (or an edge) is important in the network. One such centrality measure is current-flow closeness. To allow network analysis workflows...

A hot-issued research topic in the workflow intelligence arena is the emerging topic of “workflow-supported organizational social networks.” These specialized social networks have been proposed to primarily represent the process-driven work-sharing and work-collaborating relationships among the workflow-performers fulfilling a series of workflow-related operations in a workflow-supported organi...

We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a given neighborhood around each node, thus not requiring full knowledge of the network topology. We show that the node centrality ranking computed by DACCER is highly correlated with the node ranking ba...

The use of predefined phrase patterns like: N-grams (N>=2), longest common sub sequences or pre defined linguistic patterns etc do not give any credit to non-matching/smaller-size useful patterns and thus, may result in loss of information. Next, the use of 1-gram based model results in several noisy matches. Additionally, due to presence of more than one topic with different levels of importan...

making an informed decision with regards to a suitable business location or site selection for organizations is becoming challenging for business decision makers globally; and even more challenging in business environment that are saddled with uncertainties. the continues raise of multiple criteria variation of site preferences has also necessitated the application of advanced decision making t...

68 Computer C lustering is a discovery process in data mining. 1 It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. 1,2 These discovered clusters can help explain the characteristics of the underlying data distribution and serve as the foundation for other data mining and analysis techniques. Clustering is ...

Abstract The paper makes two points. First, any theory of knowledge must explain the difference between cases from falsehood (provided there are any) and Gettier where subject relies on reasoning falsehood. Second, closeness-to-the-truth approach to explaining knowledge-yielding knowledge-suppressing falsehoods does not hold up scrutiny.