Traffic jams are reality in many cities and the current ways to deal with the problem always incur costs for citizens. This paper aims to understand the traffic behavior in a regular grid and in a real world network, with uniform and non-uniform demands and different demand loads. To analyze traffic flow, two metrics are used: waiting vehicles and the correlation ratio between the edge occupati...

Betweenness centrality, measuring how many shortest paths pass through a vertex, is one of the most important network analysis concepts for assessing the (relative) importance of a vertex. The famous state-of-art algorithm of Brandes [2001] computes the betweenness centrality of all vertices in O(mn) worst-case time on an n-vertex and m-edge graph. In practical follow-up work, significant empir...

Betweenness centrality (BC) was proposed as an indicator of the extent individual's influence in a social network. It is measured by counting how many times vertex (i.e., individual) appears on all shortest paths between pairs vertices. A question naturally arises to team or group network can be measured. Here, we propose method measuring this bipartite graph comprising vertices (individuals) a...

We present an incremental algorithm that updates the betweenness centrality (BC) score of all vertices in a graph G when a new edge is added to G, or the weight of an existing edge is reduced. Our incremental algorithm runs in O(ν∗ · n) time, where ν∗ is bounded by m∗, the number of edges that lie on a shortest path in G. We achieve the same bound for the more general incremental vertex update ...