In the context of expert systems approach, the problem of community detection can be afford as a clustering model for networks. In this respect, a way to measure the community structure is the clustering coefficient. Such a quantity is based on the number of existing triangles around the nodes over the theoretical ones. To the best of our knowledge, scarce attention has been paid to the fictiti...

BACKGROUND It has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabili...

Given a set of moving object trajectories, we show how to cluster them using k-means clustering approach. Our proposed clustering algorithm is competitive with the k-means clustering because it specifies the value of “k” based on the segment’s slope of the moving object trajectories. The advantage of this approach is that it overcomes the known drawbacks of the k-means algorithm, namely, the de...

If each node of an idealized network has an equal capacity to efficiently exchange benefits, then the network’s capacity to use energy is scaled by the average amount of energy required to connect any two of its nodes. The scaling factor equals e, and the network’s entropy is ln(n). Networking emerges in consequence of nodes minimizing the ratio of their energy use to the benefits obtained for ...

Online social networks (OSNs) are trendy and rapid information propagation medium on the web where millions of new connections either positive such as acquaintance, or negative animosity, being established every day around world. The links (or sometimes referred to harmful connections) mostly by fake profiles they created minds with ill aims. Detecting suspicious) within online users can better...

We present an approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks, such as the average degree of the nearest neighbors, the clustering coefficient, the "betweenness," the distance between two nodes, and the diameter of a network. All these measures are well established for unweighted networks but have...