In social and information network analysis, ranking has been considered to be one of the most fundamental and important tasks where the goal is to rank the nodes of a given graph according to their importance. For example, the PageRank and the HITS algorithms are well-known ranking methods. While these traditional ranking methods focus only on the structure of the entire network, we propose to incorporate a local view into node ranking by exploiting the clustering structure of real-world networks. We develop localized ranking mechanisms by partitioning the graphs into a set of tightly-knit groups and extracting each of the groups where the localized ranking is computed. Experimental results show that our localized ranking methods rank the nodes quite differently from the traditional global ranking methods, which indicates that our methods provide new insights and meaningful viewpoints for network analysis.

Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.