Ranking by Loops: a new approach to categorization
@inproceedings{Kerrebroeck2008RankingBL, title={Ranking by Loops: a new approach to categorization}, author={Valery Van Kerrebroeck and Enzo Marinari}, year={2008} }
We introduce Loop Ranking, a new ranking measure based on the detection of closed paths, which can be computed in an efficient way. We analyze it with respect to several ranking measures which have been proposed in the past, and are widely used to capture the relative importance of the vertices in complex networks. We argue that Loop Ranking is a very appropriate measure to quantify the role of both vertices and edges in the network traffic.
4 Citations
Ego-centric Graph Pattern Census
- Computer Science2012 IEEE 28th International Conference on Data Engineering
- 2012
This paper proposes and study ego-centric pattern census queries, a new type of graph analysis query, where a given structural pattern is searched for in every node's neighborhood and the counts are reported or used in further analysis.
Approximating the Number of Network Motifs
- Computer ScienceInternet Math.
- 2009
Several algorithms with time complexity O(((3e) k · n · |E| · log )/∊2) that approximate for every vertex the number of occurrences of the motif in which the vertex participates are presented.
Declarative Cleaning, Analysis, and Querying of Graph-structured Data
- Computer Science
- 2013
This dissertation develops declarative methods to perform cleaning, analysis and querying of graph-structured data efficiently, and introduces 'ego-centric pattern census queries', a new type of graph analysis query that supports searching for structural patterns in every node's neighborhood and reporting their counts for further analysis.
An Improved Model for Effectiveness Evaluation of Missile Electromagnetic Launch System
- Computer ScienceIEEE Access
- 2020
An improved model for effectiveness evaluation is established that takes the availability-dependability-capability (ADC) model as the basic evaluation framework and verifies the effectiveness of the model.