# Motifs in evolving cooperative networks look like protein structure networks

@article{Hales2008MotifsIE, title={Motifs in evolving cooperative networks look like protein structure networks}, author={David Hales and Stefano Arteconi}, journal={Networks Heterog. Media}, year={2008}, volume={3}, pages={239-249} }

The structure of networks can be characterized by the frequency of different subnetwork patterns found within them. Where these frequencies deviate from what would be expected in random networks they are termed “motifs” of the network. Interestingly it is often found that networks performing similar functions evidence similar motif frequencies. We present results from a motif analysis of networks produced by peer-to-peer protocols that support cooperation between evolving nodes. We were…

## 31 Citations

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.

Identifying Emerging Motif in Growing Networks

- BusinessPloS one
- 2014

A novel research framework of motif identification was proposed, defining critical boundaries for the evolutionary process of networks and a significance metric of time scale and an industrial ecosystem at Kalundborg was adopted as a case study to illustrate the effectiveness and convenience of the proposed methodology.

Efficient Counting of Network Motifs

- Computer Science2010 IEEE 30th International Conference on Distributed Computing Systems Workshops
- 2010

This paper presents an efficient counting algorithms for 4-nodemotifs, and shows how to efficiently count the total number of each type of motif, and the number of motifs adjacent to a node.

StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs

- Computer ScienceAlCoB
- 2015

StreaM, a stream-based algorithm for counting undirected 4-vertex motifs in dynamic graphs is presented and shown to be capable to capture essential molecular protein dynamics and thereby provides a powerful method for evaluating large molecular dynamics trajectories.

Fast Parallel Graphlet Counting for Large Networks

- Computer ScienceArXiv
- 2015

This paper proposes a fast, efficient, and parallel algorithm for counting motifs of size $k=\{3,4\}$-nodes that take only a fraction of the time to compute when compared with the current methods used.

Superfamilies of networks for analyzing the correlations of different flow fields

- Engineering2016 12th World Congress on Intelligent Control and Automation (WCICA)
- 2016

The results suggest that the time series of gas concentration, wind speed and wind direction belong to different superfamilies, and it is found that compared with wind direction signals, the correlations between gas concentration signals and wind speed records are stronger.

Towards Validating Social Network Simulations

- Computer ScienceESSA
- 2013

This paper looks at several social network analysis measures but then turns its focus to techniques that not only consider the position of the nodes but also their characteristics and their tendency to cluster with other nodes in the network – subgroup identification.

Efficient Graphlet Counting for Large Networks

- Computer Science2015 IEEE International Conference on Data Mining
- 2015

This paper proposes a fast, efficient, and parallel algorithm for counting graphlets of size k={3,4}-nodes that take only a fraction of the time to compute when compared with the current methods used, and is on average 460x faster than current methods.

Emergence of Scale-Free Close-Knit Friendship Structure in Online Social Networks

- PhysicsPloS one
- 2012

This work proposes a simple directed network model that captures the observed properties of close-knit friendship structures and derives the local-scale and mesoscale structural properties through rate equation analysis.

Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in Large Graphs

- Computer Science, Mathematics2016 IEEE 16th International Conference on Data Mining (ICDM)
- 2016

This paper presents a new algorithm, called the Waddling Random Walk (WRW), which estimates the concentration of motifs of any size with significantly higher accuracy and higher precision than the current state-of-the-art algorithms for mining subgraph statistics.

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