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… 

Figures from this paper

Approximating the Number of Network Motifs
TLDR
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
TLDR
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
  • Dror Marcus, Y. Shavitt
  • Computer Science
    2010 IEEE 30th International Conference on Distributed Computing Systems Workshops
  • 2010
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
  • Guyue Han, H. Sethu
  • Computer Science, Mathematics
    2016 IEEE 16th International Conference on Data Mining (ICDM)
  • 2016
TLDR
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.
...
...

References

SHOWING 1-10 OF 35 REFERENCES
Network motifs: simple building blocks of complex networks.
TLDR
Network motifs, patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks, are defined and may define universal classes of networks.
Superfamilies of Evolved and Designed Networks
TLDR
An approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks, finds several superfamilies of previously unrelated networks with very similar SPs.
Emergence of scaling in random networks
TLDR
A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Design and statistical properties of robust functional networks: a model study of biological signal transduction.
TLDR
This analysis suggests that robustness against link removals plays the principal role in the architecture of real signal transduction networks and developmental genetic transcription networks.
Evolutionary design of functional networks robust against noise
TLDR
Flow distribution (pipeline) networks, representing a simplification of biological signal transduction systems or a toy model of logistic transportation systems, are investigated, and networks having prescribed output patterns and robust against structural noise are constructed.
From selfish nodes to cooperative networks - emergent link-based incentives in peer-to-peer networks
  • David Hales
  • Computer Science
    Proceedings. Fourth International Conference on Peer-to-Peer Computing, 2004. Proceedings.
  • 2004
TLDR
This work presents initial results from simulations of an algorithm allowing nodes to adapt selfishly yet maintaining high levels of cooperation in both a Prisoners' dilemma and a flood-fill query scenario and appears to emerge its own incentive structure.
Emergent Group Level Selection in a Peer-to-Peer Network
TLDR
Simulation experiments with a simple selfish re-wiring protocol (SLAC) that can spontaneously self-organize networks into internally specialized groups (or ‘tribes’) are described, which are scalable, robust and self- Organizing.
Collaborative Spam Filtering Using E-Mail Networks
TLDR
A distributed spam-filtering system that leverages e-mail networks' topological properties is more efficient and scalable than client-server-based solutions.
Gossip-based aggregation in large dynamic networks
TLDR
This work proposes a gossip-based protocol for computing aggregate values over network components in a fully decentralized fashion and demonstrates the efficiency and robustness of the protocol both theoretically and experimentally under a variety of scenarios including node and communication failures.
SLACER: a self-organizing protocol for coordination in peer-to-peer networks
TLDR
SLACER is a selfish link-based adaptation for cooperation excluding rewiring that self-organizes the network into a robust artificial social network (ASN) with small-world characteristics and high cooperation.
...
...