Network Motifs

apricotpigletAI and Robotics

Oct 19, 2013 (3 years and 10 months ago)


Network Motifs

Zach Saul

CS 289

Network Motifs: Simple Building Blocks of Complex Networks

R. Milo et al.

Network Models

Interactions are represented as directed
nodes (as presented in class)

Example problems include gene networks,
neural nets, ecological models and
computer networking models

Network Motifs

Patterns that appear more often in real
networks than in randomly generated

Many notions of a random network

Naïve algorithm

Renyi random graphs

Scale free networks

Even more specialized?

Random Graphs

Three node motifs

Preserve degree for each node

Four node motifs

Preserve degree for each node

Preserve the number of three node motifs

Example Motif


Using brute force, searched target network for
every possible subgraph, counting results

Similarly, searched random network

Motifs are patterns that occur greater or equal
number of times in random networks more than
1% of the time.


Results (cont.)

Gene/Neural Net Analysis

The nematode neural net and the gene net
both contain similar structures

Feed forward


Both are information processing networks
with sensory and acting components

Sensory neurons/transcription factors
regulated by biochemical signals

Motor neurons/structural genes

Food Web Analysis

Food Webs do not show feed

Suggests that direct interaction between
species at a separation of two layers selected
against (e.g. Omnivores)

parallel suggests that prey of same
predator share prey

Electronic Circuit Analysis

Circuits can be classified by function using
network motifs

Circuits from benchmark set showed
different motifs for each functional class

Some info processing circuits show similar
motifs to biological info processing circuits

Web Analysis

Network of hyperlinks

Many more bidirectional links

Motifs indicate a design that allows the
shortest path among sets of related pages


Technique robust to data errors

Motifs can indicate common function

..or could indicate similar evolutionary

Scalability to other types of networks

Scalability to larger subgraphs difficult