### Matlab code for generating randomized networks with the same degree sequence and edge weight distribution as a target network and approximately the same edge length distribution and length-weight relationship.

*fcn_match_length_degree_distribution.m*

**Q: **When should I used this function?

**A: **If your application requires a rewiring-based null model that preserves basic spatial and structural properties.

### References:

### Betzel & Bassett (2017). The specificity and robustness of long-distance connections in weighted, inter-areal connectomes.

### Matlab code for generating consensus communities (requires the *genlouvain* package) from a set of pre-computed partitions.

*fcn_consensus_communities.m*

**Q: **When should I used this function?

**A: **When you've generated many partitions using modularity maximization or infomap and want a semi-principled, semi-supervised approach for getting an "average" partition.

**Note: **There are other nice software packages for generating consensus networks, e.g. work from Santo Fortunato & Olaf Sporns.

### References:

### Betzel & Bassett (2017). The specificity and robustness of long-distance connections in weighted, inter-areal connectomes

### Betzel et al (2017). The modular organization of human anatomical brain networks: Accounting for the cost of wiring [link].

### Betzel et al (2017). Inter-regional ECoG correlations predicted by communication dynamics, geometry, and correlated gene expression [link].

### Matlab code for estimating time-varying connectivity with tapered window.

*fcn_taper.m*

**Q: **When should I used this function?

**A: **It's just another way of estimating windowed functional connectivity. The difference is that it discounts distant (in the past) fluctuations.

### References:

### Betzel et al (2016). Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks [link]