ARF

Force-directed

Circular layout

Circular

This is simple, easy to interpret, and easy to set parameters

This is limited to small networks for easy viewing; it has potential for excessive edge crossing

This layout can be used in cases where you have a particular order in mind for the data—by clusters, size, and so on

Concentric layout

Circular

This is good for focusing on a single node within a network

This is not ideal for large diameter networks as graph size increases geometrically

This is useful for featuring a single node at the center and displaying their neighbors in descending order from direct to distant

DAG layout

Tree

Ordering hierarchical data

This is impractical for very large networks

This can be used in cases where you wish to see levels of data in a top to bottom order

Dual Circle layout

Circular

This has the ability to focus on a group of nodes within the larger network

This layout results in very large networks that might create viewing issues

This layout can be used in instances where a second circle is desirable to focus on a limited group of nodes

Force Atlas

Force-directed

This includes many options and has a high level of accuracy

This can be very slow and is not suited to large networks

This layout is useful for network analysis and discovery, and for measuring network behavior

Force Atlas 2

Force-directed

This is faster than original Force Atlas and handles very large networks

This suffers slightly on overall accuracy

This is used as a good tool for network analysis and discovery, and for detecting behavioral patterns

Fruchterman-Reingold

Force-directed

This is accurate, and tends to be easy for viewers

This is very slow and not suited for large networks

This is good for a generalized view of small-to medium-sized networks

Geo layout

Geographic

This uses lat/lon data for geo-based networks

This is limited to geographic data, and must have lat/lon attributes

This can be used with any geo-based data

Hiveplot layout

Radial

This provides a good solution for network hairballs by spreading connections along radial axes

Can be difficult to see interactions within groups along each axis

This is ideal for viewing cross-group interactions in small-to medium-sized networks

Isometric layout

Layered

Adds third (z) dimension to help spread crowded networks

More difficult to determine relative positioning of nodes within the larger network

Useful for cases where the network has natural groupings or layers

Layered layout

Layered

This is an easy way to view a network with distinct layering patterns based on clusters or groupings

This has very few options for setting layer behavior and layout

Useful for simple graph creation where layers are a key part of the story

Maps of Countries

Geographic

This provides a background of countries and regions for use with other networks; this also works with lat/lon overlays

This requires country-level data to be useful

This is used in cases where national affiliations are part of the story—perhaps author networks or research collaborations

Multipartite layout

Multipartite

Minimizes edge crossings, best suited to multitier network structures

This has few options to customize the graph

This is best used when the network data shows linkages between individuals and organizations or other level of aggregation

Network Splitter 3D

Layered

This adds a third (z) dimension to help in viewing crowded networks with natural layers

This separates layers, making it more difficult to perceive the whole network

This is ideal for splitting crowded networks along a specific criteria, such as clusters or groups

OpenOrd

Force-directed

This is very fast, and can handle large networks

This is not highly accurate on smaller networks

This is used for a rapid understanding of large network structure

Radial Axis layout

Radial/Circular

This is flexible, and is a good layout for clustered datasets

This can be challenging with large networks, and is not ideal for viewing intragroup connections

This is ideal for viewing connections across groups

Yifan Hu

Force-directed

This is fast compared to other force-directed algorithms

This lacks separate repulsion and attraction variables

This has an easy to understand approach for rapidly viewing small to medium networks

Yifan Hu Proportional

Force-directed

This handles relatively large networks; and has fast graph creation

This has moderate quality versus other force-based layouts

This has an easy to understand approach for rapidly viewing small-to-medium networks

Yifan Hu Multilevel

Force-directed

This handles very large graphs, fast

Quality is sacrificed as a trade-off for processing speed

Very fast method for viewing large networks