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Graphia UI
Gene coexpression graph of GNF mouse tissue atlas
Phlyogenetic tree of all mammals

Graphia is a powerful network analysis platform for big data exploration. With support for massive graphs, fast dynamic layout, beautiful 3D visualisations and powerful graph analytics, it represents a new analysis paradigm.

Core to Graphia’s functionality is its ability to quickly go from any data matrix to a relationship graph, where data structure can be explored and analysed as a whole. It couples cutting-edge visualisation technologies with the latest generation of graph analysis algorithms to provide a highly flexible interface where data can be viewed, manipulated and interpreted.

DNA assembly graph of TPM1 transcript showing expression of two mutually exclusive exons

The platform works using a plugin system and be customised to meet your specific needs. The pages below are designed to help get you going and guide through Graphia's rich and powerful functionality.

See Also:
Use Case Examples Tutorials Input Formats Example Datasets Correlation Analysis

Getting started:


Graphia is platform independent and runs on Windows, MacOS or Linux operating systems.

While Graphia will run on any modern computer, for processing and displaying larger graphs (>5,000 nodes), it is recommended that a dedicated graphics card is used, such as those available from NVidia or AMD.

In short, the bigger the graph you want to render, the better your hardware configuration (CPU, RAM, GPU) will need to be for fast, efficient layout and analysis.


To download the software, please go to Downloads section and follow instructions.

Key concepts in network analysis:

Nodes and Edges

Graphs (networks) can be used to represent many different types of data. A graph can be considered as a sum of its nodes and edges:

  • Nodes, sometimes also called vertices, generally represent different entities. For example, people in a social network, or genes in a biological network. Nodes are generally visually represented by a circle or a sphere. Nodes can also be associated with different attribute data; properties about the the entity they represent. For example, if nodes in a graph represent people, the graph may have attributes for age and gender. A nodes 'degree' is the number of edges associated with it. Edges convey information about relationships between nodes, and thus the entities they represent. These could be physical contacts, transactions, calculated similarities etc.. There are various types of edges:
    • Directed edges imply a one way relationship, for example person A called person B, but not the other way round, or protein A phosphorylates protein B but the opposite does not happen.
    • Undirected edges depict commutative relationships between the nodes, in other words, A is related to B and B is also related to A.
    • Weighted edges can be directed or undirected and have a quantitative value associated with them. This is used to depict concepts such as reliability, strength of association or number of interactions.

Graph Topology and Metrics

A graph’s structure or ‘topology’ is dependent on the source of data it represents. A graph is a powerful visual representation of the underlying structure of the data it represents. It can be exploited to using analytical techniques to group nodes, for example by using clustering, or by finding highly connected nodes. Common graph metrics that describe either a graphs structure or a node's properties within the graph include PageRank, distance, betweeness and centrality.

A component is a subset of a graph in which any two nodes are connected by following a sequence of one or more edges. In other words, if nodes are not connected in this way, they are in separate components.


Attribute data refers to information associated with a node or edge. For example, in the case where a node represents a person, it could refer to their gender, ethnicity or socioeconomic status. Attributes can also be calculated based on the graph itself, for example node degree, PageRank or cluster membership.

Attribute values may be used as a basis for visualisation. For example, a numerical attribute's value may be used to scale a node, or in the case where the attribute has a textual value, it can be used to assign a discrete colour.

The graph can also be queried using an attribute's value. For example, by searching a graph for all the nodes that represent men. Attribute data can also be used for analyses - if a cluster of nodes seems to have a lot of men in it, is it statistically enriched in them.

In Graphia attribute data can be loaded using a number of file formats, and can be examined in the 'attribute viewer'.


Graph layout is performed by a ‘layout algorithm’ whose job is position nodes and edges according to a set of defined mathematical rules or forces. The purpose of a layout algorithm is to allow the person viewing the graph to understand a graph's structure at a local or global level. There a number of different types of algorithm, some better suited than others to particular graph topologies.

Force-directed graph layout is a class of graph layout algorithms that calculate the positions of each node by simulating an attractive force between each pair of linked nodes, as well as a repulsive force between the nodes. Typically, the attractive force acts like a spring between the nodes, calculated using Hooke’s law. On the other hand, two nodes are pushed away from each other using Coulomb’s law. Graphia uses its own force-directed dynamic layout algorithm to position nodes in 3D space. It runs continually (unless paused) and seeks to place nodes in their optimal position for visualisation. This means should nodes or edges be added or removed, the graph's layout will adjust accordingly. The parameters for layout can be changed on the fly, thereby adjusting the appearance of graph to suit an individual’s preference.


Graph transformation is a process by which a graph is modified to produce a new graph, or create new attribute(s). It is central to Graphia's core functionality. Transforms include such processes as a simple edge filter, whereby weighted edges below a certain threshold are removed, or branch pruning algorithms where a graph is reduced to only include cycles. Graphia has a sophisticated and flexible interface for transforming graphs on-the-fly.


Graphs are an inherently visual medium with which to explore data, allowing one to understand data structure at a global or local level. However, they can also be used as a medium on which other data can be overlaid and explored visually. Text can by overlaid on a graph, providing the names of nodes or the weight of an edge. Colour, shape and size can also be used to visually distinguish between nodes and edges with different properties, potentially enriching any graph visualisation.

Graphia possesses a sophisticated and flexible interface for visualising graph data.

Distribution of Pearson correlation values based on randomised or real (observed) data. The use of an r threshold selects statistically significant relationships whilst excluding those that occur by random chance.
Distribution of correlation values.

Correlation analysis

Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two continuous variables. This particular type of analysis is useful when an analyst wants to establish if there are possible relationships between variables. It is applicable to a wide variety of data types, from share prices to gene expression data; in fact any numerical matrix, the bigger the better.

If correlation is found between two variables it means that when there is a systematic change in one variable, there is also a systematic change in the other; the variables alter together over time or in individual sets of measurements. Correlations can be either positive or negative.

  • Positive correlation exists if one variable increases simultaneously with the other, i.e. the high numerical values of one variable relate to the high numerical values of the other.
  • Negative correlation exists if one variable decreases when the other increases, i.e. the high numerical values of one variable relate to the low numerical values of the other.

However, it should be noted that correlation is not causation, because other variables (not measured) may have impacted on the results.

Graphia relies on the Pearson correlation coefficient for graph-based analyses of numerical data. Pearson correlation measurements range between +1 and -1. +1 indicates the strongest positive correlation possible, and -1 indicates the strongest negative correlation possible. Therefore the closer the coefficient to either of these numbers the stronger the correlation of the data it represents. On this scale 0 indicates no correlation, hence values closer to zero highlight weaker/poorer correlation than those closer to +1/-1. When using correlation as a basis for graph analyses the use of a threshold value selects statistically significant relationships (edges) whilst excluding those that occur by random chance. It should be noted the narrower the data set, i.e. number of points over which a variable is measured, the more likely things are to be correlated by chance. Calculating a correlation graph is a computationally intensive process that grows quadratically () with the number of data rows. It can be a very big calculation and Graphia is designed to make this easy and fast for large high dimensional datasets.

Click here for a detailed guide to correlation analysis using a numerical matrix.

In principle other correlation measures could be employed for graph-based analyses.

Cluster Analysis

Cluster analysis or clustering is the task of dividing a collection into discrete groups of related objects, which are referred to as clusters. In the graph based case, clusters form groups of nodes. It is a main task of exploratory data mining, and a common approach used in many fields for pattern finding. Cluster analysis refers to the general task to be solved, not a specific algorithm. In the context of a graph, the aim is define these clusters based on the prevalence of high connectivity, which infers a degree of similarity. The parameters of a clustering algorithm can generally be modified so that clustering is more or less granular. Once defined clusters can also be explored for their enrichment of nodes with a particular attribute.


In tool tutorial

The first you open Graphia, the tool will run a brief tutorial to provide a new user with the basic functionality.

Once closed you may reopen the tutorial by going to the menu Help -> Show Tutorial

Video tutorials

View our introductory video tutorial series here

Input formats

For a detailed look at Graphia input formats and how to load them please view the dedicated input formats page

In order to use Graphia, data must be imported in the correct format. A wide range of standard and non-standard file formats are available for data input:

Numerical Matrix (.csv, .tsv)

Click here for a detailed guide to correlation analysis using a numerical matrix

Graphia can create a graph structure by calculating a correlation matrix from a numerical data table.

Adjacency Matrix (.csv, .tsv)

Graphia can open matrices where cells are separated by tab or comma. The file extension should be either .csv, or .tsv.

Matrices of similarity measures calculated by whatever method appropriate and saved as .csv file can be loaded directly into Graphia. On import, specify Matrix CSV File when prompted.

Pairwise formats (.txt)

The simplest graph format Graphia supports is pairwise text.

Pairwise text format is very simple way to define a graph. Each line specifies two node names that will be connected by an edge. There is no support for additional attributes.

Biolayout (.layout)

This is a simple but non-standard file format originally used by BioLayout Express3D and still used by Graphia Pro to import and export graphs.

BioPAX OWL ontology (.owl)

Biological Pathway Exchange (BioPAX) is a standard format for sharing biological pathway structures, based on the OWL format. Graphia enterprise supports BioPax Level 3 OWL files.

JSON Graph (.json)

JSON Graph is a specification for the definition of Graphs utilising the widely popular JSON format.

GraphML (.graphml)

GraphML is an XML-style graph format. Graphia supports the loading of GraphML files.

Graph Modelling Language (.gml)

GML is a hierarchical text format similar to a simplified JSON. Graphia can load GML files.

MATLAB Data file (.mat)

Graphia Enterprise supports loading 2D adjacency matrix/array variables exported from MATLAB.

For a detailed look at Graphia input formats and how to load them please view the dedicated input formats page

Example data

Go to our Example Data page.



Graph Layout Menu and Layout Settings

The layout algorithm in the Graphia uses a force-directed algorithm to dynamically position nodes within the graph. A limit is set on the distribution of forces within the graph to determine when the graph is at a minima. Some graphs never reach a stable-minima and will eventually stop laying out once Graphia has detected no change over a large time period. Once a suitable minima has been detected, it is not possible to restart the layout without modifying the structure of the graph. When the graph structure is changed, due the removal or addition of node(s) or edges, i.e. undergone a transformation, layout will resume.

To Pause and Resume the layout use the "Pause" key.

To change the force model for layout go to Layout -> Settings and use Local or Global sliders (bottom left of graph window) to dynamically change appearance of a graph


Once a file has been loaded into Graphia the network graph will be displayed.


  • Graphia User Interface - annotated
    Rotate the graph, press the left mouse button and drag.
  • Translate the graph (move up, down, left, right), press right mouse and drag.
  • Zoom in or out, rotate mouse wheel.
  • Focus on a specific node, double click it.
  • Select Single Nodes in the graph, press left mouse button on node.
  • Select Multiple Nodes in the graph, press and hold Shift, then drag a selection rectangle around nodes.
  • View a Component (multi-component graphs only) double click left mouse button on a node in the component of interest.
  • Exit Component Mode and to return to overview mode, click the back arrow at the bottom of the screen.
  • Delete node(s) by selecting them as above, then click the delete icon in the toolbar.

Setting visual preferences

The Options dialog (accessible from the Edit menu) allows the user to change various defaults in accordance with their preferences.

The Appearance tab allows a user to change the default sizes and colours of various graph elements, and the font used on the main graph display.

The Transition Time slider determines how quickly a graph will transform from one state to another and Minimum Component Radius alters the relative size of components in a multi-component graph layout.

The Misc tab contains other unrelated options. If Focus Found Nodes is checked Graphia will move the display to focus on the result of a find operation. If Switch To Component Mode when Finding is selected, the containing component of the find result will be automatically focused. Web Search Url allows the user to customise the website that is opened when right clicking a node and selecting Search Web. In this context, %1 is substituted for the selected node name(s). Maximum Undo Levels refers to the number of steps that can be reversed by clicking on the undo button. Using a smaller value will reduce the amount of memory used, at the expense of some flexibility in how far back changes can be undone.

We provide help within the tool but an experienced user may wish to Disable Extended Help Tooltips.


There are three ways to search:

  1. Find (Ctrl+F): Search the graph's node attributes for some term.
  2. Advanced Find (Ctrl+Shift+F): Search the graph for a term within a specific attribute.
  3. Find By Attribute Value (Ctrl+H): Select nodes where an attribute has a specific value.

When node(s) match the search term they will be highlighted in the graph.

Transformation Options

Add Transform Dialog

Graphia provides an interface offering a wide range of options to transform graphs based on the attributes of nodes and edges or the graph as a whole.

The Transform List is positioned at the top right of the graph display window. When selected the Add Transform dialog box will appear.

The available graph transforms are subdivided into categories:

  • Analyses has transforms that create new attributes using particular algorithms. These attributes can subsequently be visualised or used as a basis for further transformation.
  • Attributes has transforms that take existing attribute values and create new ones. The results of these transforms are often most useful for the purposes of visualisation.
  • Filters allow elements of the graph to be removed or retained based a supplied condition.
  • Transforms describes additional processes which do not neatly fit into the other categories.

For a detailed description of individual transforms please go to the Transforms page.

Upon selecting a transform, a short description is provided that summarises the process involved. The remainder of the dialog is filled with options pertaining to the transform in question. In the case of a filter, a condition is required, whereas for other transforms, various other settings are displayed.

Active Transforms List displayed on Graph Display Window with options to change (hamburger icon)

After clicking OK the transform is calculated and applied to the graph. Where this results in a change in the number of nodes or edges the graph will automatically adjust to show the new topology.

Active transformations will be show in the top right hand corner of the graph display window above the Add Transform button (bottom figure).

Transforms are shown in the order they are carried out (top-to-bottom) and may be dynamically adjusted by use of a slider or dialog box. The order in which transforms are applied may be changed by holding down left mouse button over hamburger icon and moving transform up or down.

The arrow next to the Add Transform button hides the transform list.

Visualisation dialog

Visualisation Options

Graphia has a range of options to superimpose information on the graph based on attributes of nodes and edges. The Add Visualisation button is positioned at the bottom right of the graph display window. When selected the Add Visualisation dialog box will appear.

Select an Attribute then decide whether you wish it to be displayed as text, size or colour. Then press OK. In the case of figure shown on the right, user has chosen to select the edge attribute 'Pearson correlation' and to 'Colour' edges according to edge weight. Details of the visualisation will be shown on the the bottom right of the graph display, below the Add Visualisation button.

Once applied to a graph a visualisation can be changed. To change the:

  • Attribute being displayed click the attribute name and select another from the list.
  • Colour Scheme click on the key to select or change its style.
    • Gradient click the hamburger icon to invert how the colours are applied.
    • Palette click the hamburger icon to select whether colours are assigned alphabetically, by value or by quantity - how frequently the values occur.

Multiple visualisation schemes may be selected at once but later schemes will override earlier visualisations when they compete.

The arrow next to the Add Visualisation button hides the visualisation list.

Cluster Analysis

Clustered graph, with a cluster highlighted together with the profile of the nodes selected. Cluster dialog inset.

The aim of a cluster analysis is to group a set of objects, in the context of a graph - nodes, in such a way that nodes in one cluster are more similar to each other than to those in other clusters. Once clustered a graph can be viewed as a set node clusters. In the context of a correlation graph, clustering can be used as a means to discover and explore patterns in the source data.

Graphia uses the MCL algorithm, a fast and tunable approach to graph clustering. It determines clusters based purely on their connectivity.

To perform a cluster analysis, left click Add Transform on the top right of the graph window, and when dialog appears select MCL Cluster on the left and then click OK. Clusters will be shown on the graph, the largest cluster being Cluster 1, subsequent clusters being of decreasing size, and nodes belonging to the same cluster possessing the same colour. The colour assignments of the largest clusters will appear on the bottom right of the screen and can be changed by clicking on the key.

The granularity setting of clustering can be adjusted using the slider provided, or a value entered directly.

Once clustered a dataset can be rapidly explored, scrolling through the clusters using the attribute viewer (Ctrl+H) to examine the data patterns therein and clusters can also be explored for their enrichment of nodes with particular attributes (below).

Enrichment analysis results window. Left table of results, right heatmap showing overview of statistical hits.

Enrichment Analysis

Although it is not strictly speaking a network analysis tool, enrichment analysis is often used in combination with topological network analysis.

There are different varieties of this type of analysis, but in its most basic form, enrichment analysis is used to infer which annotations are over-represented in a selection of network. Graphia uses a type of a hypergeometric (Fisher's exact) test to answer the following question:

"When sampling X nodes (test set) out of N nodes (input data), what is the probability that x or more of these nodes have an attribute C, that is shared by n of the N nodes in the reference set."

In other words, given the frequency of a given attribute in the dataset, what the chance of finding so many nodes in a selection (cluster) with that attribute. It helps explain what a cluster may represent based on prior knowledge.

To perform an enrichment analysis, select Analyses -> Enrichment from the menu. When presented with the first dialog select the first attribute class (A) and in the second window the second attribute class (B). The tool will then calculate whether values for attribute B are enriched in nodes in with attribute A and display the results.

The results table of enrichment scores. On the left are the tabulated values for enrichment (<0.05) showing the observed vs. expected and results of Fisher's exact test of those terms over-represented. On the right is a heatmap of the results given in the table; clicking on one of the areas in the heatmap will highlight the corresponding data in the table.

Icons above the table offer options to also show under represented values, show/hide heatmap, delete results and perform another analysis.

Attribute Table and Data Plot Viewer

Attribute table functions

The attribute viewer is where attribute data relevant to the current node selection is displayed. It is docked beneath the main graph display by default, but it can also be detached and shown in a separate window if desired.

Node Attribute Table: The node attribute table window provides details of all selected nodes; their names and all associated attribute information, both imported attributes and those that are calculated or created by transforms. When nodes are first selected, all rows in the table are also selected. Clicking on a single row will highlight that associated node in the graph without deselecting others. Use the Arrow keys to scroll the selection up or down. To select multiple consecutive rows hold down Shift+click or Ctrl+click to select individual rows.

The attribute has a toolbar with icons for common tasks. Their functions may be determined by hovering the mouse and observing the tooltip displayed.

Attribute table and data plot window

Data Plot: When doing a correlation analysis, the raw data for selected nodes is also plotted. This allows a user to compare the pattern of data associated with a node selection. It has been design to produce publication ready plots.

The Plot menu provides options to alter the plot display. In particular:

  • Select Visible Column Attributes sometimes correlation datasets have information associated with columns of data. These are referred to as Column Attributes. Clicking this option allows the user to choose which of these to display. Once selected, click the icon in the top left of the plot to confirm.
Saved plot from data plot window.
  • Scaling display data rescaled by the specified means.
  • Averaging instead of showing individual lines for each node, show an average of the selection, using the specified means.
  • Dispersion show standard measures of dispersion.
  • Include 0 in Y-axis by default the Y-axis of the plot is chosen to maximise the available range based on the data. Selecting this option forces zero to be included in the plot.
  • Sort By the order in which to sort of the columns.


After inspecting the graph, you may wish to keep a record of node selections that are interesting. Under the Bookmarks menu select Add Bookmark... (Ctrl+D). After naming the bookmarked selection, you'll be able to be able to easily return to that set of nodes at a later time.


Related nodes may be selected using various options in the Edit menu. In particular:

  • Select Sources of Selection refers to all the nodes whose outgoing edges point to the current selection
  • Select Targets of Selection refers to all the nodes whose incoming edges originate from the current selection
  • Select Neighbours of Selection refers to all the nodes that are directly connected to the current selection

These selection options can also be found in the context menu that is shown by right clicking a node.

Web search

Right clicking a node will show the context menu, including the option to Search [the] Web for that nodes name. The default search engine is Google but this can be changed under the Options Dialog (Misc tab) such that graphs can be linked to a specific online resource.

Export and saving data

During an analysis you may wish to save screenshots of the graph, export lists of nodes and associated attribute data, data plots or analysis results.

  • Screenshots: File -> Save Image As...
  • Export Table: Table -> Export...
  • Data Plots: Plot -> Save Image as...

Once an analysis is complete you can save the resulting graph in a number of formats either by clicking the save icon on the top left of the graph display window or File -> Save.

It is also possible to export the graph into the following common graph formats. Note that some information may be lost in this process.

  • GraphML
  • GML
  • Pairwise text
  • JSON Graph


Problem Possible reason Possible solution
Authentication fails It is possible that your firewall is preventing the software communicating with Kajeka's servers. Try disabling any personal firewalls you may have installed. Failing that, please contact you local IT support.
File will not load The input file is incorrectly formatted or corrupted. Check the input formats page. Check the file for missing data, data outside the normal bounds, bad formatting, etc.. Also check the file extension as some programs e.g. Excel may add the wrong extension when saving.
Rendering of graphs is slow and jerky The graph you are trying to display is larger than your system is able to display effectively, or your graphics drivers may be out of date. Run smaller graphs, limit the graph size with a transform or upgrade system hardware. Install the latest graphics drivers available for your machine configuration.
I tried to load a file and Graphia has stopped responding Some tasks or files may use large quantities of compute resource. Take heed of any warnings suggesting that the resultant graph may be excessively large. Try adjusting parameters in order to generate a smaller graph, or use a computer with more memory.
I experience image quality problems and/or text is oddly sized Your graphics drivers may be out of date. Install the latest graphics drivers available for your machine configuration.

If you are still experiencing problems please email: