ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Social media network

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Land transport networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Maritime transport networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Air transport networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Utility networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Real world graph

• Social networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Classical Graph Visualisation and Analysis

• Using sociogram, also know as network graph, to study friendship ties amongst the Wiring Room employees

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A Complete Graph

• A simple undirected graph in which every pair of distinct vertices is connected by a unique edge.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

An un-directed graph

• A simple graph in which every pair of distinct vertices (also known as nodes) are connected by an unique edge.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A directed graph

• Have a clear origin and destination.

• Also known as asymmetric edges.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A weighted graph

• A weighted edge includes values associated with each edge that indicate the strength or frequency of tie. For example, numbers of calls between two staffs.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A weighted graph

• Edges with different thickness are used to represent the monthly calls by staffs.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A Bipartite Graph

• A graph whose vertices can be divided into two disjoint sets U and V such that every edge connects a vertex in U to one in V; that is, U and V are independent sets.

• Equivalently, a bipartite graph is a graph that does not contain any odd-length cycles.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A Bipartite Graph-affiliation networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

A Multimodel Graph

• Social network connecting different types of vertices.
  • For example, a network may connect peers to discussion forums and blog posts they have commented on.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Basic Principles of Graph

Graph Analytics Process Model

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to show how the news are all connected by degrees of separation

For more information: https://blog.embed.ly/visualizing-discussions-on-reddit-with-a-d3-network-and-embedly-e3ac9297bebd#.2fs77r21y

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Using graph visualisation to understand business networks

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to reveal voting patterns among United States senators

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to support network instrution analysis

  • (a) Similarity graph of log entries and (b) Similarity graph of network scans

(a)

(b)

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Networks universities' alumni

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to study the interaction between project team members in a construction project.

Source: Pryke, S.D.”Analysing construction project coalitions: exploring the application of social network analysis”, Construction Management and Economics, (2004), 22. pp. 787-797.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to investigate community structure in protein-protein interaction networks.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation is used to study for multi-modal urban transport systems

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Graph visualisation for global transportation networks study

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• Network graph for visualising the growth of R packages and their relationships

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics in Action

• To learn more, go to Visual Complexity

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

Source of Network Data

• Structured data with node and link fields (for example, phone call)

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

Source of Network Data

• Transaction records (for example, EZLink card)

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

Source of Network Data

• Company directory such as OneSource and opencorporates (https://opencorporates.com/)

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

An undirected link table

• Focus on interaction but not the direction.

  • For example C1 -> C6 and C6 -> C1 are considered as one interaction.

• The table is symmetric.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

A directed link table

• Focus on both interaction and direction.

  • For example C1 -> C6 and C6 -> C1 are considered as two different interactions.

• The table is asymmetric.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

Popular Graph Data File Formats

• GraphML, an XML-based file format for graphs.

• GXL, graph exchange format based on XML.

• Trivial Graph Format, simple text based format.

• GML is another widely used graph exchange format.

• DGML, Directed Graph Markup Language from Microsoft.

• XGMML, an XML-based graph markup language closely related to GML.

• Dot Language, a format for describing graphs and their presentation, for the Graphviz set of tools.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Data

Graph database

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force-Directed Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force-Directed Layout - Fruchterman Reingold layout

• It lays out the graph in a fairly compact circle, and separate components stay within this circle, as opposed to flying away as found in some other layout algorithms.
• The downside to Fruchterman Reingold is that all nodes tend to be the same distance apart, and separate components may not be readily apparent.
• It is highly dependent on the layout before it is run, so a better result may occur after first running one of the other layouts.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force-Directed Layout - Force Atlas layout

• It can produce good layouts pulling strongly connected nodes together and weakly connected nodes apart.
• It may require more iterative experimentation with parameters. If available, try Force Atlas 2, and try adjusting parameters such as “Prevent Overlap,” “LogMode,” and “Scaling.” Scaling adjusts the ratio of the attraction force along the links and the repulsion between nodes.
• It can work well for dense graphs and fully connected graphs where the edges have weights associated with them.
• Sometimes it is less effective when the graph is very sparse, or the graph has many components.
  components.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force-Directed Layout - Yifan Hu layout

• It is somewhat similar to Force Atlas. It can be fast on large data sets.
• Whereas Force Atlas tends to get different results tweaking various parameters, Yifan Hu tends to come up with a similar layout.
• Yifan Hu does not take into account node sizes, edge weights, and so on, in the software package Gephi.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force-Directed Layouts - A comparison

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Force Settings

• Attraction/repulsion adjusting the ratio between attraction/repulsion is one important consideration. This may be set as a single parameter (for example, Relative Strength in Yifan Hu, Scaling in Force Atlas, or both in Gephi). If all the nodes are in a blob, increasing repulsion can help push the nodes apart.
• Gravity is similar to attraction, but it applies to all nodes, even when not connected. Turning up gravity can bring very distant nodes back toward the center, but turning up gravity too high will create a packed circle.
• Edge weight is the strength of the attraction can be the same for all nodes, or it can be based on weight of the edges. Strong weights will pull nodes closer together. Edge weight is an attribute provided with the data. This is perhaps the most important additional attribute to include when you prepare your data.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Node-Only Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Time-Oriented Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Radial Hierarchical Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Tree Hierarchical Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Geographic Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Adjacency Matrix

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

BiPartite Layout

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Chord Diagrams

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Sankey Diagrams

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Hive Plot

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Layout

Hive Plot using d3.js

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Degree

• Degree, the number of direct connections a node has.

• If the network is directed (meaning that ties have direction), then we usually define two separate measures of degree centrality, namely in-degree and out-degree.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Betweenness centrality

• Betweenness is a centrality measure of a vertex within a graph (there is also edge betweenness, which is not discussed here).

• Vertices that occur on many shortest paths between other vertices have higher betweenness than those that do not.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Closeness Centrality

• In graph theory closeness is a centrality measure of a vertex within a graph.
• Vertices that are 'shallow' to other vertices (that is, those that tend to have short geodesic distances to other vertices with in the graph) have higher closeness.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Eigenvector Centrality

• A measure of the importance of a node in a network.
• It assigns relative scores to all nodes in the network based on the principle that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Clustering Coefficient

• A measure on how connected a vertex’s neighbours are to one another.  More specifically, it is the number of edges connecting a vertex’s neighbours divided by the total number of possible edges between the vertex’s neighbour.

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Metrics

Summary of Graph Metrics

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Additional basic visual attributes

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Motif Simplification – Basic concept

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Motif Simplification Methods

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Motif Simplification Methods

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Motif Simplification Methods

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Visual Attributes

Motif Simplification Methods

• NodeXL Implementation of Motif Simplification

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics Toolkits

Point-and-Click Toolkit-NodeXL

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics Toolkits

Point-and-Click Toolkit-Gephi

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics Toolkits

Point-and-Click Toolkit-Cytoscape

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics Toolkits

Web development library - sigma.js

ISSS608 Visual Analytics and Applications

Lesson 10: Graph Analytics

Graph Analytics Toolkits

Web development library - d3.js