What is a social network?
At their simplest level, social networks are graphs of social interactions and personal relationships.
A social network can be easily understood and explored in a graph format, using people as nodes, relationships as edges and additional information (characteristics, preferences, affiliations, etc) as properties. Sometimes other entities can be included as nodes, for example companies, products, groups or organizations.
Why analyze social connections?
Network dynamics dictate the spread of information, news and ideas. They can help identify someone’s tastes, opinions and activity. If we can understand a person’s network, we will have a much deeper knowledge of them than if we assessed them in isolation.
By studying a social network we can find influential people, anticipate peaks in demand for products or services, generate more targeted marketing approaches and predict illegal activity. On a more personal level, we can also build communities, identify vulnerable and isolated people and help people find new connections.
How to visualize Social Networks
This white paper offers more details on the topic of social networks and social network visualization.
Who needs to understand social networks?
Anyone with an interest in understanding humans and their behaviors can benefit from analyzing and visualizing social networks. A few groups and associated use cases are listed below:
Sales and Marketing
- Identify, find and target influential people, including decision makers and thought leaders
- Understand your customer’s aspirations and requirements to better meet their needs (e.g. product recommendation mechanisms)
- Understand content and campaign propagation
Researchers and journalists
- Discover breaking stories as they happen and understand information spread
- Find authoritative experts and well-connected sources
- Research connected individuals pertinent to a story
Government and law enforcement
- Predict criminal activity by monitoring connections between suspects
- Understand gang dynamics, for example discover leaders, followers and new individuals being integrated into a group
- Find new leads of enquiry by mapping known connections between crime
Social networking sites
- Allow users to interact with their connections and discover new ones in an enjoyable and visually engaging way
- Help users understand how they share their data and with whom
- Discover and suggest community structures based on connections and shared interests
How to visualize social connections
Visualizing social networks in an interactive format offers faster and more accurate access to the network analysis:
- Overview – There are certain visual cues, such as size, shape, distance and alignment, that the brain can interpret pre-attentively. Using these techniques, a visualization can provide an almost instant overview of a complex network.
- Zoom and filter – Additional information can be included using techniques such as colour or numbers.
- Details on demand – A final layer of detail can be added using text labels and on-click menus.
Adding interactive features, such as filters, node manipulation, node grouping and expansion, network dynamics and connections can be explored on a micro, meso and macro scale.
Social Network Analysis
A powerful and well thought-out automatic layout is key to ensuring a useful visualization of a social network.
Visualizing networks with KeyLines
Sophisticated functionality can be incorporated into these applications, including:
- Social Network Analysis – Degree, betweenness, closeness, kCores and shortest path
- Network filtering – filter nodes and links based on any filter logic you choose
- Layouts – precise and powerful layouts, including Standard, Structural, Hierarchical and Radial
- Combine nodes – Investigate nodes as well as groups by combining nodes
- Temporal analysis – understand how networks form, evolve and change over time
- Geospatial analysis – visualize your networks on maps, to understand geographic trends
Find out more
For more information, or for a demonstration of how KeyLines could help you understand infrastructure, get in touch.
Our blog posts about social networks
We take a closer look at how we used graph theory to predict France as the 2018 FIFA World Cup winners.
In this blog, we use a New York taxi cab database to show how graph visualization can be useful when working with large and complex datasets.
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