Discover the Influential and Invisible People Networks, and the Flow of Power, Trust, Communication, in your Company Social Network Analysis, and teamworking dynamics Network
Here’s an expanded and optimized explanation of Social Network Analysis (SNA):
**What is Social Network Analysis (SNA)?**
SNA is a powerful methodology used to study the structure and dynamics of networks. It goes beyond simply identifying who knows whom. Instead, it focuses on the relationships, interactions, and flows of information, resources, or influence between connected entities.
**Key Components of SNA:**
* **Nodes:** These represent the individual entities within the network, such as people, groups, organizations, computers, or even websites (URLs).
* **Edges:** These represent the connections or relationships between nodes. Edges can be directed (showing the flow of information or influence) or undirected (simply indicating a connection).
* **Attributes:** Nodes and edges can have additional information associated with them. For example, a node representing a person might have attributes like department, role, or expertise. An edge might have an attribute indicating the strength or frequency of interaction.
**What can SNA be used for?**
SNA has a wide range of applications across various disciplines. Here are some examples:
* **Business and Management:** Identifying key influencers within an organization, understanding communication patterns within teams, analyzing customer relationships, and tracking the flow of information.
* **Public Health:** Mapping the spread of diseases, identifying high-risk individuals, and evaluating the effectiveness of intervention programs.
* **Social Sciences:** Studying social movements, analyzing collaboration patterns in scientific communities, and understanding the spread of information and ideas on social media.
* **Cybersecurity:** Identifying vulnerabilities in computer networks, tracking the spread of malware, and analyzing attacker behavior.
**Benefits of SNA:**
* **Uncovers Hidden Patterns:** SNA can reveal hidden patterns and connections that might not be readily apparent by simply looking at individual entities.
* **Improved Decision-Making:** By understanding the structure and dynamics of a network, organizations and individuals can make better informed decisions.
* **Enhanced Communication:** SNA can help identify communication bottlenecks and improve information flow within networks.
* **Prediction and Modeling:** Networks can be modeled and analyzed to predict future behavior or the potential impact of interventions.
**Types of SNA Techniques:**
There are various techniques used in SNA, some of which include:
* **Centrality Measures:** These identify the most important nodes in a network based on factors like the number of connections or the flow of information through them.
* **Community Detection:** This technique helps identify groups or clusters of nodes with more connections to each other than to other nodes in the network.
* **Network Visualization:** SNA tools often use visual representations of networks to help researchers and analysts understand the relationships and patterns within the data.
**In Conclusion:**
Social Network Analysis (SNA) is a valuable tool for understanding the complex web of relationships and interactions that define our world. By analyzing networks, we can gain insights into human behavior, information flow, and the spread of ideas. As technology and data collection methods continue to evolve, SNA is likely to become an even more powerful tool for understanding and navigating our increasingly interconnected world.
Social Networks Analysis (SNA) is the mapping, measuring, and visualising, of relationships, interactions, and flows, between people, groups, teams, organizations, computers, URLs, and other connected information/knowledge entities.
The nodes in the network are the people and groups, while the links show relationships or flows between the nodes.
SNA provides both a visual and a mathematical analysis of human relationships, connections and flow. Management consultants use this methodology with their business clients and call it… Organizational Network Analysis [ONA].
To understand people networks and their participants, we evaluate the location of actors in the network. Measuring the network location is finding the cen
trality of a node.
These measures give us insights into the various roles, and groupings, in a network -who are the connectors, mavens, leaders, bridges, isolates, where are the clusters and who is in them, who is in the core of the network, and who is on the periphery?
We look at a social network – the “Kite Network” (right)- developed by David Krackhardt, a leading researcher in social networks.
Social Network Visualisation
For Example: In the graph on the right…
Two nodes are connected if they regularly talk to each other, or interact in some way. Andre regularly interacts with Carol, but not with Ike.
Therefore Andre and Carol are connected, but there is no link drawn between Andre and Ike.
This network effectively shows the distinction between the three most popular individual centrality measures: Degree Centrality, Betweenness Centrality, and Closeness Centrality.
Degree Centrality
Social network researchers measure network activity for a node by using the concept of degrees — the number of direct connections a node has. In the kite network above, Diane has the most direct connections in the network, making hers the most active node in the network.
She is a ‘connector’ or ‘hub’ in this network. Common wisdom in personal networks is “the more connections, the better.”
This is not always so. What really matters is where those connections lead to — and how they connect the otherwise unconnected!
Here Diane has connections only to others in her immediate cluster — her clique. She connects only those who are already connected to each other.
Betweenness Centrality
While Diane has many direct ties, Heather has few direct connections — fewer than the average in the network. Yet, in may ways, she has one of the best locations in the network — she is between two important constituencies.
She plays a ‘broker’ role in the network.
The good news is that she plays a powerful role in the network, the bad news is that she is a single point of failure. Without her, Ike and Jane would be cut off from information and knowledge in Diane’s cluster.
A node with high betweeness has great influence over what flows — and does not — in the network.
Heather may control the outcomes in a network. That is why I say, “As in Real Estate, the golden rule of networks is: Location, Location, Location.”
Closeness Centrality
Fernando and Garth have fewer connections than Diane, yet the pattern of their direct and indirect ties allow them to access all the nodes in the network more quickly than anyone else.
They have the shortest paths to all others — they are close to everyone else.
They are in an excellent position to monitor the information flow in the network — they have the best visibility into what is happening in the network.
Network Centralization
Individual network centralities provide insight into the individual’s location in the network. The relationship between the centralities of all nodes can reveal much about the overall network structure.
A very centralized network is dominated by one or a few very central nodes. I
f these nodes are removed or damaged, the network quickly fragments into unconnected sub-networks.
A highly central node can become a single point of failure.
A network centralized around a well connected hub can fail abruptly if that hub is disabled or removed.
Hubs are nodes with high degree and betweeness centrality.
A less centralized network has no single points of failure. It is resilient in the face of many intentional attacks or random failures — many nodes or links can fail while allowing the remaining nodes to still reach each other over other network paths.
Networks of low centralization fail gracefully.
Network Reach
Not all network paths are created equal. More and more research shows that the shorter paths in the network are more important. Noah Friedkin, Ron Burt and other researchers have shown that networks have horizons over which we cannot see, nor influence.
They propose that the key paths in networks are 1 and 2 steps and on rare occasions, three steps.
The “small world” in which we live is not one of “six degrees of separation” but of direct and indirect connections < 3 steps away.
Therefore, it is important to know: who is in your network neighborhood?
Who are you aware of, and who can you reach?
In the network above, who is the only person that can reach everyone else in two steps or less?
Network Integration
Network metrics are often measured using geodesics — or shortest paths.
They make the (erroneous) assumption that all information/influence flows along the network’s shortest paths only. But networks operate via direct and indirect, shortest and near-shortest paths.
We often hear interesting things from various sources in the network.
Different interpretations arrive via different paths.
Therefore, it is important to be on many efficient paths in networks that reach out to various parts of the extended network.
Those well integrated in the network of paths have both local and distant information, along with several flavors of it!
Boundary Spanners
Nodes that connect their group to others usually end up with high network metrics.
Boundary spanners such as Fernando, Garth, and Heather are more central in the overall network than their immediate neighbors whose connections are only local, within their immediate cluster.
You can be a boundary spanner via your bridging connections to other clusters or via your concurrent membership in overlapping groups.
Boundary spanners are well-positioned to be innovators, since they have access to ideas and information flowing in other clusters.
They are in a position to combine different ideas and knowledge, found in various places, into new products and services.
Peripheral Players
Most people would view the nodes on the periphery of a network as not being very important.
In fact, Ike and Jane receive very low centrality scores for this network.
Since individuals’ networks overlap, peripheral nodes are connected to networks that are not currently mapped.
Ike and Jane may be contractors or vendors that have their own network outside of the company — making them very important resources for fresh information not available inside the company!
To uncover the REAL power, communications, trust, connection networks in your #ADAPTAGILITY team building interventions and organisation, contact Expert Author, Business Speaker, and high performance REVOLUTIONARY WORKPLACE #Adaptagility Facilitator….Tony Dovale on 083-447-6300 www.lifemasters.co.za
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