Preferential Linking
Preferential linking or preferential attachment is a concept in network theory that describes how nodes in a network tend to become connected to other nodes based on their existing degree or number of connections. In other words, the more connected a node is, the more likely it is to receive new connections in the future.
We can observe preferential linking in action in various aspects of life such as :
1. Social networks : In social networks, people tend to connect with others who have many connections. For instance, a popular celebrity is likely to have a large number of followers, which can lead to even more people following them due to preferential linking. Similarly, people tend to join groups or communities that are already popular and have a large number of members. This leads to formation of a few concentrated centres of attention instead of equal distribution. This is how Virality of any content also occurs. Once its caught the attention of and shared by a few big accounts with large followings, it tends to blow out of proportion.
2. Business : In business, preferential attachment can be observed in the way that customers tend to prefer established and well-known brands over new ones. A popular brand tends to have a large customer base which reinforces its authority, which can lead to even more customers choosing them due to their existing popularity instead of going for a new upcoming brand which might offer better value for money. Similarly, businesses tend to form partnerships with other companies that already have a large customer base. This effect again leads to concentration of power in a few well known brands over the rest of the market. This is one of the reasons why the so called “First Mover Advantage” is so real. The first brand to really prove its product’s credibility gets preferential treatment from the market which kind of reinforces the brands power to dictate how the market evolves for its own advantage to certain extent.
3. Decision-making : People generally tend to rely on and trust information that is already widely accepted or popular. For example, a job seeker may be more likely to apply to a company that has a well-established reputation and a large employee base, even if there are other companies that offer better job opportunities. This can make it really hard for new companies to attract good talent and reinforce the competitive edge of a well established business. This is one of the reasons why its so difficult for startups to survive the first few years of operation when its not known at all.
Preferential attachment is also closely related to the network effect, which is the phenomenon where the value or usefulness of a product or service increases as more people use it. The network effect occurs because as more people use a product or service, it becomes more valuable to each individual user due to the increased opportunities for communication, interaction, and exchange.
Preferential attachment can contribute to the network effect by reinforcing the growth and spread of the network. When new nodes join a network, they are more likely to connect to nodes that are already highly connected, which can increase the overall connectivity and value of the network. This, in turn, can attract even more nodes to the network, leading to a positive feedback loop where the network becomes more valuable as more nodes join.
The combination of preferential attachment and the network effect can lead to the emergence of large-scale, highly connected networks, such as social networks like Facebook, online marketplaces like Amazon, or transportation networks like Uber. These networks can have significant implications for businesses, consumers, and society as a whole, and understanding the dynamics of preferential attachment and the network effect is important for predicting and managing their growth and impact.
In general, preferential attachment can have a significant impact on the formation and growth of networks in various domains, including social, technological, and economic networks. Understanding and modeling preferential attachment can help us to better understand these networks and their dynamics, which can in turn inform our decision-making in various contexts.
