Social Structure And Network (A Statistical Model For Social Behaviour)

Analogy and metaphor are often as used by social scientists to elucidate a social phenomenon because certain social concepts are otherwise very difficult to grasp. For example, a physical structure like ‘building’ or a physical structure like ‘organism’ is compared to define the concept ‘social structure’. Actually, social structure is not a physical structure. An abstract concept which cannot be seen is explained in a things to consider way by using an analogy which can be seen easily  netwyman blogs by everyone. Physical scientists use a model to test the estimations. If the estimations are correct when the model is tested every time then the model constructed is perfect. Otherwise, the model is suitably modified and then the estimations are tested again. This process is continued so that the model becomes perfect. Do we have a grand style of social structure which they can display to test social estimations? In this article, an attempt was created to know the way far network theory pays to in explaining social structure and whether social estimations can be made using the network.

Radcliffe-Brown was one of the earliest how late is the closest grocery store open  to discover that the analysis of social structure would ultimately take a statistical form. Radcliffe-Brown specifies social structure as a ‘set of actually existing associations at a given moment of time, which link together certain human beings’. According to Oxford thesaurus, ‘relations’ means the greatest two persons, groups, or countries behave towards each other or deal with each other. The phrase, ‘link together certain human beings’ can be compared with a ‘net work’ of connections.

Network means a closely connected utsav7fun crowd who exchange information. Each point (person or agent) in the network is called a ‘node’ and the link between two nodes is connected by a line called an ‘edge’. When two nodes have a direct social respect they’ll likely are linked to an edge. When a node is linked to all possible nodes with that your node has social associations, it produces a graph. The resulting graph is a social network. The number of edges in a network is given by a formula nc2, where ‘n’ is the number of nodes. For example, if there are 3 people in a party then the number of handshakes will be 3. If there are 4 people then the number of handshakes will be 6. If there are 5 people then it will be 10. If there are 10 people then the number of handshakes will be 45. If there are 1000 people then the number of handshakes will be 499, 500. When the number of people has increased 100 folds from 10 to 1000, the number of handshakes has increased 10, 000 folds. So the number of relationships increases significantly as ‘n’ increases. The network theory was created by the Hungarian mathematicians, Robert Erdos and Alfred Renyi, in the mid twentieth-century. Networks of nodes that can be in a state of 0 or 1 are called Boolean networks. It was invented by the mathematician George Boole. In Boolean networks, the 0 or 1 state of the nodes depends upon a couple of rules.

If two nodes are connected then the network wpc 2026 of the two nodes thinks four states (00, 01, 10, and 11). The number of states of network grows exponentially as the number of nodes increases which is obtained by a formula 2n, where ‘n’ is the number of nodes. When n is greater than 100, it is quite difficult to explore all the possible states of the network even for the world’s fastest computer. In a Boolean network we can fix the number of states as 0 and 1. In a Boolean network, if there are three nodes A, B, and C which are connected directly by edges then the state of C can be determined by fixing the states of a and B. It means hawaii of C depends upon the states of a and B in some combination. Further it demonstrates if we know hawaii of C then we will know the combinational behaviour of a and B. But in a social network of persons, we have no idea how a man’s behaviour is deterministic. Further, in a Boolean network, the behaviour of the nodes can be studied in controlled experiments as nodes here are objects. But in a social network, nodes which are individual persons cannot be treated as objects. In a social network how do we define the states of a person? How many states does a person have? What is the type of a state? If the expected behaviour of you are reduced to two states like ‘yes’ or ‘no’, then the number of states of a network will be 2n. Out of this, only one state will show up at a given moment of time. How do we predict that one particular state?

Family is a very fine network within the network. Your family members are closely connected with each other. Most of the members are also linked with other networks external to the family. Communications take place within the family among the members who also have communications not in the family. So there are several edges proceed collected from one of node of a family towards nodes within the family and nodes not in the family. The edges within a family show intimate relationship, whereas the edges connecting nodes not in the family do not necessarily show intimate relationship. This intimate relationship is a very important prediction that we need to consider so as to reduce the number of states of the social network. For example, it is likely that a close relative to adapt your family norms will be higher. Similarly, it is likely that a person to side with a close friend will be higher. Also, it is likely that a member of a particular group to adapt group norms will be higher. These assumptions are necessary to measure the probability of how the whole network behaves in a certain way.

Interaction develops along the nodes. The text of one node to the other is either direct or oblique. For example, a person’s friend is linked with the person directly; the individual’s buddy’s friend is linked with the person indirectly, separated by one friend or technically by one degree. Research (Stanley Milgram, 1967) demonstrates every person in the world is separated only by six degrees to any other person. What this means is that every person is connected directly or indirectly with other persons in the network excepting an remoted community whose members do not have any contact with outside world. The six degrees of divorce is only an approximation. For example, if you know the targeted person then the degrees of divorce is zero. If your friend knows the targeted person then the degrees of divorce is one and so on. Milgram’s conclusion was if you have selected a person to be geared towards random, then the maximum degrees of divorce would have been six. However, the number of degrees of divorce depends upon the number of critical nodes in the network in question. We will discuss about critical nodes later. So, on-line is more or less a social reality. The question is whether this on-line can be used as a tool to study social phenomena? If the answer is yes, definitely, then where can we apply this tool?

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