Analogy and metaphor are often employed by social scientists to go into detail a social phenomenon because certain social concepts are otherwise very difficult to understand. For example, a physical structure like ‘building’ or a neurological structure like ‘organism’ is compared to define the concept ‘social structure’. Actually, social structure is not a physical structure. An summary concept which are not seen is explained in a basic **netwyman blogs** way by using an analogy which can be seen easily by everyone. Physical scientists use a model to test the prophecy. If the prophecy are correct when the model is tested every time then the model constructed is perfect. Otherwise, the model is suitably modified and then the prophecy are tested again. This process is continued prior to the model becomes perfect. Do we have a grand type of social structure which they can use to test social prophecy? In this article, an attempt is enabled to know how far network theory is advantageous in explaining social structure and whether social prophecy can be made using the network.

Radcliffe-Brown was one of the earliest **how late is the closest grocery store open** to uncover that the analysis of social structure would ultimately take a exact form. Radcliffe-Brown becomes social structure as a ‘set of actually existing contact at a given moment of time, which link together certain human beings’. According to Oxford book, ‘relations’ means just how 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 is understood to be a closely connected lot of people 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 regards **utsav7fun** chances are they are associated with an edge. And once a node is associated with all possible nodes with how the node has social contact, 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 up from 10 to 1000, the number of handshakes has increased 10, 000 folds up. So the number of relationships increases significantly as ‘n’ increases. The network theory was made by the Hungarian mathematicians, John 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 relies on some rules.

If two nodes are connected then the network **wpc 2026** of the two nodes considers 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 in 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 nys of C depends upon the states of a and B in some combination. Further it means if we know nys of C then we will know the combinational behaviour of a and B. But in a social network of persons, we don’t know how a model’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 are not 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 character of a state? If the expected behaviour of you were reduced to two states like ‘yes’ or ‘no’, then the number of states of a network will be 2n. Using this, only one state will show up at a given moment of time. How do we predict that one particular state?

Family is a tiny network within the network. The household members are closely connected with each other. Most of the members are also linked to other networks external to the family. Connections take place within the family among the members who also have connections beyond the family. So there are several edges proceed from node of a family towards nodes within the family and nodes beyond the family. The edges within a family show intimate relationship, whereas the edges connecting nodes beyond the family do not necessarily show intimate relationship. This intimate relationship is a very important premiss that we must consider so as to reduce the number of states of the social network. For example, the possibilities of family members to mould to the household norms will be higher. Similarly, the possibilities of a person to side with a friend will be higher. Also, the possibilities of a member of a particular group to mould to group norms will be higher. These assumptions are necessary to measure the probability of how the whole network acts in a certain way.

Interaction occurs along the nodes. The bond of one node to the other is either direct or roundabout. For example, a person’s friend is linked to the person directly; the individuals pal’s friend is linked to the person indirectly, separated by one friend or technically by one degree. Research (Stanley Milgram, 1967) ensures that every person in the world is separated only by six degrees to any other person. This means that every person is connected directly or indirectly with other persons in the network apart from an singled out community whoever members do not have any contact with outside world. The six degrees of splitting up is only an approximation. For example, if you know the targeted person then the degrees of splitting up is zero. If your friend knows the targeted person then the degrees of splitting up is one and so on. Milgram’s conclusion was if you have selected a person to be directed at random, then the maximum degrees of splitting up would have been six. However, the number of degrees of splitting up depends upon the number of critical nodes in the network in question. We will discuss about critical nodes later. So, connection is more or less a social reality. The question is whether this connection can be used as a tool to study social phenomena? If the answer is yes, then where can we apply this tool?