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Information spreads quickly in today’s tech-connected communications network. As we all know, the way to spread information changes greatly. The flow of information has never been as easy or wide-ranging as it is today. We are tasked to explore the evolution of the methodology, purpose, and functionality of society’s networks by taking a historical perspective of flow of information relative to inherent value of information.I develop one models to show how the flow of information changes over time. I adopt topology model to show the flow of information. Besides, the discussion about what kinds of information are qualified as news is concerned. Introduction With the popularity and interactivity offered by the Internet and World Wide Web, the tech-connected social network is very common to us, which leads to the information spreading quickly than ever before. Every day we are informed of enormous information, including useful, helpful, crucial information and also misinformation. It’s no longer the days when people were almost sealed themselves in the small village. The mass media went through a dramatic change and development, which not only show up in its mode of transmission, but also change public mode of thinking. The progress of this change can be sketched by the following picture. Fig 1. The changes of mass media over time Theories of mass communication have changed dramatically since the early 1900s, largely as a result of quickly changing technology and more sophisticated academic theories and research methods. A quick overview of the state of the media in the early 1900s and in the early 2000s provides some context for how views of the media changed. In the early 1900s, views of mass communication were formed based on people’s observation of the popularity of media and assumptions that something that grew that quickly and was adopted so readily must be good. In the end of the 1950s, Erdos and Renyi created the famous ER random-network model. In 1998, Watts and Strongatz proposed the WS small world network which improved from completely regular network to completely stochastic network. In 1999, Newman and Watts structure the small world network of randomization. Barabasi and Albert published an article which put forward BA scale-free network model. 2nd International Conference on Economy, Management and Education Technology (ICEMET 2016) © 2016. The authors Published by Atlantis Press 1967 Assumptions  I simply the way to spread information into two kinds. The first way to spread is network topology, and another is the spread of social network which is according to whether the receiver can sent the message or not.  Iconsider the former 3 periods (when information are spread by newspapers, radios and televisions) is in accordance with network topology. Because all the newspapers, radios and televisions can only receive the message, but cannot send message to other places. In 1990s and 2010s, as the computer and mobile phones came into popularity, iconsider the information spread as how virus do. Because both computer and mobile devices can not only receive but also send information to others.  I assume that information spread almost at the same time with the occurrence of information. Notations and Definitions Notations Definitions k the degree of nodes kNk the total number of edges starting from the node whose degree is k. ) t k p , ( 1 possibility of a susceptible nodes with k nodes connecting to a transmission nodes at time t ) | ( 1 k k p the contingent probability that the node with degree of k is adjacent to the node with degree of k1 ) | I ( 1 k k S p possibility of node with k1 is in propagating behavior under the condition that the node connects a susceptible nodes with degree of k p1. possibility of susceptible nodes transfers into probability latent node v1 speed of latent node transfers into transmission nodes v2 speed of latent node transfers into immune nodes p2 possibility of transmission nodes that are next to immune nodes transfers into immune nodes v3 speed of transmission nodes stops spreading the information Model :Based on network topology Network G=(V,E) can be expressed as the simple undirected graph whose vertex set is V, and sets E without the ring and multiple edge. Using notations N and K to represent all the nodes and edges of network G, scilicet V N = and E K = . In recent years, researchers have proposed many methods to depict network topology structure and its statistical characteristic. Define the each element of the adjacent matrix { } N N ij a A × = of unweighted and undirected graph G as    = . else , 0 , with v connected v nodes , 1 j i ij a (1) Average path length: the simple route that the smallest number of edges goes through from node vi to different node vj. And the number of edges of the shortest path is called the distance between node vi to vj, noted as dij. And then define the diameter of the network D for the maximum distance between any two nodes, noted as ij , 1 d max D N j i ≤ ≤ = .Network of average path length L can be used the

Explore the flow of information and find what qualifies as news