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Network science

Network science

 

Network science is an interdisciplinary academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks. The field draws on theories and methods including graph theory from mathematics, statistical mechanics from physics, data mining and information visualization from computer science, inferential modeling from statistics, and social structure from sociology. The United States National Research Council defines network science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena."

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Background and history

The study of networks has emerged in diverse disciplines as a means of analyzing complex relational data. The earliest known paper in this field is the famous Seven Bridges of Königsberg written by Leonhard Euler in 1736. Euler's mathematical description of vertices and edges was the foundation of graph theory, a branch of mathematics that studies the properties of pairwise relations in a network structure. The field of graph theory continued to develop and found applications in chemistry (Sylvester, 1878).

In the 1930s Jacob Moreno, a psychologist in the Gestalt tradition, arrived in the United States. He developed the sociogram and presented it to the public in April 1933 at a convention of medical scholars. Moreno claimed that "before the advent of sociometry no one knew what the interpersonal structure of a group 'precisely' looked like (Moreno, 1953). The sociogram was a representation of the social structure of a group of elementary school students. The boys were friends of boys and the girls were friends of girls with the exception of one boy who said he liked a single girl. The feeling was not reciprocated. This network representation of social structure was found so intriguing that it was printed in The New York Times (April 3, 1933, page 17). The sociogram has found many applications and has grown into the field of social network analysis.

Probabilistic theory in network science developed as an off-shoot of graph theory with Paul Erdős and Alfréd

 

Rényi's eight famous papers on random graphs. For social networks the exponential random graph model or p* is a notational framework used to represent the probability space of a tie occurring in a social network. An alternate approach to network probability structures is the network probability matrix, which models the probability of edges occurring in a network, based on the historic presence or absence of the edge in a sample of networks.

In 1998, David Krackhardt and Kathleen Carley introduced the idea of a meta-network with the PCANS Model. They suggest that "all organizations are structured along these three domains, Individuals, Tasks, and Resources". Their paper introduced the concept that networks occur across multiple domains and that they are interrelated. This field has grown into another sub-discipline of network science called dynamic network analysis.

More recently other network science efforts have focused on mathematically describing different network topologies. Duncan Watts reconciled empirical data on networks with mathematical representation, describing the small-world network. Albert-László Barabási and Reka Albert developed the scale-free network which is a loosely defined network topology that contains hub vertices with many connections, that grow in a way to maintain a constant ratio in the number of the connections versus all other nodes. Although many networks, such as the internet, appear to maintain this aspect, other networks have long tailed distributions of nodes that only approximate scale free ratios.

Department of Defense Initiatives

The U.S. military first became interested in network-centric warfare as an operational concept based on network science in 1996. John A. Parmentola, the U.S. Army Director for Research and Laboratory Management, proposed to the Army’s Board on Science and Technology (BAST) on December 1, 2003 that Network Science become a new Army research area. The BAST, the Division on Engineering and Physical Sciences for the National Research Council (NRC) of the National Academies, serves as a convening authority for the discussion of science and technology issues of importance to the Army and oversees independent Army-related studies conducted by the National Academies. The BAST conducted a study to find out whether identifying and funding a new field of investigation in basic research, Network Science, could help close the gap between what is needed to realize Network-Centric Operations and the current primitive state of fundamental knowledge of networks.

As a result, the BAST issued the NRC study in 2005 titled Network Science (referenced above) that defined a new field of basic research in Network Science for the Army. Based on the findings and recommendations of that study and the subsequent 2007 NRC report titled Strategy for an Army Center for Network Science, Technology, and Experimentation, Army basic research resources were redirected to initiate a new basic research program in Network Science. To build a new theoretical foundation for complex networks, some of the key Network Science research efforts now ongoing in Army laboratories address:

As initiated in 2004 by Frederick I. Moxley with support he solicited from David S. Alberts, the Department of Defense helped to establish the first Network Science Center in conjunction with the U.S. Army at the United States Military Academy (USMA). Under the tutelage of Dr. Moxley and the faculty of the USMA, the first interdisciplinary undergraduate courses in Network Science were taught to cadets at West Point. Subsequently, the U.S. Department of Defense has funded numerous research projects in the area of Network Science.

In 2006, the U.S. Army and the United Kingdom (UK) formed the Network and Information Science International Technology Alliance, a collaborative partnership among the Army Research Laboratory, UK Ministry of Defense and a consortium of industries and universities in the U.S. and UK. The goal of the alliance is to perform basic research in support of Network- Centric Operations across the needs of both nations.

In 2009, the U.S. Army formed the Network Science CTA, a collaborative research alliance among the Army Research Laboratory, CERDEC, and a consortium of about 30 industrial R&D labs and universities in the U.S. The goal of the alliance is to develop a deep understanding of the underlying commonalities among intertwined social/cognitive, information, and communications networks, and as a result improve our ability to analyze, predict, design, and influence complex systems interweaving many kinds of networks.

Today, network science is an exciting and growing interdisciplinary field. Scientists from many diverse fields are working together. Network science holds the promise of increasing collaboration across disciplines, by sharing data, algorithms, and software tools.

Network properties

Often, networks have certain attributes that can be calculated to analyze the properties & characteristics of the network. These network properties often define network models and can be used to analyze how certain models contrast to each other. Many of the definitions for other terms used in network science can be found in Glossary of graph theory.

Density

The density D of a network is defined as a ratio of the number of edges E to the number of possible edges, given by the binomial coefficient \tbinom N2, giving D = \frac{2E}{N(N-1)}.

Size

The size of a network can refer to the number of nodes N or, less commonly, the number of edges E which can range from N-1 (a tree) to E_{max} (a complete graph).

Average degree

The degree k of a node is the number of edges connected to it. Closely related to the density of a network is the average degree, <k> = \tfrac{2E}{N}. In the ER random graph model, we can compute <k> = pN(N-1) where p is the probability of two nodes being connected.

Average path length

Average path length is calculated by finding the shortest path between all pairs of nodes, adding them up, and then dividing by the total number of pairs. This shows us, on average, the number of steps it takes to get from one member of the network to another.

Diameter of a network

As another means of measuring network graphs, we can define the diameter of a network as the longest of all the calculated shortest paths in a network. In other words, once the shortest path length from every node to all other nodes is calculated, the diameter is the longest of all the calculated path lengths. The diameter is representative of the linear size of a network.

Clustering coefficient

The clustering coefficient is a measure of an "all-my-friends-know-each-other" property. This is sometimes described as the friends of my friends are my friends. More precisely, the clustering coefficient of a node is the ratio of existing links connecting a node's neighbors to each other to the maximum possible number of such links. The clustering coefficient for the entire network is the average of the clustering coefficients of all the nodes. A high clustering coefficient for a network is another indication of a small world.

The clustering coefficient of the i'th node is

C_i = {2e_i\over k_i{(k_i - 1)}}\,,

where k_i is the number of neighbours of the i'th node, and e_i is the number of connections between these neighbours. The maximum possible number of connections between neighbors is, of course,

{\binom {k}{2}} = {{k(k-1)}\over 2}\,.

Connectedness

The way in which a network is connected plays a large part into how networks are analyzed and interpreted. Networks are classified in four different categories:

Node centrality

Node centrality can be viewed as a measure of influence or importance in a network model. There exists three main measures of Centrality that are studied in Network Science.

Network models

Network models serve as a foundation to understanding interactions within empirical complex networks. Various random graph generation models produce network structures that may be used in comparison to real-world complex networks.

Erdős–Rényi Random Graph model

The Erdős–Rényi model, named for Paul Erdős and Alfréd Rényi, is used for generating random graphs in which edges are set between nodes with equal probabilities. It can be used in the probabilistic method to prove the existence of graphs satisfying various properties, or to provide a rigorous definition of what it means for a property to hold for almost all graphs.

To generate an Erdős–Rényi model two parameters must be specified: the number of nodes in the graph generated as N and the probability that a link should be formed between any two nodes as p. A constant <k> may derived from these two components with the formula <k> = 2E/N = p(N-1).

The Erdős–Rényi model has several interesting characteristics in comparison to other graphs. Because the model is generated without bias to particular nodes, the degree distribution is binomial in nature with regards to the formula: P(\operatorname{deg}(v) = k) = {n-1\choose k}p^k(1-p)^{n-1-k}. Also as a result of this characteristic, the clustering coefficient tends to 0. The model tends to form a giant component in situations where <k> > 1 in a process called percolation. The average path length is relatively short in this model and tends to log(N).

Watts-Strogatz Small World model

The Watts and Strogatz model is a random graph generation model that produces graphs with small-world properties.

An initial lattice structure is used to generate a Watts-Strogatz model. Each node in the network is initially linked to its <k> closest neighbors. Another parameter is specified as the rewiring probability. Each edge has a probability p that it will be rewired to the graph as a random edge. The expected number of rewired links in the model is pE = pN<k>/2.

As the Watts-Strogatz model begins as non-random lattice structure, it has a very high clustering coefficient along with high average path length. Each rewire is likely to create a shortcut between highly connected clusters. As the rewiring probability increases, the clustering coefficient decreases slower than the average path length. In effect, this allows the average path length of the network to decrease significantly with only slightly decreases in clustering coefficient. Higher values of p force more rewired edges, which in effect makes the Watts-Strogatz model a random network.

Barabási–Albert (BA) Preferential Attachment model

The Barabási–Albert model is a random network model used to demonstrate a preferential attachment or a "rich-get-richer" effect. In this model, an edge is most likely to attach to nodes with higher degrees. The network begins with an initial network of m0 nodes. m0 ≥ 2 and the degree of each node in the initial network should be at least 1, otherwise it will always remain disconnected from the rest of the network.

In the BA model, new nodes are added to the network one at a time. Each new node is connected to m existing nodes with a probability that is proportional to the number of links that the existing nodes already have. Formally, the probability pi that the new node is connected to node i is[2]

p_i = \frac{k_i}{\sum_j k_j},

where ki is the degree of node i. Heavily linked nodes ("hubs") tend to quickly accumulate even more links, while nodes with only a few links are unlikely to be chosen as the destination for a new link. The new nodes have a "preference" to attach themselves to the already heavily linked nodes.

The degree distribution resulting from the BA model is scale free, in particular, it is a power law of the form:

P\left(k\right)\sim k^{-3} \,

Hubs exhibit high betweenness centrality which allows short paths to exist between nodes. As a result the BA model tends to have very short average path lengths. The clustering coefficient of this model also tends to 0. While the diameter, D, of many models including the Erdős Rényi random graph model and several small world networks is proportional to log N, the BA model exhibits D~loglogN (ultr-small word).[4] Note that the average opath length scale with N as the diameter.

Network analysis

Social network analysis

 

 

Social network analysis examines the structure of relationships between social entities.[5] These entities are often persons, but may also be groups, organizations, nation states, web sites, scholarly publications.

Since the 1970s, the empirical study of networks has played a central role in social science, and many of the mathematical and statistical tools used for studying networks have been first developed in sociology.[6] Amongst many other applications, social network analysis has been used to understand the diffusion of innovations, news and rumors. Similarly, it has been used to examine the spread of both diseases and health-related behaviors. It has also been applied to the study of markets, where it has been used to examine the role of trust in exchange relationships and of social mechanisms in setting prices. Similarly, it has been used to study recruitment into political movements and social organizations. It has also been used to conceptualize scientific disagreements as well as academic prestige. More recently, network analysis (and its close cousin traffic analysis) has gained a significant use in military intelligence, for uncovering insurgent networks of both hierarchical and leaderless nature.[7][8]

Biological network analysis

With the recent explosion of publicly available high throughput biological data, the analysis of molecular networks has gained significant interest. The type of analysis in this content are closely related to social network analysis, but often focusing on local patterns in the network. For example network motifs are small subgraphs that are over-represented in the network. Activity motifs are similar over-represented patterns in the attributes of nodes and edges in the network that are over represented given the network structure.

Link analysis

Link analysis is a subset of network analysis, exploring associations between objects. An example may be examining the addresses of suspects and victims, the telephone numbers they have dialed and financial transactions that they have partaken in during a given timeframe, and the familial relationships between these subjects as a part of police investigation. Link analysis here provides the crucial relationships and associations between very many objects of different types that are not apparent from isolated pieces of information. Computer-assisted or fully automatic computer-based link analysis is increasingly employed by banks and insurance agencies in fraud detection, by telecommunication operators in telecommunication network analysis, by medical sector in epidemiology and pharmacology, in law enforcement investigations, by search engines for relevance rating (and conversely by the spammers for spamdexing and by business owners for search engine optimization), and everywhere else where relationships between many objects have to be analyzed.

Network robustness

The structural robustness of networks[9] is studied using percolation theory. When a critical fraction of nodes is removed the network becomes fragmented into small clusters. This phenomenon is called percolation[10] and it represents an order-disorder type of phase transition with critical exponents.

Pandemic Analysis

The SIR Model is one of the most well known algorithms on predicting the spread of global pandemics within an infectious population.

Susceptible to Infected

S = \beta(1/N)

The formula above describes the "force" of infection fore each susceptible unit in an infectious population, where β is equivalent to the transmission rate of said disease.

To track the change of those susceptible in an infectious population:

\Delta S = \beta \times S {1\over N} \Delta t

Infected to Recovered

\Delta I = \mu I\Delta t

Over time, the number of those infected fluctuates by: the specified rate of recovery, represented by \mu but deducted to one over the average infectious period {1\over \tau}, the numbered of infecious individuals, I, and the change in time, \Delta t.

Infectious Period

Whether a population will be overcome by a pandemic, with regards to the SIR model, is dependent on the value of R_0 or the "average people infected by an infected individual."

R_0 = \beta\tau = {\beta\over\mu}

Web Link Analysis

Several Web search ranking algorithms use link-based centrality metrics, including (in order of appearance) Marchiori's Hyper Search, Google's PageRank, Kleinberg's HITS algorithm, the CheiRank and TrustRank algorithms. Link analysis is also conducted in information science and communication science in order to understand and extract information from the structure of collections of web pages. For example the analysis might be of the interlinking between politicians' web sites or blogs.

PageRank

PageRank works by randomly picking "nodes" or websites and then with a certain probability, "randomly jumping" to other nodes. By randomly jumping to these other nodes, it helps PageRank completely traverse the network as some webpages exist on the periphery and would not as readily be assessed.

Each node, x_i, has a PageRank as defined by the sum of pages j that link to i times one over the outlinks or "out-degree" of j times the "importance" or PageRank of j.

x_i = \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}

Random Jumping

As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as Breadth-First Search and Depth-First Search.

In an improvement over the aforementioned formula for determining PageRank includes adding these random jump components. Without the random jumps, some pages would receive a PageRank of 0 which would not be good.

The first is \alpha, or the probability that a random jump will occur. Contrasting is the "damping factor", or 1 - \alpha.

R{(p)} = {\alpha\over N} + (1 - \alpha) \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}

Another way of looking at it:

R(A) = \sum {R_B\over B_{(outlinks)}} + ... + {R_n \over n_{(outlinks)}}

Centrality measures

Information about the relative importance of nodes and edges in a graph can be obtained through centrality measures, widely used in disciplines like sociology. Centrality measures are essential when a network analysis has to answer questions such as: "Which nodes in the network should be targeted to ensure that a message or information spreads to all or most nodes in the network?" or conversely, "Which nodes should be targeted to curtail the spread of a disease?". Formally established measures of centrality are degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and katz centrality. The objective of network analysis generally determines the type of centrality measure(s) to be used.

Spread of content in networks

Content in a complex network can spread via two major methods: conserved spread and non-conserved spread.[11] In conserved spread, the total amount of content that enters a complex network remains constant as it passes through. The model of conserved spread can best be represented by a pitcher containing a fixed amount of water being poured into a series of funnels connected by tubes . Here, the pitcher represents the original source and the water is the content being spread. The funnels and connecting tubing represent the nodes and the connections between nodes, respectively. As the water passes from one funnel into another, the water disappears instantly from the funnel that was previously exposed to the water. In non-conserved spread, the amount of content changes as it enters and passes through a complex network. The model of non-conserved spread can best be represented by a continuously running faucet running through a series of funnels connected by tubes . Here, the amount of water from the original source is infinite Also, any funnels that have been exposed to the water continue to experience the water even as it passes into successive funnels. The non-conserved model is the most suitable for explaining the transmission of most infectious diseases.

The SIR Model

In 1927, W. O. Kermack and A. G. McKendrick created a model in which they considered a fixed population with only three compartments, susceptible: S(t), infected, I(t), and recovered, R(t). The compartments used for this model consist of three classes:

The flow of this model may be considered as follows:

\color{blue}\mathcal{S} \rightarrow \mathcal{I} \rightarrow \mathcal{R}

Using a fixed population, N = S(t) + I(t) + R(t), Kermack and McKendrick derived the following equations:

\frac{dS}{dt} = - \beta S I
\frac{dI}{dt} = \beta S I - \gamma I
\frac{dR}{dt} = \gamma I

Several assumptions were made in the formulation of these equations: First, an individual in the population must be considered as having an equal probability as every other individual of contracting the disease with a rate of \beta, which is considered the contact or infection rate of the disease. Therefore, an infected individual makes contact and is able to transmit the disease with \beta N others per unit time and the fraction of contacts by an infected with a susceptible is S/N. The number of new infections in unit time per infective then is \beta N (S/N), giving the rate of new infections (or those leaving the susceptible category) as \beta N (S/N)I = \beta SI (Brauer & Castillo-Chavez, 2001). For the second and third equations, consider the population leaving the susceptible class as equal to the number entering the infected class. However, a number equal to the fraction (\gamma which represents the mean recovery rate, or 1/\gamma the mean infective period) of infectives are leaving this class per unit time to enter the removed class. These processes which occur simultaneously are referred to as the Law of Mass Action, a widely accepted idea that the rate of contact between two groups in a population is proportional to the size of each of the groups concerned (Daley & Gani, 2005). Finally, it is assumed that the rate of infection and recovery is much faster than the time scale of births and deaths and therefore, these factors are ignored in this model.

More can be read on this model on the Epidemic model page.

Interdependent networks

An interdependent network is a system of coupled networks where nodes of one or more networks depend on nodes in other networks. Such dependencies are enhanced by the developments in modern technology. Dependencies may lead to cascading failures between the networks and a relatively small failure can lead to a catastrophic breakdown of the system. Blackouts are a fascinating demonstration of the important role played by the dependencies between networks. A recent study developed a framework to study the cascading failures in an interdependent networks system.[12][13]

Network optimization

Network problems that involve finding an optimal way of doing something are studied under the name of combinatorial optimization. Examples include network flow, shortest path problem, transport problem, transshipment problem, location problem, matching problem, assignment problem, packing problem, routing problem, Critical Path Analysis and PERT (Program Evaluation & Review Technique).

Network analysis and visualization tools

See also

References

Further reading

  •  

External links

Notes

  1. Jump up ^ Committee on Network Science for Future Army Applications (2006). Network Science. National Research Council. ISBN 0309653886.
  2. Jump up ^ Albert, Réka; A.-L. Barabási (2002). "Statistical mechanics of complex networks". Reviews of Modern Physics 74: 47–97. arXiv:cond-mat/0106096. Bibcode:2002RvMP...74...47A. doi:10.1103/RevModPhys.74.47. More than one of |author1= and |author= specified (help)
  3. Jump up ^ Albert-László Barabási & Réka Albert (October 1999). "Emergence of scaling in random networks". Science 286 (5439): 509–512. arXiv:cond-mat/9910332. Bibcode:1999Sci...286..509B. doi:10.1126/science.286.5439.509. PMID 10521342.
  4. Jump up ^ R. Cohen, S. Havlin (2003). "Scale-free networks are ultrasmall". Phys. Rev. Lett 90 (5): 058701. doi:10.1103/PhysRevLett.90.058701. PMID 12633404.
  5. Jump up ^ Wasserman, Stanley and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
  6. Jump up ^ Newman, M.E.J. Networks: An Introduction. Oxford University Press. 2010, ISBN 978-0199206650
  7. Jump up ^ "Toward a Complex Adaptive Intelligence Community The Wiki and the Blog". D. Calvin Andrus. cia.gov. Retrieved 25 August 2012.
  8. Jump up ^ Network analysis of terrorist networks
  9. Jump up ^ R. Cohen, S. Havlin (2010). Complex Networks: Structure, Robustness and Function. Cambridge University Press.
  10. Jump up ^ A. Bunde, S. Havlin (1996). Fractals and Disordered Systems. Springer.
  11. Jump up ^ Newman, M., Barabási, A.-L., Watts, D.J. [eds.] (2006) The Structure and Dynamics of Networks. Princeton, N.J.: Princeton University Press.
  12. Jump up ^ S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, S. Havlin (2010). "Catastrophic cascade of failures in interdependent networks". Nature 464 (7291): 1025–28. arXiv:0907.1182. Bibcode:2010Natur.464.1025B. doi:10.1038/nature08932.
  13. Jump up ^ Jianxi Gao, Sergey V. Buldyrev3, Shlomo Havlin4, and H. Eugene Stanley (2011). "Robustness of a Network of Networks". Phys. Rev. Lett 107 (19): 195701. arXiv:1010.5829. Bibcode:2011PhRvL.107s5701G. doi:10.1103/PhysRevLett.107.195701. PMID 22181627.
  14. Jump up ^ [1] Bejan A., Lorente S., The Constructal Law of Design and Evolution in Nature. Philosophical Transactions of the Royal Society B, Biological Science, Vol. 365, 2010, pp. 1335-1347.
Author:Bling King
Published:Dec 23rd 2013
Modified:Dec 23rd 2013
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There is no such thing as time
Posted by Bling King

    

     Upon further ponderance I have come to the conclusion that time does not exist except in the law of physics. I have come to this conclusion through the observation of how things change and why they change at the pace in which they change. To me it seems that every change that takes place  in the universe is not dictated by time but rather physics. It is the law of physics that dictates the rate and speed at which all things change. For example if you have a car  that is traveling at 100 miles an hour the speed at  which the car travels is all dictated by physical changes and therfor controlled by the law of physics..Therfor it seems that for any change to take place all you need is physics and the law of physics that governs the physical changes. Time does not need be a factor and bears no relavance. As long as we have the law of physics everything will happen in accordance with those laws.

The composition of time
Posted by Bling King

   

    Time has 3 components. A front a middle and a rear. In the front time has what appears to be something of perspectual perspectualness that will move things forward at a set forth proponent. This part of time is easy to see and witness. However it is not easy to predict at which point time will make forward momentum happen. It would seem that this forward momentum is always in inactment but I would disagree with this. To me it seems more as if time interacts with things on its own accord leaving somethings unchanged for long standing periods of time. An example of this would be how time occasionally interacts with the speed of light. The speed of light remains constant but occasionally time will manifest itself into the equation and make modifications of the speed that light travels. For instance light will move forward forthwittingly at a billion miles a second but if it encounters any kind of resistance then time will inject itself and change the speed at which it was moving. Which leads me to the assumption that in order for time to inject itself into any equation a proponent has to take place that makes a physical change that would cause time to interject itself. If no physical change takes place than time has also not been a factor.

    The middle proponent of time is the area in which time is manipulating  the change that takes...Read More

👄What turns me on
Posted by Bling King

    I get turned on by some funny stuff. I'm not really into like full blown kinkiness or at least I wouldn't consider myself to be a kinky person but I do have a few fetishes. Some of them are a little out of the ordinary. For instance I have this one fetish about being tied up  and thrown in the ocean and then rescued by a mermaid. I think this fantasy comes from when I was a kid and I used to dream of mermaids and always wanted to meet one. Well one day its gonna happen. Now don't go telling me mermaids don't exist. You don't know cause they are in fact real and as soon as I meet one I will prove it to you. As far as some of my other turn ons  I guess what really gets me excited is people who  tell other people to shut the fuck up. I love when a woman just looks at a man and tells him to shut his mouth. To me thats a big turn on because the woman seems assertive like a dominatrix or something. If she will be assertive in a conversation she will be assertive in the bedroom or so I  would like to believe.

Time is a dialectable derelict
Posted by Bling King

To fathom the fortrighteousness of time one has to contemplate the personification of forthwittial forthwittil. Time forthwittingly will only listen to the commands of its on inner personification to which there is no directional direction or so it would seem but on further inquisitories I have come to realize that there is a forthwittingly forthwittal of which time has pronounced and those commands seem to speak to the nature of to which time corresponds. To review these pronouncements for your own bemusement look at time as if you had it captured it  in a bottle. What would happen? We know on the inside of the bottle time would force the inner workings of the bottle to correspond to times diabolical commands. Causing everything to change to times everlescent rules. however on the outside of the bottle things would not change, everything would stay in constant neutrality or would it? The question remains if there was no time would things still be allowed to happen and if so at what pace and what would dictate the pace at which things would change. There seems to be no rule in place for the dictation of the pace change which takes place. So it would seem that time has decided that factor somehow within itself. There could be a correlation at which things change and the pace being dictated by physics and the amount the physical world can be allowed to change within its own accord of set boundaries. To actually find...Read More

Free from time constraints
Posted by Bling King

 

 

 

There was a time when time did not matter. The thing that was an utmost relevance now was of no matter. The diffrence it made seemed miniscule and now it is constantly dictating everything that takes place before me. What is this thing that controls and makes everything manifest itself to its constraints and why and how does it do this. Time is nothing but the utmost miracle before us. Something that has always had to exist for anything ever to take place. There is no changing its course there is no variance in its absolute everlasting existance. To control time would be the utmost  crown jewel of all accomplishments if indeed it could ever be controlled. The only way I ever see time being manipulated to change its values is to speed up everything that time has interacted with. In order to do such a thing you would have to understand the nature of the objects in question and how they are effected by time. For instance a speeding car will slow down in time without constant force being distrubuted by the engine. To slow down the car one only has to take their foot off the accelarator and gradually time will do the rest but if you could freeze time at the speed at which the car was traveling then time would not  exist because the...Read More

the truth about time
Posted by Bling King

        I have looked at time many times and I have noticed a few components. There is a precise proponent that ushers in a manifestation. Whenever something new is going to happen you can look at that event which is about to take place and precisely predict exactly when it has started. Once you realize a manifestation has taken place you can precisely predict its out come. If you know that a manifestation has started to take place then you will know you are being guided through the realm precisely by the forces of an enlightenment. Throughout time this manifestation will remain constant starting with a beginning and an end and ending in a preconcieved enlightenment. Sometimes an enlightenment can take weeks and some times an enlightenment can take centuries. It depends on how many times that enlightenment has been benounced to the realm. 

 

nothing
Posted by Bling King

I suspect a suffcient of sufficence of suffiacantel suffiance of suffiance of absurdity of absurdanace. In all actual actuality there is an  actual actuality of actualityness in retrospect to the retorospective respect in which every person who has an intellectual intellect can see that the world is a prominance of prominance in which the order will reside as long as the order is maintained. Once that order is relinquished chaos will ensue. For chaos to be a calamity there only needs to be a perspectual perspective of perspectance that escalates the chaos to that height. What would cause that is a person or persons in the realm of the realmatical realmatics looking beyond thier own existance to the existance of there forfathers to see what has become of thier existance. If you look at your own existance for what it is you will see that it is neither logical nor illogical for it makes all the sense of a sensimatical sensematic. As long as you have a reason for your own existance then it is fruitful for you to exist. Once that reason or reasons are gone you will no longer care whether it is you live or die. In the realm in which we live is a prospectus prospectant of prospectantin which all will ensue. To change the prospectus prospectus you need to look to the realm and see what the prospectus prospectant is and manifest it to your own liking. My...Read More

The conclusive conclusion
Posted by Bling King

In all actual reality the realm is manifested of certain procedural procedures that come forth frequently to forthrightous forthrightenous. In the place of predicament I have found that I can properly place things in the procedural sequence unbenowst to people of the realm. In order to conflict the conflictions you have to equate the equation of equationalness in to proper equations. Very simple but also very tedious. You do this by equating the equation into percise preciseness. An example of an equation would be a placement of perdicament of a certain event in which you wish it to be. The next manifestation I could manifest is a manifestual manifestation of manifests of a sequance of certainal circumstances. Put together a sequence by asking the sequence in order to manifest itself and then tell the manifestations to happen in frequence in which they will unfold.

The Unattainable future
Posted by Bling King

     If the future is a grain of sand and its falling through an hour glass nothing in the world can stop it. It will eniquivaocalby blind as to where its going when it comes to its rest it has befallen its fate and will remain where it lay for an eternity knowing nothing about itself or it's surroundings. I am that grain of sand. Nothing ever can change my destiny for only time here makes a diffrence.. To benounce the future is the only way to change ones fortune. The time it takes to make an equivical change remains the utmost mystery of the universe.

🤯In the eyes of myself
Posted by Bling King

 

 

There where three men. All who seemed frightened. They stood on the edge of the canyon looking on as a fourth man tumbled to his death. We could have saved him said one of the men. He should have saved himself said another. The third man just look at them bewildered and brought a handgun to his own head and pulled the trigger. Blood spattered. The two men watched as he slumped to the ground. The first man screamed and the second threw himself to the side of the man on the ground. Why?!! he screamed. It was the only sound heard. Sobbing he looked at the man standing and said you did this! You and your frigging righteous speech about the lives we leave and the sacrifice we must make. Your the devil. I am not the devil said the standing man only the truth. The truth about what? The other man screamed. Your life he said and he jumped.

The man heard a ringing and he sat up slowly. It was over the dream but his thoughts where still on the side of the canyon. How did this happen. How did it all just fade away? The dream came and went in an instant leaving his mind boggled and his eyes heavy. I knew I was there thought the man but how? It was all to familiar the...Read More

The story Elijah and Ellen
Posted by Bling King

The story of Elijah and Ellan. This is the story of Elijah and Ellan. Ellan is a beutiful temptress and Elijah is a dutiful servant of Ellan's. Together the pair fell in love and soon became a duo of in excessible excession. They frolicked in the sun under the rare occurance of rain they took shelter in the arms of each other. One day while hiding from the glares of the sun under an oak tree that provided an abundance of shade they looked into each others souls and realized there where no people suited for each other then the two of them where suited for each other. They basked in the notion that they where the most two compatible souls on the planet. As they where thinking this a giant unforseen acclamaited acclamation occurred. The planet began to tremble and shake beneath them and the stars came out. The sun hid amongst the clouds and everything from start to finish began to take shape. There where huge explosions and giant surges of wind and rain. The two began to run for their shelter knowing at the exact moment the trembling and violent agressions of unacclaimated weather started that they most likely wouldn't make it to see another sunrise. The planet was exploding with molten lava and the tempertures where unbearable as for the two of them could remember they had never seen a winter climate and didn't expect they ever would. The planet had been warming out of...Read More

today was a day of dismal despair
Posted by Bling King

Things have gone down hill drastically now for a very long time. We seem to be some what defeated but yet i know we still have some power and prominance. We are fighting an up hill battle and there is no way forward from here from what i can see. We are trudging along a path that goes nowhere.

⚔️The Greatest Warrior of All Time
Posted by Bling King

 

 

Today i conquered and beat all adverseries there where to beat. Tomorrow new adversaries will arise. I will be ready, there is never a shortage of enemies who wish to dethrone me from the top of the world. I didn't get here by being passive and yeilding to the oppostion. I got here by defeating them both mentally and physically and in entiriety.

In a time of desilute despair
Posted by Bling King

     There was a time when I was in desilute despair. The only thing I had was me myself and I to fall back on. I looked at the person who was my opponent and I knew one of  us was going to die and I was going to do everytrhing I could to make dam sure it wasn't me. I pulled my six shooter from its holster and aimed at the guy looking at me  about 30 yards away. He also went for his gun and in lightning speed he was laid sprawled out on the dirt bleeding and moaning. I had heard a shot but new that it had come from my own gun. He never even got a shot off. I was unscathed and again undeafeted. Anybody who ever tried to kill me was dead and their where over 30 who had tried and failed to kill yours truly.

Gravity
Posted by Bling King

Gravity is the force of nature that pulls cellestrial bodies toward one another. The cause of gravity is the enertia of a bodies movement through space and time. This happens by an object preconcievably traveling through the cosmos at an alarming rate of acceleration. The faster an object travels the more enertia it will build up and then will therefore have a greater ability to move. the more it moves the more other objects will cling to it. the way this can be proved is by taking an object and hurtling it towards another object the two objects would collide do to the enertia pulling them towards each other. Thy would not stay on their current trajectory but their paths would alter towards one another in a greater force than their initial gravitational pull. the best test to accomodate this theory would be tow baseballs flying through the air at speeds over one hundred miles an hour. The baseballs would not interject themselves with one another normally but at this speed would do so do to the balls enertia pulling them towards one another.

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