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YouTube Videos – Computer network and Related Articles

A computer network, or data network, is a digital telecommunications network which allows nodes to share resources. In …

Fiber optic cables are used to transmit light from one computer/network node to another

Computers are very often connected to networks using wireless links

An ATM network interface in the form of an accessory card. A lot of network interfaces are built-in.

A typical home or small office router showing the ADSL telephone line and Ethernet network cable connections

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1. Computer network – A computer network or data network is a telecommunications network which allows nodes to share resources. In computer networks, networked computing devices exchange data with other using a data link. The connections between nodes are established using either cable media or wireless media, the best-known computer network is the Internet. Network computer devices that originate, route and terminate the data are called network nodes, nodes can include hosts such as personal computers, phones, servers as well as networking hardware. Two such devices can be said to be networked together when one device is able to exchange information with the other device, Computer networks differ in the transmission medium used to carry their signals, communications protocols to organize network traffic, the networks size, topology and organizational intent. In most cases, application-specific communications protocols are layered over other more general communications protocols and this formidable collection of information technology requires skilled network management to keep it all running reliably. The chronology of significant computer-network developments includes, In the late 1950s, in 1960, the commercial airline reservation system semi-automatic business research environment went online with two connected mainframes. Licklider developed a group he called the Intergalactic Computer Network. In 1964, researchers at Dartmouth College developed the Dartmouth Time Sharing System for distributed users of computer systems. The same year, at Massachusetts Institute of Technology, a group supported by General Electric and Bell Labs used a computer to route. Throughout the 1960s, Leonard Kleinrock, Paul Baran, and Donald Davies independently developed network systems that used packets to transfer information between computers over a network, in 1965, Thomas Marill and Lawrence G. Roberts created the first wide area network. This was an precursor to the ARPANET, of which Roberts became program manager. Also in 1965, Western Electric introduced the first widely used telephone switch that implemented true computer control, in 1972, commercial services using X.25 were deployed, and later used as an underlying infrastructure for expanding TCP/IP networks. In July 1976, Robert Metcalfe and David Boggs published their paper Ethernet, Distributed Packet Switching for Local Computer Networks, in 1979, Robert Metcalfe pursued making Ethernet an open standard. In 1976, John Murphy of Datapoint Corporation created ARCNET, a network first used to share storage devices. In 1995, the transmission speed capacity for Ethernet increased from 10 Mbit/s to 100 Mbit/s, by 1998, Ethernet supported transmission speeds of a Gigabit. Subsequently, higher speeds of up to 100 Gbit/s were added, the ability of Ethernet to scale easily is a contributing factor to its continued use. Providing access to information on shared storage devices is an important feature of many networks, a network allows sharing of files, data, and other types of information giving authorized users the ability to access information stored on other computers on the network

2. Network motif – All networks, including biological networks, social networks, technological networks and more, can be represented as graphs, which include a wide variety of subgraphs. One important local property of networks are so-called network motifs, which are defined as recurrent, network motifs are sub-graphs that repeat themselves in a specific network or even among various networks. Each of these sub-graphs, defined by a pattern of interactions between vertices, may reflect a framework in which particular functions are achieved efficiently. Indeed, motifs are of notable importance largely because they may reflect functional properties and they have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Although network motifs may provide an insight into the network’s functional abilities. Let G = and G′ = be two graphs, graph G′ is a sub-graph of graph G if V′ ⊆ V and E′ ⊆ E ∩. If G′ ⊆ G and G′ contains all of the edges ‹u, v› ∈ E with u, v ∈ V′, then G′ is an induced sub-graph of G. We call G′ and G isomorphic, if exists a bijection f, V′ → V with ‹u, v› ∈ E′ ⇔ ‹f. The mapping f is called an isomorphism between G and G′, when G″ ⊂ G and there exists an isomorphism between the sub-graph G″ and a graph G′, this mapping represents an appearance of G′ in G. The number of appearances of graph G′ in G is called the frequency FG of G′ in G, a graph is called recurrent in G, when its frequency FG is above a predefined threshold or cut-off value. We used terms pattern and frequent sub-graph in this review interchangeably, there is an ensemble Ω of random graphs corresponding to the null-model associated to G. We should choose N random graphs uniformly from Ω and calculate the frequency for a particular frequent sub-graph G′ in G, the larger the Z, the more significant is the sub-graph G′ as a motif. A sub-graph with P-value less than a threshold will be treated as a significant pattern, another statistical measurement is defined for evaluating network motifs, but it is rarely used in known algorithms. This measurement is introduced by Picard et al. in 2008 and used the Poisson distribution, in addition, three specific concepts of sub-graph frequency have been proposed. As figure illustrates, the first frequency concept F1 considers all matches of a graph in original network and this definition is similar to what we have introduced above. The second concept F2 is defined as the number of edge-disjoint instances of a given graph in original network. And finally, the frequency concept F3 entails matches with disjoint edges and nodes, therefore, the two concepts F2 and F3 restrict the usage of elements of the graph, and as can be inferred, the frequency of a sub-graph declines by imposing restrictions on network element usage. As a result, a network motif detection algorithm would pass over more candidate sub-graphs if we insist on frequency concepts F2 and F3

3. Dependency network – The dependency network approach provides a system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the nodes, and provides an important step towards inference of causal activity relations between the network nodes. This methodology has originally been introduced for the study of data, it has been extended and applied to other systems, such as the immune system. In the case of network activity, the analysis is based on partial correlations, in simple words, the partial correlation is a measure of the effect of a given node, say j, on the correlations between another pair of nodes, say i and k. Using this concept, the dependency of one node on another node, is calculated for the entire network and this results in a directed weighted adjacency matrix, of a fully connected network. The partial correlation based Dependency Networks is a new class of correlation based networks. This original methodology was first presented at the end of 2010 and this research, headed by Dror Y. Kenett and his Ph. D. supervisor Prof. Eshel Ben-Jacob, collaborated with Dr. Michele Tumminello and they quantitatively uncovered hidden information about the underlying structure of the U. S. stock market, information that was not present in the standard correlation networks. Thus, they were able for the first time to show the dependency relationships between the different economic sectors. Following this work, the dependency network methodology has been applied to the study of the immune system, as such, this methodology is applicable to any complex system. To be more specific, the correlations of the pair. Defined this way, the difference between the correlations and the partial correlations provides a measure of the influence of node j on the correlation. Therefore, we define the influence of node j on node i, or the dependency of node i on node j- D, in the case of network topology, the analysis is based on the effect of node deletion on the shortest paths between the network nodes. Note that the correlations for all pairs of nodes define a symmetric correlation matrix whose element is the correlation between nodes i and j. Next we use the resulting node correlations to compute the partial correlations, the first order partial correlation coefficient is a statistical measure indicating how a third variable affects the correlation between two other variables. The partial correlation between nodes i and k with respect to a third node j − P C is defined as, P C = C − C C where C, C and C are the node correlations defined above. We note that this quantity can be viewed either as the dependency of C on node j. The node activity dependencies define a dependency matrix D whose element is the dependency of node i on node j, for this reason, some of the methods used in the analyses of the correlation matrix have to be replaced or are less efficient

4. Social network – A social network is a social structure made up of a set of social actors, sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to local and global patterns, locate influential entities. Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and web of group affiliations. Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships and these approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s. Social network analysis is now one of the major paradigms in contemporary sociology, together with other complex networks, it forms part of the nascent field of network science. The social network is a theoretical construct useful in the sciences to study relationships between individuals, groups, organizations, or even entire societies. The term is used to describe a structure determined by such interactions. The ties through which any given social unit connects represent the convergence of the social contacts of that unit. This theoretical approach is, necessarily, relational, thus, one common criticism of social network theory is that individual agency is often ignored although this may not be the case in practice. Precisely because many different types of relations, singular or in combination, form these network configurations, in the late 1890s, both Émile Durkheim and Ferdinand Tönnies foreshadowed the idea of social networks in their theories and research of social groups. Tönnies argued that groups can exist as personal and direct social ties that either link individuals who share values and belief or impersonal, formal. Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, in psychology, in the 1930s, Jacob L. Moreno began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups. In anthropology, the foundation for social theory is the theoretical and ethnographic work of Bronislaw Malinowski, Alfred Radcliffe-Brown. In sociology, the work of Talcott Parsons set the stage for taking a relational approach to understanding social structure. Later, drawing upon Parsons theory, the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory, by the 1970s, a growing number of scholars worked to combine the different tracks and traditions. In general, social networks are self-organizing, emergent, and complex and these patterns become more apparent as network size increases. However, a network analysis of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative