Functionality in SNA(Social Network Analysis)[7]
Functionalities are firstly the visualization of the network, secondly the computation of statistics based on nodes and on edges, and finally, community detection (or clustering)
1)Visualization of the network- Methods
1) FruchtermanReingold
2) Kamada-Kawai (which has a faster convergence than FruchtermanReingold, but which often does not give so good results than this last one)
2) Computation of statistics based on nodes
A) Vertex and edge scoring
The place of a given actor in the network can be described using measures based on vertex scoring. Common types of vertex scoring are the centrality measures. Within graph theory and network analysis, there are various measures of the centrality of a vertex to determine the relative importance of this vertex within the graph
• Degree centrality
• Closeness centrality
• Between’s centrality
Vertices that occur on many shortest paths between other vertices have higher between’s than those that do not.
PageRank: The score computed by Page Rank is higher for nodes that are highly connected and connected with nodes that are highly connected themselves.
HITS algorithm: Hyperlink-Induced Topic Search (HITS, also known as hubs and authorities) calculates two scores: hub and authority score. The more a vertex has outgoing arcs, the higher is its hub score. The more a vertex has incoming links, the higher is its authority score.
Tools for SNA[7,8,9,10]
a) Pajek graph file
It is a "network of networks" that consists of millions of smaller domestic, academic, business, and government networks, which together carry various information and services, such as electronic mail, online chat, file transfer, and the interlinked web pages and other resources of the World Wide Web (WWW).
3.2. BlackHole. In this attack,malicious nodes advertise very short paths (sometimes zero-cost paths) to every other node, forming routing black holes within the network [41]. As their advertisement propagates, the network routes more traffic in their direction. In addition to disrupting traffic delivery, this causes intense resource contention around the malicious node as neighbors compete for limited bandwidth.
Proposed algorithm consider three types of nodes every type have different initial energy level. normal nodes have E_0 energy. m advanced nodes have a times energy more than normal nodes with E_0 (1+a) energy level. m_0 super nodes have b times energy more than normal nodes with E_0 (1+b) energy level, where a and b are energy factors. As N is the number of total nodes in network, then for number of normal nodes, advanced nodes and super nodes N(1-m) , Nm〖(1-m〗_0) and Nmm_0 in the network, respectively.
In this example, here node A wants to send data packets to node D and starts to find the shortest path for its destination, so if node D is a malicious node then it will show that it has active route to the specified destination. It will then send the response In the example, data packets transfer in a hierarchic data center network. The link capacity is 1000 kb/s. The number on each is the traffic load. The distribution of traffic is based on equal cost multi-path (ECMP). In figure 8, we can see that the 3). Congestions
Builds topologies map (Every node knows how to reach to its directly connected neighbors and by making sure that the total of this acknowledge is distributed to every node then every node will have enough information to build a complete map of the network).
PageRank is an algorithm developed by Sergey Brin and Lawrence Page which uses subset of network analysis to understand associations between nodes in a linked database. The algorithm uses multitude of parameters to assign PageRank to various pages and is the backbone to Google search functionality. Rank assigned to a webpage is calculated based on the ranks of the webpage citing it.
In simulated network the source node designated as1 initiates the routing procedure by sending RREQ or Route Request message to its surrounding nodes. The RREQ message sent by the source node is denoted in the color green. The other RREQ messages are shown in cyan, yellow, black etc. The source node 1 is sending the RREQ message to its neighbour nodes 5, 6, 9, 11 and 13 and the links are formed shown by the green line. Every time node 5,6,9,11,13 is sending the RREQ message to its neighbour and the links are formed.
Social networks are growing day by day. For modular representation of Graph $G(V,E)$ first phase of the design issue is to modularize the network having border nodes\cite{newman2006modularity}. Boarder nodes
Step 1: Construct a network diagram for the project. (NOTE: EF for activity H should be 19)
The basic idea is to set up a monitor at each node in the network to produce evidences and to share them among all the nodes .An evidence is a set of relevant information about the network
After watching the movie The Social Network, the first thing I did was to search for Mark Zuckerberg’s real life experiences to see which parts are facts and which are fictions. As a matter of fact, this Harvard genius that founded the world’s first social network was not as childish as the movie portrayed. At least he didn’t write programming for getting into elite Harvard “Final Clubs” or for retaliating his girlfriend. During Mark’s high school, Microsoft and AOL tried to purchase the music player that he built and also invited him to join them. However, Mark decided to enroll in Harvard for further education. From where I stand, although the movie is fictional, it can easily
As written by the authors social ties are usually discussed in terms of “structure and content (Umerson, crosnoe & Reczek, p.1). Structure refer to social integration which is a process that involved everyone within a circle to participate with the hope of achieving a peaceful and good relationship whereas social network can be thought of as a process whereby individuals build relation among each other and share similar idea and thoughts. Content which can be positive or negative refers to social support and stress.
\textbf{Social systems and the structure of static interaction networks.} Social organization of species can be highly variable, ranging from solitary to highly structured social societies. Species are generally categorized into (discrete) social systems based on (qualitative) observations of life history traits (but see \citep{Silk2006, Aviles2012, Silk2013}). The degree of social complexity of many species has however been recently debated based on the structure of their social networks \citep{Mourier2012, Sah2016}. The spectrum of social complexity (and associated costs and benefits) therefore cannot be be fully realized without consideration of social network structure. Since interaction patterns are key to assess the social complexity, can the structure of social network be used to quantify the differences among social systems? To answer this question, we compared the structural characteristics associated with the
Within this process nodes are created that are in dyads or a grouping of three dyads that makes a triad. The edges communicate to each other again whether the dialogue is shared or if it is one way. If the visual shows a clustering of triads you can read into the importance in my interest of anthropology this is important. You can infer information through triads on the relationships of many things including tribal patterns and how different people groups are linked. This is one way that the humanities can engage in digital tools and gain insight where it would otherwise be lacking (Weingard, 2013). The information provided through network analysis would be important or the humanities could see repeated or lost information not being meaningfully transmitted.
Following the success of Netscape and its web browser, Internet became a resource and communication platform idolized by many IT students in the universities. What started off as a hobby-cum-research[1] work by Jerry Yang (now Chief of Yahoo!) and David Filo (Co-founder of Yahoo!) for their Ph.D. dissertations; has evolved and became an Internet sensation over time. What they did was to compile all their favourite web links to form an online directory for easy navigation in the World Wide Web. The duo’s work immediately garnered a lot of attention from many surfers in the Internet world and before they realized it, Yahoo! became one of the most highly visited websites of all time. The duo saw the