Networking Towards Data Science
Hello one and all myself Dr Ganesh Khekare and I'm working in a School of Computer Science and Engineering in Vellore Institute of Technology Tamilnadu India so my today's keynote session is related to the networking towards the data science. So before moving ahead As we know the network is basically Australian bomb said by the titanium network is interconnected collection of autonomous entities when we say interconnected then exchange must be there sharing must be there so in order to share something two or more than two nodes now node may be in the form of anything so nodes must be there which can exchange their information and again independently they should have their identity as well their freedom as well autonomous must content must be there so now first of all what is network and in this keynote session we were going to have a focus on networking and how data science will be helpful to integrate the network or to do the network analysis we will see one by one so what is Network a network array first to a structure representing a group of object or people and relationship between theme it is also known as a graph in mathematics a network structure consists of nodes and edges here nodes represent objects which are used to analyze while ages represent the relationship between those objects for example if we are studying a social relationship now in this my entire session I am going to correlate with the Facebook or the Instagram we have for the better understanding so let us take one exam if you are studying a social relationship between a Facebook users nodes are nothing but the target users the who are using the Facebook and the ages are relationships such as friendship between two users or group memberships are there so in Twitter as well ages can be if you are following or for our relationship will be considered as a egis now the second is what is a network analysis and why network analysis is required if you are using any social media applications you may have experienced the friend or follower suggestion functions have you ever wondered how these functions work one common technology used in this case is the network analysis so why network analysis is required that we will see so network analysis is useful in many Living applications tasks it helps us in a deep understanding the structure of a relationship in a social network a structure or a process or a change in a natural phenomenon or even the analysis of biological system organisms so everywhere we have a huge amount of data nowadays available to manage it we require a proper analysis of that particular Network again let's use open network of social media users as an example analyzing this network helps in identifying the most influent person or people in a group defining characteristic of groups of users prediction of suitable item for users and identifying CM targets Etc so other easy to understand examples are the friend suggestion function in a Facebook or follow suggestion function in it in a Twitter so who is the important person in this if you are trying to analyze the network who has the most importance so a crucial application of network analysis is identifying the important node in a network this task is called as measuring Network centrality in social network analysis it can refer to the task of identifying the most influential member or the representative of the group for example which node do you think is the most important amongst the various nodes are available like on a Facebook or a Twitter also various users are available and various things has been done between the two users so you have to identify which is the best node is available in such a scenario that's why we require a data analysis to do a proper analysis of the entire data we have so ah of course to define the most important node we need a specific definition of the important node there are several indicators used to measure the centrality of a node so the very first indicator is the degree centrality node with a higher degree has higher centrality so this has been We are following from the data structure subject as well the second we have the Eagan vector centrality so adding to the degree of one node the centrality is of an important nodes are considered as a result the eigen vector corresponding to the highest taken value of the adjacency Matrix represent the centrality of nodes in the network now between third parameter or the indicator we have is the between nice centrality the number of Parts between two nodes that go through the ith node is considered as a is node between nest centrality and the fourth parameter or the indicator which we can use is the closeness centrality the length of the path from the ayat node to the other nodes in the network is considered as a ith node closeness centrality with this definition for example this centrality can be applied in the task of defining a suitable evacuation site in the city now the last part we have is the to identify ourselves who we are as far as network data science network is concerned so another application of network analysis is the community detection task so this task purpose to divide a network into a group of nodes that are similar in any specific features examples of this task are a task of defining groups or user in SNS who share common interests or opinions find groups of customer to advertise specific items recommendation systems in online shopping systems so many researchers are working on algorithm to effectively solve communicating Community detection problems some well-known algorithm methods in this task are carnegan lean algorithms then spectral castillary then label propagation then modularity optimization Etc so we have to identify ourselves so that we can accommodate in any cluster or class we can Define the things again we have the supervised learning unsupervised learning and then reinforcement learning is also moving nowadays and again we are moving towards the D free enforcement learning algorithm so we should able to analyze the newer environment as well and we should able to differentiate between these three and we can share it properly that is the main thing we have so moving towards the next part so now Network in a data science so the network in a data science is a collection of different connected objects these objects present which are called in nodes or vertices we draw a line to connect different object which are called ages different nodes connected with objects using edges and which can connect multiple objects so networks are as one example is given in the sample network is given in the presentation as well so these are our new nodes we have black solid circles and in order to connect two nodes we have the different edges available so networks are often referred to as a graphs there are various systems in a real world which can be represented as a Networks let's take an example of Internet it is a network where the nodes are the computers or laptops and the edges are the connections between the devices the rail network is a system of stations Interlink by different railway lines the Friendship is also called as a network where all friends connected with each other via different group of people now let's say so here you can see a Facebook Network graphs has been shown and you can see the complexity of billions and millions are users are there on the social networking sites again each user is a doing a transaction with the thousands of another users doing a charting sending something so a huge kind of data is available over there so Network also referred as a graph or Digraph which carries some sort of information whereas a graph represents only the connection between two or more systems and provides no information regarding its elements however a network defines additional vital quantitative information now the next state what are the different types of networks are available so we have on a generalized form these seven networks are available first one is the road networks then pipeline networks then artificial neural network then biological networks and Telecommunications Network then computer network and then social network where I'm going to provide you the glimpses of first two Road networks and the pipeline networks so one by one we will see this so as you can see in this diagram it is the example diagram of given of a Rhoda Network so road is a system of interconnected lines designed to accommodate Field Road going vehicles and prediction is traffic in Road networks weight of each nodes can be the length of the road in miles or kilometers the valued or actual time for traveling and the cost of traveling can be considered such as the cost of fuels and tolls in addition the weight of the nodes can also be the appealing scenery the quality of road then the danger rate of traveling along the road and the amount of traffic is taken into consideration while calculating this so simplest way to find the path between two cities can be work using the road Network in the below given Network problem the amount of traffic that each Road can take is not taken into consideration but the length of the road or the distance between the two cities is taken into account for assigning the weight on the two edges of our graph so to find the shortest path between two cities which is considered as a vertices on the graph we can determine the shortest route between the two corresponding cities so as you can see on the presentation as well in this image a seven major cities of UK has been shown so each node is a city and each Edge is the graph represent a straight flight path distance that a traveler would take while going from one node to another node again various recent Trends is going on while calculating the shortest path from one city to another city you can also refer my various latest paper which is available on my all research profiles so while now while calculating the shortest path between different nodes you know we are considering the multi parameters again with multi parameters we are also focusing on the history data as well as current data and what is going to be in the future as well so by considering this all three data we can come to the prediction because we cannot say if a particular one road is busy for one day it will be busy for throughout the year like we have a particular University which is has a opening time and the closing time so I if suppose 9 am to 5 PM is a timing of our University so most probably in the morning 8 30 to 9 it is going to be a busy that road and again in the evening 5 to 5 30 PM again it is going to be a busy because at a sudden a lots of a student will use that road so rest of the time it will remain and it also on the weekends holiday will be there and no one can use the role so we cannot predict that's why we have to take into consideration the history data as well current data as well as future data also the second type we have is the pipeline networks now in a pipeline networks in a pipeline Network ages between two nodes are represented as a pipeline and two nodes considered as a two Junctions and the weight of the node represent the capacity of the pipeline a fluid such as water oil or gas flows from source to terminal the total flow can be considered as a flow coming from the source and going to terminal so we can say that no fluid loss while going from source to terminal the fluid flow ah flowing through the pipeline must not be exceed the node capacity and the specified direction as well. We can determine that the flow of two units of fluid can be said along the root highlighted in the we can see so this way pipeline networks works so moving ahead with the graph. So basically what is graph I show you you can show in the you can see in this diagram a graph is a compilation of set of nodes or vertices and the set of edges so we can draw the graph by displaying the nodes by dots and drawing lines between the end points of the age if the set or vertices given problems is for example you can say vertices V can be classified as five elements will be there as a a b c d e and the set of edges is will be a to b b 2 e then D to e then C to D and then a to a to c one additional age is also given as a to d okay so we can apply or replace the vertices in whichever order we want if two vertices are connected by a line if there is any a predefined weight that should be taken into consideration now multiple edges now like in this for node a we have multiple edges or multiple paths are available multiple ages are those for two or more it is joining the same pair of nodes if single node has an a joining a node to itself is called as a loop loop you can see over here this is the multiple edges as shown in the diagram and this is the loop we have and the simple graph will look like a mesh structure as you can see in the this diagram so a a graph which does not contain multiple edges or Loops is a simple graph you can say so most so the most common type of graphs are simple graph then directed graph and the weighted graph so let's one by one see this so the first graph we have is the a simple graph as you can show in the diagram a graph which does not have any Loops or without parallel edges it's called a simple graph simple graph is also referred to as a strict graph it does not have any weighted undirected graph containing no graph loss or multiple edges a simple graph can be connected or disconnected a simple graph which has Total Line vertices the degree of every vertex is at most n minus 1.
the next graph we have is the directed graph now in a directed graph as you can see in the diagram as well if the connections between the nodes are directional then it is called as a directed graph this directed graph is called as a diagram in a direct date graph all the branches of a graph are represented with arrows on which they are connected to next node a director graph is a graph consisting of points called vertices joined by directed lines called arcs every Arc joins exactly two vertices the director graph is also as shown in the diagram so in this diagram your G will become a v comma e with a mapping done such that every H map onto some ordered pair of vertices like a v i comma VG so we can say which node is going towards which node as a direction there can be a different nodes reaching one node the sample of directed graph is as you can see on the presentation in a friends network person a might know person B but that does not mean that person B nodes personing so this kind of relationship can be shown by the directed graph now the third graph we have is the weighted graph a weighted graph is a graph where each age has a numerical value called weight this can also be measured the weight needed to reach other node this weight is represented by a weight function which is given by w e belongs to R the weight on an H can signify many things for example on a road map a person a need time to reach percent B so person a if need a time to reach person b or the distance between cities to reach the Final Destination can be considered over here now the most important part of my keynote speech if you are talking about the networks to handle through data science so tool is required for the same so there are various tools are available nowadays I am focusing on one of the tool I am given the insights of one tool so data science building a network graph using Microsoft power bi for SQL relational data so we will have a look on it so how to build a network graph for sales data stored in a SQL Server to visualize the sales pattern you can see this presentation on this slide so Network theory is a state of art theory that is used to represent the complex relationship between entities some interesting applications are pandemic diffusion analysis for example code 19 social network analysis like Facebook Network World Trade analysis Etc so Network graph is built upon the network Theory and you can provide a dynamic sometimes mind blowing graphs for storytelling so here we are going to discuss how to build a network graph for SQL relational data stored in SQL Server it may be of interest of walks for example like a software engineer data engineer database administrator who want to take on some data science projects above is a final Network graph for sales data for bike related product note that I am not using all the data for Simplicity and the cleaner view for a demo purpose so from this graph which you can see on your in my presentation as well it is easy to answer the frequently Asked question by Computing executive so what products are sold all the green nodes in the graph including touring bikes Road Bikes mountain bike bike tracks so a second where are products sold to all the gray nodes in the graph including California New England New Mexico Colorado and Nevada and Utah so simply you can answer the third question may be asked how many products are sold or how many much are products sold for so simply by seeing the graph you can provide the answer mostly to California and New Zealand some to know Mexico and Colorado and a small amount of Uther and Nevada now the next question can be asked as are there any correlation between the product and customer geographical so simply you can say yes for example a lot more Road rights than bike racks are sold to New Zealand indicated by a thicker line for road bikes next question can be asked for a brand new product where should it be marketed and sold so for example a touring a bike can be marketed marketed to California New Zealand New Mexico and Colorado now how do we build something like this in Microsoft power bi so this as a one example I have put it one graph in my presentation so this kind of questions you've asked you can simply answer it but how to create this cup now in order to create this graph we have a software available Microsoft power bi now let's see how it works has two specific thing one is prerequisites and second one is what is in the sample Adventure Works database so the first one is prerequisite so a Microsoft account you can register for free then power bi desktop you can download the software it is open source software from the Microsoft official link also you can download it then you require a SQL Server you can download it from free for the Microsoft link so Microsoft sample database that is Adventure Works LT 2016. okay all the researchers listening to me can note it down these softwares so now the second point is what is the sample Adventure Works database so fake CMS data for a sales system then products and product categories then customers and their addresses and sales orders Now by installing the software which I have told in a pre-sequences you can use it very easily they are very user friendly okay you can put your data and with respect to you can provide the attributes to it and with respect to that it will generate the graph for you now the next we have I would like to have a focus on vulnerability due to the interconnectivity so as you can see the diagrams on your screen at a first Reliance the two satellite images are indistinguishable so showing light shining brightly this kind of images we usually see on the Google as well most of the times so being a data science engineer we have to buy for gate we have to come up with the solutions on it automatically through system software we should be able to do that so at the first line the two satellite images are indistinguishable showing light shining brightly in a highly populated areas and dark places that Mark was uninhibited forests and oceans 8 upon a closer inspection we notice the differences Toronto Detroit Cleveland Columbus and Long Island bright and shining in a you can see how gone dark in B this is not a doctored shot from the next Armageddon movie but represents a real image of the U.S Northeast on August 14 2003 Before and After the Blackout that left without a power on estimated 14 5 million people in eight U.S states and another 10 million in Ontario
so image a you can see on your screen is the satellite image on Northeast United States taken on August 13 2003 at 9 29 pm 20 hours before the 2003 blackout and the image be the same as above put five hours After the Blackout so the 2003 blackout is a typical example of cascading failure when a network acts as a transportation system a local failure shift loads to other nodes if the lexter load is negligible the system can simply seamlessly observed and the failure goes unnoticed if however the external load is too much for the neighboring nodes they will two tip and reduce and distribute and load to their neighbors in no time we are faced with the cascading event whose magnitude depends on the position and the capacity of the nodes that failed initially cascading failures have been observed in many complex system they take place on the internet when traffic is re-roted to bypass malfunctioning routers this routine operation can occasionally create denial of service attacks which make a fully functional routers unavailable or by overwhelming them with traffic we witness cascading events in a financial system like in a 1997 when the international monetary fund has a pressure put pressure on it in central on the central banks several Pacific Nations to limit their credit which defaulted multiple cooperations eventually resulting in a stock market crashes worldwide the 2009 to 2011 Financial meltdown is often seen as a classic example often cascading failure the U.S credit crisis paralyzing the economy of the globe leaving behind scores of failed Banks then corporations and even bankrupt States so cascading failures can also be induced artificially an example is the worldwide effort to drive the money supply of terrorist organizations aim at creeping their availability to function similarly cancer researchers aim to induce cascading failures in our sales to kill cancer cells the Northeast blackout illustrates several important themes of this book so first to avoid damaging Cascades we must understand the structure of the network on which the Cascade propagates second we must be able to model the dynamic dynamical processes taking place on these networks like the flow of electricity finally we need to uncover how the interplay between the network structure and Dynamics are failed the robustness of the whole system although cascading failures may appear random and unpredictable they follow repredictable laws that can be Quantified and even predicted using the tools of network space the blackout also this blackout also illustrate a bigger thing vulnerability due to the interconnectivity indeed in the early years of electric power each City and its own generators and electric Network electricity cannot be stored however once produce electricity must be immediately consumed it made economic sense therefore to link neighboring cities allowing them to share the extra production and borrow electricity if needed we await the low price of electricity today to the power grid the network that image through this pairwise connection linking all procedures and consumes into a single Network it allows cheaply produce power to the instantly transported it anywhere electricity hence offers a wonderful example of the huge positive and that networks that have on our line being a part of the network has its catch however local failures like the breaking of the fuse somewhere in the OU may not stay local any longer their impart can travel along the Network's link and affect other nodes as well consumers and individuals apparently removed from the original problem in general interconnectivity induces a remarkable non-locality it allows information memes business practices power energy and viruses to spread on their respective social or technological networks and reaching us no matter our distance from The Source hence Network carry both benefits and vulnerabilities uncovering the factors that can enhance the spread of traits dim positive and limit others that make Network weak all vulnerable is one of the goals of This research work so the research scope is there only thing is that quality research is required and researchers should come forward to solve such kind of problems now moving ahead so networks at the heart of complex systems so I think the next Century will be the century of complexity so this is the statement given by the Stephen Hawking so we are surrounded by the systems that are hopelessly complicated consider for example the society that requires cooperation between the billions of individuals or the communications infrastructure that integrate billions of cell phones with computers and satellites our ability to reason and comprehend our world requires the coherent activity of billions of neurons in our brain our biological existence is rooted in a seamless interactions between the thousands of genes and metabolic bullets within our cells the systems are collectively called complex system capturing the phase that is difficult to derive their Collective behavior from a knowledge of the systems components given the important role complex system play in our daily life in a science and in economy their understanding mathematical description prediction and eventually control in one of the major intellectual and scientific challenges of the 21st the emergence of the network science at the dawn of the 21st century is a vivid demonstration that science can live up to this challenge indeed behind each complex system there is an intricate Network that encodes the interactions between the system components so five to six things we always need to take an into consideration the very first is the network encoding the interaction between the genes proteins and metabolase integrates this component into a live cells the very existence of this cellular network is a prerequisite of a life the second thing is the wiring diagram capturing the connections between the neurons called the neural networks holds the key to our understanding of how the brain functions to our consequences the third thing is the sum of all professional friendship and Family Ties often called the social network is the fabric of the society and determines the spread of knowledge behavior and resources the fourth thing is communication Network describing which communication devices interacts with each other through wired internet connections or Wireless links are at the heart of the modern Communication System fifth one is the power grid a network of generator and transmission line supplies with energy virtually all modern technology sixth one is the trader networks maintain our ability to exchange the goods and services being responsible for the material prospective prosperity and that the world has enjoyed since World War II so networks are also at the heart of some of the most revolutionary Technologies of the 21st century and also going ahead with the 22nd century so empowering everything from the Google to Facebook Cisco and Twitter at the end Networks format science technology business and nature to much higher degree then it may be evident upon a casual inspection consequently we will never understand complex system unless we develop a deep understanding of the networks behind them and for understanding deeply the network data science will be required the exploding interest in a network science during the first decade of the 24th server Century either rooted in the discovery that despite the obvious diversity of complex systems the structure and the evaluation of the networks behind each system is driven by a common set of fundamental rods and principles therefore notwithstanding the amazing differences in a form size nature agents K-pop on real Network most networks are differences in a form size nature age and scope of real networks most networks are driven by Common organizing principle once we disregard the nature of the components and the precise nature of the interactions between them the obtained networks are more similar than different from each other in the now moving towards the next section now here we are going to focus on two forces help the emergence of the network science so Network science is a new discipline one may debate its precise beginning but all accounts the field has emerged as a separate discipline only in the 21st century not now so why didn't we have a network science 200 years earlier after all many of the networks that the field explores are by no means new metabolic networks date back to the origins of life with a history of 4 billion years and the social network is as old as Humanity furthermore many disciplines from the biochemistry to sociology and the Brain science have been dealing with their own networks for decades craft Theory a prolific sub field of mathematics has explored graph since 1735. in this is there reason therefore to call a network science the science of the 21st Century something special happen at the dawn of the 24th century and that transcended individual research fields and catalyze the emergence of a new discipline to understand why this happened now and not 200 years earlier we need to discuss the two forces that have contributed to the emergence of the data science Network the first one we have is the emergence of the network map so the emergence of the network science while the study networks has a long history with roots in a graph Theory and sociology the modern chapter of the network science emerge only during the first decade of the 21st century the explosive interest in a network is well documented by the citation pattern of two classic papers the 1956 paper by wall reduce and Alfred Rani that marks the beginning of the study of random networks in a graph Theory and in 1973 paper by Mark Granovator the most cited social network paper the figure so it had they have shown that they have acquired their population and that both papers had only limited impact outside their field the explosive growth of the citations of this paper in the 21st century is the consequences of the emergence of the network science drawing a new interdisciplinary attention to these classic Publications now the emergence of the network map now the very first thing the emergence of the network map to describe the detailed behavior of the system consisting of hundreds of billions of inter acting components we need a map of the system so write in diagram in a social system this would have required an accurate list of your friends your friends friends and so on in the world wide web this map tells us which web pages linked to each other in the sale the map corresponds to a detailed list of binding interactions and chemical reactions involving genes proteins and metabolics in the past we lack the tools to map these networks it was equally difficult to keep track of the huge amount of data behind them the internet Revolution offering effective and fast data sharing methods and cheap digital storage fundamentally change our ability to collect a symbol share and analyze data pertaining to real networks thanks to this technology advances at the turn and Milestone we witness and explore explosion of a map marking example range from the guide or dimes project that offered the first large-scale maps on the internet to the hundred of millions of dollars spent on biologies to experimentally map out protein to protein interactions in human cell the efforts made by social network companies like Facebook Twitter or LinkedIn to develop accurate depositories of our friendships and professional ties the contempt project of the U.S National Institute of Health that aims to systematically trace the neural Connections in a mammalian brain so the sudden availability of these maps at the end of the 20th century has catalyzed the emergence of network science the next point we have is the universality of the network characteristics so it is easy to list the differences between the various networks we encounter in a nature or a society that notice of the metabolic Network or tiny molecules and the links are chemical reactions governed by the laws of chemistry and the quantum mechanics the nodes of the world wide wave are wave no comments and the link are URLs guaranteed by the computer algorithm the nodes of the social network are individual and the links represent family professional friendship and acquaintances styles the processes that generated these networks also differed a greatly metabolic networks where shaped by billions of years of evaluation the world wide way we built by the collective actions of the millions of individual organization the social networks are the shaped by social norms whose roots go back thousands of year given the diversity in size nature scope history evaluation one would not be surprised if the networks Behind These systems would differ greatly a key discovery of network science is that the architecture of network emerging in various domain of science nature and Technology are similar to each other a consequences of being governed by the same organizing principle consecutively we can use a common set of mathematical tools and explore the system this universality is one of the guiding principles of This research but each time we ask how widely they apply we will also aim to understand their Origins uncovering the laws that shape Network evaluation and then consequences on the network Behavior so we can summarize like this while many disciplines have made the important contributions with the network science the emergence of the new field was partly made possible by data availability offering accurate maps of network encountered in different disciplines these diverse Maps allowed Network scientists to identify the universal properties of various networks characteristics this university universality offers the foundation of the new discipline of the network science now moving ahead the next is the characteristics of the network science so the very first characteristic is interdisciplinary nature now Network Sciences offers a language through which different disciplines can seamlessly interact with each other indeed cell biologists brain scientists and computer scientists alike are faced with the task of characteristic the wiring diagram behind their system so extracting information from incomplete and noise in data set and understanding their systems robustness to failure or attacks to be sure each discipline brings a different set of goals technical details and challenges which are important on their own in the common nature of many issues these field struggles it has led to cross-disciplinary fertilization of the tools and idea for example the concept of betweenness centrality that emerge in The Social Network literature in the 1970s today plays a key role in identifying high traffic notes on the internet similarly algorithm developed by computer scientists for a graph part dictionary is also taken into consideration so this is the first point we have and it has been found that novel application in identifying disease modules in a medicine or detecting communication within the large social network the second characteristic we have is the empirical data driven nature several key concepts of the network science how the roots in the graph theory if a fertile field of mathematics what distinguishes network science chronograph 3D is its empirical nature that you use focus on data function and utility as we will see in the coming slides in the network science we are never satisfied with the developing abstract mathematical tools to describe a certain Network property each tool we develop is tested on a real data and its value charge by the insights it offers about the system properties and behavior the third characteristic we have is the quantitative and mathematical nature to contribute to the development of network science and to properly use it tools it is essential to master the mathematical formalism behind it Network assigned borrowed the formalism to deal with the graphs from graph Theory and the conceptual framework to deal with the randomness and seek Universal organizing principle from statistical physics lately the field is being from conceived borrowed from engineering like control and the information Theory allowing us to understand the control principle of networks and from statistic helping us extract information from incomplete and noisy data set the development of network analysis software has made the tools of network science available to a wider Community even those who may not be familiar with the intellectual foundations and the full mathematical tips of the discipline it to further the field and to efficiently use its tool the mid to master its theoretical formalization the last characteristic we have is the computational nature given the size of many networks of practical interest and the exceptional amount of auxiliary data behind them Network scientists are regularly confronted by a series of formidable computational challenges and the field has a strong computational character actively borrowing from algorithm database management and a data mining a series of software tools are available to address this computational problem enabling practitioners with diverse computational skills to analyze the networks to interest to them so as a we can summarize a Mastery of a network science require familiarity with each of these aspects in the field it is their combination that offers the multi-faceted tools and perspective necessary to understand the properties of real Networks now the next part is social societal in Impact if you are talking about the network data science what will be the societal impact it has we will discuss over here so the first impact is the economic impact so from web search to social networking the most successful companies of the 21st century from Google to Facebook Twitter link Francisco Apple you can take an example of any like my base their technology and business model on Networks indeed Google not only runs the biggest Network mapping operation that Humanity has ever built generating a comprehensive and constantly updated map of the world wide wave but his search technology is deeply interlinked with the network characteristics of the wave network has gained particular popularity with the emergence of Facebook the company with the ambition to map out the social network of the whole planet Facebook was not the first social networking site and it is likely not the last either an impressive ecosystem of the social networking tools from Twitter to LinkedIn are fighting for the attention of millions of user algorithm conceived by Network scientists feel these sites aiding everything from a friend recommendation to advertising the second societal impact we have is the health from drug design to metabolic engineering so completed in almost 2001 the human German provider offered the first comprehensive list of all human genes it to fully understand how our sales function and the origin of the disease a full list of genes is not sufficient we also need an accurate map of how genes proteins metabolics and the other cellular components interact with each other indeed most cellular processes from food processing to sensing changes in the environmentally on molecular Network the breakdown of these networks is even so responsible for human diseases several new companies like Advent advantage of the opportunities are offered by the network for the health and medicine for example gengo collects map of cellular interactions from the scientific literature and genomatica uses the predictive power behind metabolic networks so identified drug Targets in bacteria and humans so recently a major pharmaceutical companies like Johnson and Johnson have made a significant investment in network medicine seeing it as a part towards the future drugs next we have a network biology and the medicine so again we have a third Society societal impact we have is the security to fighting with the terrorism so again we are I'm sitting in India delivering a lecture to the China again we have a neighbor as a Pakistan so every time one or the other thing is going on with between these three countries again there are good people who are trying to collaborate and sharing their knowledge as well like me and the conference of fertility is there so terrorism is a malady of the 21st century requiring significant resources to compact in the worldwide Network thinking is increasingly presently a rental of various law enforcement agencies in a charge of responsibility terrorist activities it is used to disrupt the financial network of the terrorist organization and to map adversial Network helping to our uncover the role of their members and their capabilities while much of the work in this area is classified several well documented case studies have been made public examples include the use of social networks to find Saddam Hussein or those responsible for the March 11 2004 Madrid and bombing through the examination of the mobile call network network Concepts has impacted military Doctrine as well leading to the concept of network Centric Warfare aim at finding fighting low intensity conflicts again terrorist and criminal networks and employ decentralized flexible Network organization now the next we have is the epidemics from forecasting so this is the network behind a military engagement so [Music] vast amount of data is available so in order to handle it wisely we require a network using the data science these are the mapping organizations we have so moving one by one so next we have is the epidemics from the forecasting of halting deadly viruses so recently we have faced the corona as well so while the H1 and one pandemic was not a device static as it was feared at the beginning of the outbreak in a 2009 it gained a special role in the history of epidemics it was the first pandemic which course and the time evaluation was accurately predicted months before the pandemic reached its peak so this was possible thanks to fundamental advances in understanding the role of Transportation networks in the spread of viruses the next we have is the Neuroscience societal goodies Neuroscience mapping mapping of brain we are working on how to map a brain and we are continuously working on it we means researchers so the human brain consisting of hundreds of billions of interlinked neurons is one of the last least understood networks from the prospective of network science the reason is simple we lack mobs Maps telling us which neurons are linked together the only fully mapped brain available for research is that of the C Elegance warm consisting of only three zero to neuron detailed maps of mammalian brains could lead to a revolution in a brain science align the understanding and curing of neurons neurological and brain diseases with that brain research could turn it into one of the most prolific application area of the network science driven by the potential transformative impact of such maps in 2010 the National Institutes of hills in the U.S has initiated the connectome project a map developing Technologies so that could provide accurate neural maps of mammalian brains the last we have is the management uncovering the internet structure of an organization so while management tends to rely on the official chain of the command it is increasingly we don't that the in front Network capturing who really communicates with whom plays the most important role in the success of the organization accurate maps of such organizational Network can expose the exponential potential lack of interactions between key units help identify individuals who play an important role in bringing different departments and products together and help fire management diagnose drivers organization and the issues furthermore there is increasing evidence in the management literature that the productivity of an employee is determined by his or her position in this informal organization Network so overall Network science tools are indispensable in a management and business enhancing the productivity boosting Innovations within the organizations the next we have is the scientific impact so this figure shows the complexity and the network science so now where is the impact of the network science more evident than in the scientific Community the most prominent scientific journals from nature to science sell to Pinas devoted reviews and editorials addressing the impact of the network on various topics from biology to social sciences for example science has published a special issue on networks marking the 10-year anniversary of the discovery of the scale free networks so during the past decade each year about a dozen of international conferences workshops summer and winter schools have focus on network science a highly network data science a highly successful networks data science conference series it's called net AC I had read the fields practitioners since 2005. several general interest books have made a bestseller released in many countries bringing network data science to a general public most major University offer Network science courses attracting a diverse student body and in around 2014 Northeast University in Boston as well and the central European University in Budapest have long the PHD programs in a network science now the rise of data science so the complexity and the network science the scientific impact of a network science is seen through a citation pattern compared to the citation of the most cited papers in a complexity the study of complex system in the 60s and 70s were terminated by the Edward Lawrence 1960 classic work on cause and Canon G Wilson's rear normalization group and Samuel F Edwards and Philip W Anderson work on spin glasses in the 1980s the communicating has shifted its focus to pattern formation following Benoit mind approach books on fractals and the Thomas written and lion centers introduction to the diffusion limited aggregation model equally influential was on hoffield's paper on neuron Network and the PowerBack shouting and put the vision field work on self-organized criticality these papers continue to Define our understanding of complex system the figure as you can see on your screen compares the yearly citation of this Landmark paper with the citation of the two most cited papers in a network science the paper by the Vats and straw Gates on a small world Network and by barabasi and Albert responding the discovery of a scaled free networks so several other matrices indicates that Network science is impacting in a defining numerous discipline for example in a several research field Network paper become the most cited paper in their leading journals so that's all about the networking towards the data science and still lots of scope is available we are forming the network of various things day by day a huge amount of data we are generating and to handle this data science integer intelligently we still require lots of Innovations to be made as far as data science is concerned and as far as algorithm is concerned so if you require to collaborate with me these are my contact details and the public profiles are available so my personal email ID is khekare that is my surname k h e k a r e dot one two three gmail.com
then my this is my official email ID given by my Institute that is the willow instructor of Technology having a reference to nine and again we are scoring good in the world ranking as well so these are my research profiles on various databases so you this is my researcher ID then my this is our cheater ID this is my problem profile and Google Scholar profile scopus profile and this is my personal primary email ID anytime if you wish to collaborate or if you are having a query related to a networking devices feel free to contact me and thanks a lot to the conference committee for inviting me as a keynote speaker and giving me a platform to share my knowledge so thanks a lot have a wonderful research life.