SOCIAL COMPUTING: AN INTELLIGENT AND RESPONSIVE SYSTEM

Transcription

1 SOCIAL COMPUTING: AN INTELLIGENT AND RESPONSIVE SYSTEM Dev Rishi Tekiwal, Undegaduate Student Akapava De, Undegaduate Student Ezhilmaan D, Assistant Pofesso School of Computing Science and Engineeing, Velloe Institute of Technology, Velloe, India Astact This pape deals with the advantages of a gaphical dataase ove conventional dataase in tems of eal wold applications. The unique popety exhiited y the gaphical dataases which is, that the edges inside the gaph which is the elationship etween the nodes can e dynamically changed in eal time without any computational oveides theey poviding a moe optimized stuctue to compute and manipulate, is given as a hypothesis in this pape. It also thows light on how the data mining techniques can e implemented in the social netwoking, thus making it moe intelligent and esponsive systém. It also popose a methodology to geneate a social gaph of use s action and pedict futue social activities using gaph mining. Though this model, we elieve that it ecomes cleae that data fom diffeent contexts can e elated such that new solutions can e exploed and thus, it may povide illumination fo the afoementioned polems and stimulate new eseach. Keywods: Gaph mining, coloing, influence facto Intoduction Social netwoking has ecome an impotant aea of study ecause of the spawling and apidly evolvement ove the last decade with the advance of Intenet and We Technologies. Computing is shifting to the edges of netwok, and individual uses ae empoweed with technology to engage in Social inteaction, shaing content and distiuting infomation. As a esult the data is inceasing day y day at a ate which is difficult to contol and pocess. Hence a model is needed which can handle the discepancies in the system connecting the people of simila inteest which consists of following some patten which could e extemely useful to otain infomation that could e of inteest to diffeent people of diffeent aeas. Hence we would e 32

2 using a gaphical model to epesent social netwok data, desciing and analyzing pattens of social elations. A novel appoach fo this would e visualizing datasets into dynamic, diected, and weighted gaphs. The datasets will e otained fom the online social netwoking site and the gaph would e constucted using the same dataset. The gaph depicts the use s status in eal time scenaio using links and the weights on the links. The association mining ules would e geneated ased on the gaph and these ules would e used fo doing vaious pedictions. One such example of Gaph Dataase System is the Faceook new Achitectue which is also known as the Gaph Seach. Faceook Gaph Seach is ased on Unicon which is a gaph tavesing tool. Using taditional infomation-etieval systems to mix keywod and stuctued queies is faily well undestood. But Faceook equied the stuctue also to find esponses moe than a single connection away, such as "hotels liked y my fiends fom India." Unicon allows you to wite a simple s-expession quey to get a list of all Faceook Ids which ae you Fiends of fiends. Using s-expession you can tavese the gaph in multiple depths in just one go. Faceook stoes two types of data: ojects, and the elationships etween those ojects. And with the use of Open Gaph, the model has een extended to thid-paty applications and wesites. Visualization In geneal, the way gaph dataases wok is that you use some index to find a stating point, called a node. Once you have a stating point, you tavese the gaph node y node y taveling acoss elationships. Designing fo a gaph dataase is all aout tuning you data into nodes and elationships. Fo Faceook Gaph that stating point is often you account, evey item in the Gaph is a node, and you actions (like, shae, etc.) ae elationships etween you and those nodes. We know that gaph is a set of vetices and connecting edges etween them, whee each vetex o node epesents uses/ oganization etc. and the edges epesenting the elationship etween them. In the case of social netwoking, the nodes epesents people, oganization while the edge epesents the elationship like fiends of, maied to, like, etc. thus we can epesent the social stuctue in fom of a social gaph whee the ehavio of the people could e leant. If an edge exists {a, } then we can say that nodes a and ae elated to each othe. The edges themselves can e unodeed pais of nodes o in a diected gaph (digaph), odeed pais of nodes whee each edge has a diection, sometimes called an ac. Gaphs ae (geneally) no eflexive; nodes ae not elated to themselves. Ode is # of nodes, size is # of edges. (Sun, Tang, Wang & Yang 2009) Most of the social netwoks ae not static and hence it is useful to think aout how a netwok evolves ove time i.e. how nodes aive and 33

3 depat and how the edges fom and vanish. If two people in a social netwok have a fiend in common, then thee is an inceased likelihood that they will ecome fiends too. This could e undestood y the Tiadic closue-a pinciple Tiadic closue-a pinciple. The fomation of the edge etween Band C illustates tiadic closue. B and C have an incentive to ecome fiends (fom a BC edge). (Cameon, Leung & Tanee 2011) Hence we ae poposing data mining technique which could e used to fom a social gaph in the eal scenaio of dynamic natue. The data otained, contains infomation of divese people fom diffeent aeas of wold and these data ae stoed in a dataase. This dataase can e mined using mining techniques fo social maketing pofessionals, advetisement, analysts and also as find isky goup of people fo secuity pupose. Now these pedictions can only e done using pope association ules techniques o algoithms that scans multiple dataases fo association ules. But techniques like apioi, fptee algoithms poduces moe complexity in tems of time and space as dataases ae tavesed multiple times thus educing efficiency. (Bhatia & Kadge 2012) Thus we appoach the polem with social gaph technique whee the dataase is oken down and visualized as a diected, weighted gaph that dynamically inceases its size. Fist the static data is taken fom use duing egistation of the use and a gaph is fomed which dynamically inceases ased on usage o ehavio of the use within the social netwoking site. The links within the gaph epesents association o elationship and the weights epesent the stength of the elationship within the links. And so we can use association techniques and suggest fiends to the use ased on his/hes mutual fiends, o ecome a pat of an oganization y undestanding the likes and dislikes of the use. And this is pefomed whee the dataase of the use acts as taining datasets fo social gaph technique.( Ilkan, Safaei & Sahan 2009) Visualizing the poposed concept Why we need the poposed technique To incease use fiendliness of the social netwoking site and make it moe intelligent towads its uses, and also to incease social awaeness and impovements 34

4 How it is done?- The dataset containing use infomation is conveted to a diected, weighted social gaph that dynamically inceases its size ased on use inteaction and ehavio and tains itself to intelligently fom social elationships etween othe uses, thus ceating a social we of infomation Rules in the social gaph fomation 1. Each edge epesents the elationship etween the nodes, which is the activeness of the use and it dynamically inceases on association techniques 2. Each edge consists of weights that epesent how stong the elationship is. 3. Each edges o link ae when linked etween two uses and unidiectional if link is etween community and use (Bhatia & Kadge 2012) Example Two uses ae epesented using nodes: If two they ae fiends then they ae epesented with nodes connected y idiectional edge. And depending on the closeness of fiendship etween them, weights ae assigned like: just fiends, est fiends, medium fiends, etc. Thus a social gaph is fomed elated to all the elationship etween uses and also etween uses and community and they ae elated using edges with weights. 35

5 Now take this concept to calculate the influence facto among the nodes in ou gaph. To get the pictue, conside the following scenaio. Even the news feed in Faceook is an efficient engine in finding the influence facto etween people i.e. the impotant people whose posts ae shown. In a social netwok, people who ae moe influenced ae consideed to me moe impotant to us. Even Faceook uses the same technique to classify the impotant news feeds fom the othe y the method of influence function which is detemined y the technique elow. We can measue the influence etween uses y means of connectivity and stength of elationship etween them. We know that the degee of a node is the nume of nodes connected to it. And fom this connectedness we can find the influence of each node ove the othe y the weights etween the connectivity i.e. the stength of the elationship. Let s epesent influence y the function I whee, (Bogatti & Eveett 1996) I = degee (p) = nume of fiends of the peson p The main advantage of this is to find the influence. The gaph is fomed using the adjacency matix calculated, then the influence of a node is the sum of the ow in the coesponding ow and the opeation is less complex in tem of pefomance, ut consideed to e the naïve appoach. Let us conside this gaph: Single peson with high Degee 36

6 Single peson low degee ut high connectivity I d denotes measue of influence and hence the fist peson, P1, has a geate ation of influence ecause it is connected diectly to 8 nodes. The second peson, P 2, howeve can influence up to 9 nodes (people).the same is the case in eal wold also. This sot of indiect influence can e measued y using a measuement called eigenvalue centality. The idea is that a peson's influence is compaative to the total influence of the people to whomeve he/she is linked. So let s this influence measue e I e. (Bogatti & Eveett 1996) Let's conside someone is the Pesident at A Cop. The Cop has fou VPs and each of them has influence facto of five and these ae the pesons to whom the Pesident is diectly connected to. Then this measuement says that thee is some λ (nume) such that I e (Pesident) = 1/ λ [ I e (VP 1 ) ] + [ I e (VP 2 ) ] + [ I e (VP 3 ) ] + [ I e (VP 4 ) ] λ is the key facto detemining the level of influence which each of the peson shae though the connectivity. Small value of λ tells that the pesident has much influence, if it is ig then he has slight. How λ is to e calculated? Let G e a social gaph, whee vetices epesents pesons and edges epesent social elationship, and let A e the adjacency matix of gaph G. If thee ae N people in the given social netwok, laeled P 1, P 2, the aove could e genealized and we can conclude: Reminisce that A i,j is 1 if p i and p j ae linked though an edge and 0 othewise. It's also significant to notice that λ is a function of the gaph and not of any individual node (peson). If we say x i = I e (p i ) then a vecto x can 37

7 e fomed whose i th coodinate is the influence of the i th peson. The aove equation could e ewitten using matices and vectos : X = 1/ λ (A/X) Roles/Positions We take a digaph epesented y D(V,E), whee the in-neigho of node v is denoted y Ni(v) which is the set of vetices that sends acs towads v, which is N i (v) = {u: (u,v) E}. We denote the out neighos of a node v y N o (v) which is the set of vetices that get acs fom v. and is denoted y, N o (v) = {u: (v,u) E}. A coloation C is consideed as an assignment of colos to the vetices V of the digaph. C(v) denotes the vetex v colo and the set of divese colos which is assigned to the nodes in a set S is epesented C(S) and is laeled the spectum of S. In the Figue 1, a coloation of the nodes has een showed y laeling the nodes with single lettes such as y fo yellow and fo ed. If two nodes ae of same colo, then they ae said equivalent. (Alet & Baaasi 2002) a y w e y c d Figue 1. A coloation is said to e stong coloation if the nodes ae allotted with the simila colo iff they have simila in-and-out neighohoods. Which means, fo all u,v V, C(u) = C(v) if and only if Ni(u) = Ni(v) and No(u) = No(v). Theefoe the coloation in Figue 1 is a stong coloation. This could e checked y taking node pais and veifying if they ae of the same colo, then they have identical neighohoods, and if they have diffeent colo, then they have dissimila neighohoods. Fo example, and d ae of same colo, and oth of thei neighohoods entail {a,c,e}. A coloation C is said to e egula if C(u) = C(v) implying that C(Ni(u)) = C(Ni(v)) and C(No(u)) = C(No(v)) fo all u, v V. To put it in diffeent wods, in egula coloations, evey pai of nodes having identical colo must eceive acs fom nodes encompassing the same set of colos and must diect acs to nodes containing the same set of colos. Also evey stuctual coloation is also a egula coloation. 38

8 a y e y c d Figue 2. Regula coloation. The coloation in Figue 2 is egula, ut not stongly stuctual. This can e checked y consideing, evey ed node which has an outneighohood compising only yellow nodes and an in-neighohood compising only yellow nodes, wheeas evey yellow node has an out neighohood compising only ed nodes and an in-neighohood compising only ed nodes. An anothe egula coloation has een depicted in figue 3. Node g could not e coloed the same as f o i, ecause it has an out neighohood containing a white node, while f and i have no out neighohood at all. This also gives an implication that g cannot e coloed the same as f and i ecause it has a tie fom a node of a othe colo. (Alet & Baaasi 2002) a c p d e f g h i y s g y w j Figue 3. Regula coloation If we epesent social netwok in fom of gaphs, the colos could e thought as defining types o classes of people such that if one meme of some class (lue) has outgoing links to memes of exactly two othe classes (yellow and geen), then all futhe memes of that (lue) class have outgoing links to memes of those simila two classes (yellow and geen). Theefoe accoding to the concept of egula coloations, memes of social netwok ae classified accoding to thei patten of elations of othes, and 39

9 two pesons ae classified in the same class if thee is some inteaction in same ways with the same kind of othes. Conclusion Thus we have poposed a mixed appoach towads the fomation of a social gaph. This mixed appoach is a comination of mining technique, gaph coloation technique and influence facto to fom a moe stale, dynamic stuctue of the social we. This system also pedicts the activities that could e done y the use. Based on this pediction technique, we could also pedicts the activity, memeship of the use in diffeent community o etween two communities. We have also visualized, how these techniques ae easie and has less time complexity and ette pefomance than othe association ules like apioi algoithm o fptee fomation. Thus we conclude the pape y poposing a ette appoach o impovement in social netwoking aea. Refeences: Juan J. Cameon,Cason Kai-Sang Leung, Syed K. Tanee, Finding Stong Goups of Fiends among Fiends in Social Netwoks, Ninth IEEE Intenational Confeence on Dependale, Autonomic and Secue Computing, Majaneh Safaei, Meve Sahan, Mustafa Ilkan, Social Gaph Geneation & Foecasting using Social Netwok Mining, 33d Annual IEEE Intenational Compute Softwae and Applications Confeence, Sanam Kadge, Gesha Bhatia, Gaph Based Foecasting fo Social Netwoking Site, 2012 Intenational Confeence on Communication, Infomation & Computing Technology (ICCICT), Oct 19-20, Mumai, India Matin G. Eveett, Stephen P. Bogatti, Exact coloations of gaphs and digaphs, School of Computing and Mathematical Sciences, Univesity of Geenwich, Wellington Steet, London, SE18 6PF, UK Jie Tang, Jimeng Sun, Chi Wang and Zi Yang, Social Influence Analysis in Lage-scale Netwoks R. Alet and A. L. Baaasi. Statistical mechanics of complex netwoks. Reviews of Moden Physics, 74(1),