Vinay Viradia.et.al. Int. Journal of Engineering Research and App-lication ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -5) March 2016, pp.48-52 RESEARCH ARTICLE
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Fuzzy AndANN Based Mining Approach Testing For Social Network Analysis Vinay Viradia, NishidhChavda, Nilesh Dubey Assistant Professor,Charotar University of Science &Technology,Changa . Gujarat. India
ABSTRACT Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups increasing day by day on that social sites , and a large group may influence other.In this paper, we recommendhybrid model of opinion recommendation systems, for single user and for collective community respectively, formed on social liking and influence network theory. By collecting thedata of user social networks and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural Network(ANN)techniques in social media data classification by using some contemporary methods different than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview of the main ideas and recent results of social networks analysis , and we point to relationships between the two social network analysis and classification approaches .This researchsuggests a hybrid classification model build on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to collect information depicting features that are then used to train and test the proposed methods . This neoteric approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data and knowledge base available in the hate lexicons. Keywords-HFANN ,SNA ,SNS ,OWA , FRPR, Webmining , MATLAB
I.
INTRODUCTION
Use of Social networking websites is popular reven before the advent of websites like Facebook, Twitter or Whatsapp. In [33] aextensive advent of modeling social network data as undirected graphs have been suggested and has been popularly recognized. During the past eratremendousanalysis has been executed on e.g. inter bonding of cluster of members in social graphs [36] or distribution of social networks [11]. However, the social weblog data analysis of the social media sites is an upcoming field now , there exists numerous interesting World Wide Web’s phenomenon, most of them are recorded by Barabási in his book “Linked: The New Science of Networks”. According to this book, “Networks are everywhere. Knowledge of them is mandatory” [12]. Commonly social network analysis, identical to the ones elaborated above, takes the links between its actors as binary (1 if present, or 0 if not). Practically, not all the actors are associated with same degree. For instance, hyperlinks between the two websites belonging to the same organization will demonstrate strong ties while these same websites will build weak ties with the third website belonging to some other organization. www.ijera.com
Hence the cohesion between the hyperlinks is different with different organization. In traditional approach the social links between the different organizations was given equal weight age. MATLAB is a good tool to test nonlinear, multi-dimensional, correlated , widely classified based data mining algorithm based on ANN Principles ,for the testing of weblog based mining problems related to social media . It is generally accepted that - When a researcher designed some algorithm that will generate test cases to solve the problem through the MATLAB , it is possible to train the agents of ANN based model to compare the results . This reduces the efforts of mathematical testing for web mining. As this study will compare theresultsets from the ANN and Fuzzy set model , the working principle is to use opinion and weights of the groups in the social network pages , and it will help to understand the uses of proposed hybrid approach from researcher side . Currently there is are good classification approaches for Social Network Analysis (SNA) as a combination of ANN and Fuzzy sets for social network analysis . This paper will test this approaches through MATLAB testing . 48|P a g e
Vinay Viradia.et.al. Int. Journal of Engineering Research and App-lication ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -5) March 2016, pp.48-52
Early Work Social networking sites are one that allow users to create their own personal information page containing information about self (real or virtual), to communicate with other members of the same website[4]. Communication is established in the the form of electronic mail, comments written on each others’ personal pages, blog or pictures, or chatting and messaging. Among the ten most popular social sites Facebook and MySpace ranks the best web sites in the world. Social Sites are extensively used in many countries and include Orkut (Brazil), Cyworld (Korea), and Mixi (Japan). The growth of SNSs is influenced by the younger generation, with Facebook originating as a college site [4] and MySpace in the beginning only has 21 members in early 2008 [48]. Nowadays the social sites are not limited to the younger generation but also an increasing number of aged people are actively participating. The key motivating factor for using Social sites is amiability, however, this implicate that some types of anti social people may never be interested in social sites[49]. Moreover, it seems that sociability is helpful in such social sites [42] and that female MySpace users have strong inclination to male users and vice versa[40], which recommend the basis for their effective communication. Fuzzy theory not only applicable to the real life problems solutions but also can be applied to all the social analysis. As argued by Brunelli and Fedrizzi that most social analyses tools represent adjacency relations in bidirectional(binary) form, and presented “A fuzzy approach to social network analysis” (Brunelli&Fedrizzi, 2009). They mathematically derived the fuzzy logic dimension to demonstrate the associations in the social network sites. They comprehend their fuzzy model to represent the ordered weighted averaging operators such as mean on m-ary fuzzy adjacency relations. Social Network Analysis combines the concept of the sociogram (a pictorial arrangement of associations in a social group) with basic fundamentals of graph theory to inspect patterns of correlation among people in different kinds of networks, permitting quantitative resemblance between different network structures.[12] There exist a large number of research literature explaining the use of Social Network Analysis. Most of this literature work upon the basic fundamentals of Social Network Analysis, that is the development of abstract models of network organization and the mathematical derivation of quantitative measures of network characteristics.[13] More recentlytheworkwillexamine the association of www.ijera.com
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these quantitative compute with organizational performance output. Cummings and Cross, for example, come out with the conclusion that that degree of hierarchy, core-periphery structure, and structural holes of leaders are associated negatively with performance in 182 work groups in a large telecom sector company,[14] and Aydin et al found that extended network communication density was related with maximal use of an electronic medical record system by nurse practitioners and physician’s assistants.[15] There have also been research showing how network parameters change with due course of time. Shah, for example demonstrated that network centrality decreased after decreasing in a consumer electronics firm,[16] whereas Burkhardt and Brass documented growth in network centrality after introduction of a new computer system in a federal agency.[17]
II.
RESEARCH METHODOLOGY
Social Netork is a social structure comprises of individualsorgroups and users also know as working or acting nodes , which are attached by one or more assignedgroups based on like and their strong relationships,such as friends, relatives ,office mates or college mates by their, knowledge or prestige [1]. The data for the study and research wereassimilated as part of a formal groups selection from social media sites to evaluate the opinion pool on some common issue inside or outside a group , we took data from Facebook , Twitter users by downloading their web logs using Informatica and other mining tools . Trained ANN Agents used to assimilate data for two week in each test case . They directly observed associations among the users .groups based on like,dislike,comments and their chatting patterns. The methods of ANN based algorithms are tested and than compared with Fuzzy sets assumptionsbasedon multi-agent system theory of ANN for social network analysis . Two wellknown and much used social networks Facebook and Twitter users are analysed for this study . A social network analysis user opinion group decision making again tested by the researcher using by advanced fuzzy reciprocal preference relation (AFRPR) . The main novelty of this method is that it can evaluate the importance degree on some common issue by combining like and comment scores . To do that, we used correlated fuzzy and ANN sets to represent a hybrid model for linking of relationship between users and other members on social media . In order to compute total sum of the individual preference for like or comments on some 49|P a g e
Vinay Viradia.et.al. Int. Journal of Engineering Research and App-lication ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -5) March 2016, pp.48-52 common issue, it is necessary to determine the scores associated to each user and group on social media . A general assumption used by trained agents of MATLAB is that the weights of users or groups is usually changed by other users like and dislikes . However, this assumption may not be wrong in real situation, and therefore it is required to check it again using Fuzzy sets. Trust and friendship is a key element for groups for like and comments , and as we propose a hybrid SNA methodology for the representation and testing the relationships between users in a social group so We tested it by Multi-Layer Perceptions method in MATLAB based on principles of ANN . The input scores of thesemulti agent networks is operatedby the next layers and the output value checked by the next layer . For similar types of network, the information is relayed forward from many layers, respectively. The data from the input layer is relayed to the hidden layer and output layer operated this data that is transferred to it in the activation function and processed the final score . The acquired output is measured with the desired output in both the cases . This process is repeated for 200 test cases .
III.
RESULTS
Results of proposed hybrid approach by computing overall frequency of user or groups on like or dislike recorded . For example, as the individual,user1like theuser2 comments and share it for next 20groups or users and the user2likes user1 in reply , the user5 and user6 never comments on the user2 opinion. As they never visited his profile ,that proved our test case , because the opinion score of the individuals are different from each other. Observed Like Centrality Sharing Centrality User 1 0.0924984 0.5934500 2 0.1096495 0.5937475 3 0.0587399 0.5428589 4 0.0408626 0.5428571 5 0.0285575 0.5135135 6 0.0485450 0.4418605 7 0.0871415 0.5135135 8 0.0579342 0.5277778 9 0.0428061 0.5757576 10 0.0489116 0.4871795 11 0.0631892 0.6333555 12 0.0421436 0.5277778 13 0.0419247 0.5428688 14 0.0645526 0.5937601 15 0.1034649 0.6333555 16 0.03970020.5588198 17 0.0834470 0.5937500 18 0.0385025 0.5588235
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0.05251900.5588230
Table 1 When we look at the like testing in the Table 1, we see that the user2 and user15 have high ranklike grade similar to others with the values 0.1096495 and 0.1034649, respectively. It iselucidated that user2 and user15, act as the leader intheir social network group . It is interpreted that the individuals groups or users , who has the higher like correlations , are the highly active users in their groups and they more influential also . When we look at the sharing testing ofusers the user11 and user15 have the highest closeness degree (0.6333555) and retrievingthe information by those user who are having higher degree than other users or groups . This demonstrated that the links . 11 and 15 are the most dominant loops. The loop, which the sharing centralization is lower (0.4418605), is the user6, and it interpret that this user6, is the most inactive link in this social network group. 4.1 TEST1 For Opinion differences (Using ANN) QAP correlation Value SignifAbg SD D(large) D(small) Nperm Pearson Correlation . 493 .000 .002 .059 .000 1.000 2485 4.2 TEST2 For Opinion differences (Using Fuzzy Sets) QAP correlation Value SignifAbg SD D(large) D(small) Nperm Pearson Correlation -.133 .000 -.002 . 047 .989 .000 2494
IV.
CONCLUSIONS
Fast and hybrid social network analysis techniques are needed to mine opinion scores on social networks as a grouping on wrong opinion may create problems for a society or country . Social Network Analysis (SNA) can be used as an important tool for researchers , as the number of users and groups increasing day by day on that social sites , and a large group may influence others, but the necessary information is often distributed and hidden on social site servers , so there is a need to design some new approaches for collection and analysis the social web data . The paper hypotheses are tested on Fuzzy set and ANN 50|P a g e
Vinay Viradia.et.al. Int. Journal of Engineering Research and App-lication ISSN : 2248-9622, Vol. 6, Issue 3, ( Part -5) March 2016, pp.48-52 based models to improve finding relation and to prove that there is meaningful and good correlation between the like and share of social media users on a common issue while chatting or commenting , even like and dislike ratio is very much similar and mean of final inputs we used for output layer. Everytestcase results is significant and there are higher degree of associations. This implies that the methods of Artificial Neural Networks, may be applied relatively, Correlation between the Artificial Neural Networksresults and Fuzzy set results may also be elucidated in form of Artificial Neural Networks . Association of both the methods improved the result upto4.5% , as user data, transferred to output layer in ANN with the connection weights will permit for interpreting the structure in the hidden layer of Artificial Neural Networks and also permit to make input for Fuzzy set layer . Furthermore to this, when the internal dynamics of Artificial Neural Networksand Fuzzy are analyzed, it maygenerate new methods named hybrid classification model using fuzzy and artificial neural network (HFANN) by us for this study . In the future studies, more efficient results will be derived as we will increase the amount of web log data taken from social network sites .For future work, we extendthis method can be applied to various other social networking websites.
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